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So,<00:00:00.400><c> Deepseek</c><00:00:00.960><c> version</c><00:00:01.280><c> 4</c><00:00:01.520><c> Pro</c><00:00:01.760><c> is</c><00:00:02.080><c> officially</c>

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So, Deepseek version 4 Pro is officially
 

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So, Deepseek version 4 Pro is officially
out<00:00:02.879><c> today.</c><00:00:03.600><c> Now,</c><00:00:03.840><c> you</c><00:00:04.000><c> might</c><00:00:04.160><c> be</c><00:00:04.319><c> confused</c>

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out today. Now, you might be confused
 

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out today. Now, you might be confused
because<00:00:05.040><c> this</c><00:00:05.359><c> model</c><00:00:05.680><c> was</c><00:00:05.839><c> technically</c><00:00:06.319><c> out,</c>

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because this model was technically out,
 

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because this model was technically out,
but<00:00:06.720><c> it</c><00:00:06.879><c> was</c><00:00:06.960><c> in</c><00:00:07.200><c> general</c><00:00:07.600><c> availability,</c><00:00:08.480><c> but</c>

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but it was in general availability, but
 

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but it was in general availability, but
this<00:00:08.960><c> is</c><00:00:09.040><c> the</c><00:00:09.280><c> official</c><00:00:09.679><c> release,</c><00:00:10.160><c> meaning</c>

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this is the official release, meaning
 

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this is the official release, meaning
that<00:00:10.559><c> they</c><00:00:10.800><c> have</c><00:00:10.960><c> trained</c><00:00:11.280><c> the</c><00:00:11.440><c> model</c><00:00:12.000><c> a</c>

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that they have trained the model a
 

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that they have trained the model a
little<00:00:12.400><c> bit</c><00:00:12.559><c> more</c><00:00:12.719><c> and</c><00:00:13.120><c> produced</c><00:00:13.519><c> a</c><00:00:13.759><c> stronger</c>

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little bit more and produced a stronger
 

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little bit more and produced a stronger
version<00:00:14.480><c> of</c><00:00:14.639><c> the</c><00:00:14.799><c> model</c><00:00:15.040><c> that</c><00:00:15.360><c> is</c><00:00:15.599><c> available</c>

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version of the model that is available
 

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version of the model that is available
today.<00:00:16.480><c> Now,</c><00:00:16.640><c> if</c><00:00:16.800><c> I</c><00:00:16.960><c> were</c><00:00:17.039><c> to</c><00:00:17.199><c> summarize</c>

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today. Now, if I were to summarize
 

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today. Now, if I were to summarize
today's<00:00:17.920><c> release</c><00:00:18.160><c> in</c><00:00:18.480><c> one</c><00:00:18.640><c> simple</c><00:00:18.960><c> sentence,</c>

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today's release in one simple sentence,
 

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today's release in one simple sentence,
it<00:00:19.680><c> is</c><00:00:19.840><c> that</c><00:00:20.000><c> the</c><00:00:20.240><c> price</c><00:00:20.480><c> to</c><00:00:20.720><c> performance</c><00:00:21.199><c> is</c>

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it is that the price to performance is
 

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it is that the price to performance is
becoming<00:00:21.840><c> a</c><00:00:22.240><c> very,</c><00:00:22.560><c> very</c><00:00:22.880><c> important</c><00:00:23.359><c> thing</c>

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becoming a very, very important thing
 

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becoming a very, very important thing
because<00:00:24.000><c> not</c><00:00:24.240><c> only</c><00:00:24.400><c> do</c><00:00:24.560><c> we</c><00:00:24.720><c> have</c><00:00:24.800><c> a</c><00:00:24.960><c> new</c><00:00:25.119><c> model</c>

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because not only do we have a new model
 

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because not only do we have a new model
from<00:00:25.600><c> the</c><00:00:25.760><c> Deepseek</c><00:00:26.320><c> team,</c><00:00:26.720><c> the</c><00:00:26.880><c> SpaceX</c><00:00:27.439><c> team</c>

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from the Deepseek team, the SpaceX team
 

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from the Deepseek team, the SpaceX team
also<00:00:28.000><c> dropped</c><00:00:28.320><c> Grock</c><00:00:28.720><c> 4.6</c><00:00:29.279><c> six.</c><00:00:29.679><c> And</c><00:00:29.920><c> both</c><00:00:30.160><c> of</c>

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also dropped Grock 4.6 six. And both of
 

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also dropped Grock 4.6 six. And both of
these<00:00:30.480><c> models</c><00:00:30.800><c> are</c><00:00:31.039><c> competing</c><00:00:31.439><c> with</c><00:00:31.599><c> the</c><00:00:31.760><c> best</c>

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these models are competing with the best
 

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these models are competing with the best
Frontier<00:00:32.480><c> Labs</c><00:00:32.960><c> at</c><00:00:33.200><c> a</c><00:00:33.440><c> fraction</c><00:00:33.680><c> of</c><00:00:33.840><c> their</c>

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Frontier Labs at a fraction of their
 

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Frontier Labs at a fraction of their
cost.<00:00:34.880><c> Let's</c><00:00:35.120><c> start</c><00:00:35.200><c> with</c><00:00:35.360><c> Gro</c><00:00:35.840><c> 4.6.</c><00:00:36.640><c> And</c><00:00:36.880><c> one</c>

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cost. Let's start with Gro 4.6. And one
 

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cost. Let's start with Gro 4.6. And one
thing<00:00:37.120><c> I'm</c><00:00:37.280><c> going</c><00:00:37.360><c> to</c><00:00:37.520><c> say</c><00:00:37.760><c> is</c><00:00:38.000><c> that</c><00:00:38.559><c> I'm</c>

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thing I'm going to say is that I'm
 

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thing I'm going to say is that I'm
genuinely<00:00:39.280><c> surprised</c><00:00:39.600><c> by</c><00:00:39.760><c> the</c><00:00:40.000><c> SpaceX</c><00:00:40.480><c> team.</c>

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genuinely surprised by the SpaceX team.
 

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genuinely surprised by the SpaceX team.
I'm<00:00:41.120><c> not</c><00:00:41.280><c> trying</c><00:00:41.360><c> to</c><00:00:41.520><c> glaze</c><00:00:41.840><c> them</c><00:00:42.000><c> or</c><00:00:42.160><c> Elon</c>

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I'm not trying to glaze them or Elon
 

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I'm not trying to glaze them or Elon
Musk<00:00:42.719><c> or</c><00:00:42.879><c> anything.</c><00:00:43.280><c> I</c><00:00:43.520><c> was</c><00:00:43.600><c> just</c><00:00:43.760><c> very</c>

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Musk or anything. I was just very
 

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Musk or anything. I was just very
critical<00:00:44.399><c> of</c><00:00:44.559><c> this</c><00:00:44.800><c> lab.</c><00:00:45.200><c> In</c><00:00:45.520><c> 2025,</c><00:00:46.239><c> they</c>

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critical of this lab. In 2025, they
 

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critical of this lab. In 2025, they
dropped<00:00:46.800><c> Grog</c><00:00:47.120><c> 4</c><00:00:47.360><c> and</c><00:00:47.600><c> other</c><00:00:47.840><c> models</c><00:00:48.160><c> like</c>

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dropped Grog 4 and other models like
 

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dropped Grog 4 and other models like
that,<00:00:48.559><c> but</c><00:00:48.800><c> I</c><00:00:48.960><c> wasn't</c><00:00:49.200><c> really,</c><00:00:49.920><c> you</c><00:00:50.079><c> know,</c>

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that, but I wasn't really, you know,
 

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that, but I wasn't really, you know,
mind<00:00:50.640><c> blown</c><00:00:50.960><c> with</c><00:00:51.200><c> their</c><00:00:51.680><c> performance</c>

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mind blown with their performance
 

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mind blown with their performance
because<00:00:52.480><c> they're</c><00:00:52.800><c> pretty</c><00:00:53.280><c> they're</c><00:00:53.600><c> pretty</c>

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because they're pretty they're pretty
 

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because they're pretty they're pretty
subpar<00:00:54.320><c> compared</c><00:00:54.719><c> to</c><00:00:54.879><c> any</c><00:00:55.120><c> of</c><00:00:55.280><c> the</c><00:00:55.440><c> other</c><00:00:55.600><c> labs</c>

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subpar compared to any of the other labs
 

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subpar compared to any of the other labs
out<00:00:56.160><c> there.</c><00:00:56.640><c> But</c><00:00:56.800><c> in</c><00:00:57.120><c> 2026,</c><00:00:57.920><c> it</c><00:00:58.160><c> looks</c><00:00:58.320><c> like</c>

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out there. But in 2026, it looks like
 

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out there. But in 2026, it looks like
things<00:00:58.800><c> have</c><00:00:59.039><c> kind</c><00:00:59.280><c> of</c><00:00:59.359><c> changed</c><00:00:59.680><c> a</c><00:00:59.920><c> little</c>

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things have kind of changed a little
 

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things have kind of changed a little
because<00:01:00.320><c> Grock</c><00:01:00.719><c> 4.5</c><00:01:01.359><c> was</c><00:01:01.600><c> quite</c><00:01:01.840><c> competitive</c>

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because Grock 4.5 was quite competitive
 

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because Grock 4.5 was quite competitive
based<00:01:02.640><c> off</c><00:01:02.800><c> what</c><00:01:02.960><c> it</c><00:01:03.120><c> cost</c><00:01:03.600><c> and</c><00:01:03.840><c> Grock</c><00:01:04.320><c> 4.6</c><00:01:05.199><c> is</c>

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based off what it cost and Grock 4.6 is
 

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based off what it cost and Grock 4.6 is
actually<00:01:06.400><c> not</c><00:01:06.640><c> too</c><00:01:06.880><c> bad.</c><00:01:07.200><c> If</c><00:01:07.360><c> we</c><00:01:07.520><c> take</c><00:01:07.680><c> a</c><00:01:07.840><c> look</c>

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actually not too bad. If we take a look
 

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actually not too bad. If we take a look
at<00:01:08.080><c> the</c><00:01:08.320><c> benchmarks,</c><00:01:09.200><c> for</c><00:01:09.280><c> example,</c><00:01:09.680><c> if</c><00:01:09.840><c> we</c>

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at the benchmarks, for example, if we
 

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at the benchmarks, for example, if we
start<00:01:10.159><c> with</c><00:01:10.320><c> the</c><00:01:10.560><c> artificial</c><00:01:11.200><c> analysis</c>

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start with the artificial analysis
 

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start with the artificial analysis
intelligence<00:01:12.400><c> index,</c><00:01:13.200><c> this</c><00:01:13.439><c> model</c><00:01:13.760><c> achieves</c>

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intelligence index, this model achieves
 

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intelligence index, this model achieves
a<00:01:14.640><c> 61</c><00:01:15.200><c> and</c><00:01:15.439><c> Fable</c><00:01:16.000><c> 5</c><00:01:16.240><c> is</c><00:01:16.400><c> at</c><00:01:16.720><c> 62.</c><00:01:17.600><c> Now,</c><00:01:17.840><c> what's</c>

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a 61 and Fable 5 is at 62. Now, what's
 

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a 61 and Fable 5 is at 62. Now, what's
really<00:01:18.400><c> important</c><00:01:18.640><c> to</c><00:01:18.880><c> remember</c><00:01:19.119><c> is</c><00:01:19.360><c> that</c>

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really important to remember is that
 

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really important to remember is that
once<00:01:19.920><c> again,</c><00:01:20.560><c> this</c><00:01:20.799><c> model</c><00:01:21.200><c> is</c><00:01:21.439><c> quite</c><00:01:21.759><c> cheap</c>

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once again, this model is quite cheap
 

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once again, this model is quite cheap
compared<00:01:22.479><c> to</c><00:01:22.640><c> Fable</c><00:01:23.119><c> 5.</c><00:01:23.360><c> This</c><00:01:23.600><c> is</c><00:01:23.759><c> about</c><00:01:24.000><c> $2</c>

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compared to Fable 5. This is about $2
 

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compared to Fable 5. This is about $2
per<00:01:24.560><c> million</c><00:01:24.880><c> input</c><00:01:25.200><c> tokens</c><00:01:25.840><c> and</c><00:01:26.240><c> $6</c><00:01:26.720><c> per</c>

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per million input tokens and $6 per
 

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per million input tokens and $6 per
million<00:01:27.200><c> output</c><00:01:27.600><c> tokens.</c><00:01:28.240><c> In</c><00:01:28.400><c> Fable</c><00:01:28.880><c> 5</c><00:01:29.200><c> sits</c>

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million output tokens. In Fable 5 sits
 

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million output tokens. In Fable 5 sits
at<00:01:29.759><c> $10</c><00:01:30.159><c> per</c><00:01:30.400><c> million</c><00:01:30.640><c> input</c><00:01:30.960><c> tokens</c><00:01:31.360><c> and</c><00:01:31.759><c> $50</c>

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at $10 per million input tokens and $50
 

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at $10 per million input tokens and $50
per<00:01:32.479><c> million</c><00:01:32.799><c> output</c><00:01:33.119><c> tokens.</c><00:01:33.840><c> And</c><00:01:34.079><c> this</c><00:01:34.240><c> is</c>

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per million output tokens. And this is
 

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per million output tokens. And this is
why<00:01:34.560><c> you</c><00:01:34.799><c> start</c><00:01:34.960><c> to</c><00:01:35.200><c> appreciate</c><00:01:35.759><c> this</c><00:01:36.079><c> release</c>

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why you start to appreciate this release
 

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why you start to appreciate this release
a<00:01:36.560><c> little</c><00:01:36.720><c> bit</c><00:01:36.880><c> more.</c><00:01:37.280><c> It</c><00:01:37.439><c> might</c><00:01:37.600><c> not</c><00:01:37.840><c> beat</c><00:01:38.079><c> the</c>

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a little bit more. It might not beat the
 

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a little bit more. It might not beat the
performance<00:01:38.640><c> of</c><00:01:38.880><c> the</c><00:01:39.040><c> best</c><00:01:39.280><c> models</c><00:01:39.840><c> is</c>

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performance of the best models is
 

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performance of the best models is
matching<00:01:40.400><c> them.</c><00:01:40.880><c> Even</c><00:01:41.119><c> GBT</c><00:01:41.759><c> 5.6</c><00:01:42.400><c> so</c><00:01:42.640><c> which</c><00:01:42.799><c> is</c>

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matching them. Even GBT 5.6 so which is
 

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matching them. Even GBT 5.6 so which is
a<00:01:43.280><c> pretty</c><00:01:43.600><c> capable</c><00:01:44.000><c> model</c><00:01:44.320><c> and</c><00:01:44.479><c> this</c><00:01:44.640><c> is</c><00:01:44.799><c> set</c>

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a pretty capable model and this is set
 

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a pretty capable model and this is set
at<00:01:45.200><c> max</c><00:01:45.840><c> on</c><00:01:46.000><c> the</c><00:01:46.159><c> artificial</c><00:01:46.720><c> analysis</c><00:01:47.280><c> index.</c>

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at max on the artificial analysis index.
 

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at max on the artificial analysis index.
This<00:01:48.240><c> model</c><00:01:48.479><c> achieves</c><00:01:48.960><c> 61</c><00:01:49.439><c> and</c><00:01:49.680><c> Grock</c><00:01:50.079><c> 4.6</c><00:01:51.040><c> 61.</c>

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This model achieves 61 and Grock 4.6 61.
 

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This model achieves 61 and Grock 4.6 61.
So<00:01:51.759><c> it</c><00:01:51.920><c> ties</c><00:01:52.159><c> it</c><00:01:52.320><c> and</c><00:01:52.560><c> it's</c><00:01:52.799><c> much</c><00:01:53.040><c> cheaper.</c><00:01:53.680><c> And</c>

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So it ties it and it's much cheaper. And
 

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So it ties it and it's much cheaper. And
then<00:01:54.000><c> even</c><00:01:54.240><c> on</c><00:01:54.479><c> all</c><00:01:54.640><c> of</c><00:01:54.720><c> these</c><00:01:54.960><c> other</c>

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then even on all of these other
 

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then even on all of these other
benchmarks,<00:01:56.079><c> for</c><00:01:56.159><c> example,</c><00:01:56.479><c> the</c><00:01:56.640><c> GDP</c><00:01:57.119><c> Val</c>

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benchmarks, for example, the GDP Val
 

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benchmarks, for example, the GDP Val
one,<00:01:57.759><c> it</c><00:01:58.000><c> actually</c><00:01:58.320><c> beats</c><00:01:58.560><c> Fable</c><00:01:59.040><c> 5,</c><00:01:59.200><c> which</c><00:01:59.439><c> is</c>

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one, it actually beats Fable 5, which is
 

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one, it actually beats Fable 5, which is
at<00:01:59.920><c> 1741.</c><00:02:01.040><c> And</c><00:02:01.200><c> then</c><00:02:01.439><c> GPT</c><00:02:02.000><c> 5.6,</c><00:02:02.560><c> it's</c><00:02:02.799><c> at</c><00:02:03.040><c> 1728.</c>

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at 1741. And then GPT 5.6, it's at 1728.
 

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at 1741. And then GPT 5.6, it's at 1728.
I'm<00:02:04.399><c> not</c><00:02:04.560><c> sure</c><00:02:04.719><c> why</c><00:02:04.960><c> they</c><00:02:05.119><c> didn't</c><00:02:05.280><c> choose</c><00:02:05.600><c> Opus</c>

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I'm not sure why they didn't choose Opus
 

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I'm not sure why they didn't choose Opus
5<00:02:06.240><c> as</c><00:02:06.399><c> well,</c><00:02:06.560><c> but</c><00:02:06.719><c> I</c><00:02:06.880><c> guess</c><00:02:06.960><c> they</c><00:02:07.200><c> wanted</c><00:02:07.360><c> to</c>

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5 as well, but I guess they wanted to
 

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5 as well, but I guess they wanted to
choose<00:02:07.759><c> the</c><00:02:08.319><c> quote</c><00:02:08.560><c> unquote</c><00:02:09.039><c> strongest</c><00:02:09.520><c> model</c>

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choose the quote unquote strongest model
 

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choose the quote unquote strongest model
lineup<00:02:10.160><c> from</c><00:02:10.399><c> each</c><00:02:10.560><c> lab.</c><00:02:10.800><c> And</c><00:02:10.959><c> they</c><00:02:11.120><c> chose</c>

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lineup from each lab. And they chose
 

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lineup from each lab. And they chose
Fable<00:02:11.680><c> 5</c><00:02:11.840><c> for</c><00:02:12.000><c> Enthropic,</c><00:02:12.800><c> which</c><00:02:13.120><c> is</c><00:02:13.280><c> fair.</c>

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Fable 5 for Enthropic, which is fair.
 

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Fable 5 for Enthropic, which is fair.
And<00:02:13.840><c> then</c><00:02:14.080><c> Deep</c><00:02:14.400><c> Software</c><00:02:14.800><c> Engineering</c><00:02:15.200><c> one,</c>

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And then Deep Software Engineering one,
 

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And then Deep Software Engineering one,
which<00:02:15.599><c> is</c><00:02:15.760><c> a</c><00:02:15.920><c> critical</c><00:02:16.319><c> benchmark.</c><00:02:17.280><c> This</c>

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which is a critical benchmark. This
 

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which is a critical benchmark. This
model<00:02:17.920><c> doesn't</c><00:02:18.319><c> beat</c><00:02:18.879><c> Fable</c><00:02:19.360><c> 5</c><00:02:19.520><c> or</c><00:02:19.760><c> GPT</c><00:02:20.239><c> 5.6</c>

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model doesn't beat Fable 5 or GPT 5.6
 

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model doesn't beat Fable 5 or GPT 5.6
six<00:02:20.879><c> soul,</c><00:02:21.200><c> but</c><00:02:21.360><c> it</c><00:02:21.520><c> gets</c><00:02:21.680><c> close</c><00:02:21.840><c> to</c><00:02:22.000><c> it.</c><00:02:22.239><c> It's</c>

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six soul, but it gets close to it. It's
 

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six soul, but it gets close to it. It's
65.9<00:02:23.520><c> and</c><00:02:23.840><c> Fable</c><00:02:24.239><c> 5</c><00:02:24.400><c> sits</c><00:02:24.720><c> at</c><00:02:24.959><c> 70%.</c><00:02:25.760><c> But</c><00:02:25.920><c> if</c>

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65.9 and Fable 5 sits at 70%. But if
 

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65.9 and Fable 5 sits at 70%. But if
you're<00:02:26.239><c> getting</c><00:02:26.400><c> results</c><00:02:26.800><c> that</c><00:02:27.040><c> are</c><00:02:27.200><c> pretty</c>

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you're getting results that are pretty
 

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you're getting results that are pretty
close<00:02:27.840><c> and</c><00:02:28.080><c> the</c><00:02:28.319><c> model</c><00:02:28.480><c> is</c><00:02:28.720><c> five</c><00:02:28.879><c> times</c>

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close and the model is five times
 

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close and the model is five times
cheaper,<00:02:29.599><c> I</c><00:02:29.840><c> wouldn't</c><00:02:30.000><c> be</c><00:02:30.160><c> too</c><00:02:30.400><c> disappointed</c>

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cheaper, I wouldn't be too disappointed
 

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cheaper, I wouldn't be too disappointed
with<00:02:31.120><c> this</c><00:02:31.360><c> result.</c><00:02:31.920><c> And</c><00:02:32.080><c> then</c><00:02:32.239><c> same</c><00:02:32.400><c> thing</c>

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with this result. And then same thing
 

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with this result. And then same thing
with<00:02:32.800><c> Cursor</c><00:02:33.200><c> Bench</c><00:02:33.599><c> 3.2,</c><00:02:34.400><c> the</c><00:02:34.640><c> model</c>

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with Cursor Bench 3.2, the model
 

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with Cursor Bench 3.2, the model
achieves<00:02:35.360><c> 69.9%,</c>

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achieves 69.9%,
 

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achieves 69.9%,
funny<00:02:37.280><c> number.</c><00:02:37.840><c> And</c><00:02:38.000><c> Fable</c><00:02:38.400><c> 5</c><00:02:38.560><c> sits</c><00:02:38.879><c> at</c><00:02:39.040><c> 70.5.</c>

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funny number. And Fable 5 sits at 70.5.
 

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funny number. And Fable 5 sits at 70.5.
So<00:02:40.319><c> once</c><00:02:40.560><c> again,</c><00:02:40.800><c> closer.</c><00:02:41.200><c> And</c><00:02:41.360><c> Grock</c><00:02:41.760><c> 4.6</c>

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So once again, closer. And Grock 4.6
 

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So once again, closer. And Grock 4.6
beats<00:02:42.720><c> GPT</c><00:02:43.280><c> 5.6</c><00:02:43.920><c> Soul.</c><00:02:44.640><c> Same</c><00:02:44.800><c> thing</c><00:02:44.879><c> with</c><00:02:45.040><c> the</c>

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beats GPT 5.6 Soul. Same thing with the
 

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beats GPT 5.6 Soul. Same thing with the
Frontier<00:02:45.680><c> Code.</c><00:02:46.239><c> It</c><00:02:46.400><c> gets</c><00:02:46.640><c> close</c><00:02:46.879><c> to</c><00:02:47.120><c> Fable</c><00:02:47.599><c> 5,</c>

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Frontier Code. It gets close to Fable 5,
 

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Frontier Code. It gets close to Fable 5,
beats<00:02:48.319><c> GPT</c><00:02:48.879><c> 5.6</c><00:02:49.519><c> Soul.</c><00:02:50.000><c> So</c><00:02:50.239><c> yeah,</c><00:02:50.560><c> this</c><00:02:50.720><c> model</c>

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beats GPT 5.6 Soul. So yeah, this model
 

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beats GPT 5.6 Soul. So yeah, this model
is<00:02:51.280><c> actually</c><00:02:51.519><c> available</c><00:02:52.000><c> in</c><00:02:52.239><c> cursor.</c><00:02:52.800><c> So</c><00:02:53.040><c> the</c>

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is actually available in cursor. So the
 

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is actually available in cursor. So the
partnership<00:02:53.760><c> with</c><00:02:54.000><c> cursor</c><00:02:54.480><c> or</c><00:02:54.720><c> I</c><00:02:54.879><c> guess</c><00:02:54.959><c> the</c>

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partnership with cursor or I guess the
 

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partnership with cursor or I guess the
acquisition<00:02:56.000><c> has</c><00:02:56.239><c> really</c><00:02:56.480><c> helped</c><00:02:56.800><c> SpaceX</c>

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acquisition has really helped SpaceX
 

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acquisition has really helped SpaceX
make<00:02:57.599><c> some</c><00:02:57.840><c> strides</c><00:02:58.160><c> in</c><00:02:58.319><c> the</c><00:02:58.400><c> AI</c><00:02:58.800><c> space</c><00:02:59.120><c> this</c>

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make some strides in the AI space this
 

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make some strides in the AI space this
year<00:02:59.760><c> and</c><00:03:00.080><c> Grock</c><00:03:00.480><c> build</c><00:03:00.720><c> is</c><00:03:00.959><c> something</c><00:03:01.120><c> that</c>

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year and Grock build is something that
 

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year and Grock build is something that
you<00:03:02.000><c> know</c><00:03:02.239><c> maybe</c><00:03:02.560><c> not</c><00:03:02.720><c> a</c><00:03:02.879><c> lot</c><00:03:03.040><c> of</c><00:03:03.120><c> us</c><00:03:03.280><c> have</c><00:03:03.519><c> been</c>

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you know maybe not a lot of us have been
 

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you know maybe not a lot of us have been
using<00:03:04.000><c> so</c><00:03:04.239><c> far</c><00:03:04.400><c> but</c><00:03:04.640><c> it's</c><00:03:04.959><c> probably</c><00:03:05.200><c> going</c><00:03:05.360><c> to</c>

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using so far but it's probably going to
 

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using so far but it's probably going to
be<00:03:06.080><c> another</c><00:03:06.560><c> platform</c><00:03:07.040><c> like</c><00:03:07.280><c> codeex</c><00:03:07.760><c> or</c><00:03:07.920><c> cloud</c>

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be another platform like codeex or cloud
 

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be another platform like codeex or cloud
code<00:03:08.480><c> that</c><00:03:08.720><c> we</c><00:03:08.879><c> start</c><00:03:09.120><c> to</c><00:03:09.360><c> use</c><00:03:09.680><c> but</c><00:03:09.920><c> obviously</c>

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code that we start to use but obviously
 

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code that we start to use but obviously
cursor<00:03:10.720><c> is</c><00:03:10.959><c> quite</c><00:03:11.200><c> strong</c><00:03:11.440><c> as</c><00:03:11.680><c> well.</c><00:03:11.920><c> So</c><00:03:12.080><c> you</c>

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cursor is quite strong as well. So you
 

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cursor is quite strong as well. So you
have<00:03:12.480><c> options</c><00:03:13.040><c> available</c><00:03:13.519><c> for</c><00:03:13.760><c> you</c><00:03:13.920><c> to</c><00:03:14.159><c> use</c>

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have options available for you to use
 

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have options available for you to use
them<00:03:14.480><c> in</c><00:03:14.720><c> both.</c><00:03:15.280><c> And</c><00:03:15.440><c> one</c><00:03:15.680><c> thing</c><00:03:15.760><c> to</c><00:03:15.920><c> note</c><00:03:16.159><c> is</c>

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them in both. And one thing to note is
 

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them in both. And one thing to note is
that<00:03:16.560><c> they're</c><00:03:16.800><c> offering</c><00:03:17.280><c> two</c><00:03:17.519><c> times</c><00:03:18.080><c> usage</c>

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that they're offering two times usage
 

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that they're offering two times usage
inside<00:03:19.040><c> Grock</c><00:03:19.440><c> build</c><00:03:19.680><c> and</c><00:03:19.840><c> cursor</c><00:03:20.239><c> for</c><00:03:20.400><c> the</c>

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inside Grock build and cursor for the
 

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inside Grock build and cursor for the
first<00:03:20.800><c> week.</c><00:03:21.200><c> So</c><00:03:21.360><c> if</c><00:03:21.519><c> you</c><00:03:21.680><c> just</c><00:03:21.840><c> want</c><00:03:21.920><c> to</c><00:03:22.080><c> try</c>

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first week. So if you just want to try
 

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first week. So if you just want to try
it<00:03:22.400><c> out,</c><00:03:22.720><c> see</c><00:03:22.879><c> what</c><00:03:23.040><c> you</c><00:03:23.200><c> feel</c><00:03:23.440><c> about</c><00:03:23.599><c> it,</c><00:03:24.239><c> then</c>

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it out, see what you feel about it, then
 

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it out, see what you feel about it, then
you<00:03:24.800><c> know</c><00:03:25.040><c> it</c><00:03:25.280><c> might</c><00:03:25.440><c> be</c><00:03:25.599><c> worth</c><00:03:25.840><c> trying</c><00:03:26.080><c> it</c><00:03:26.159><c> out</c>

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you know it might be worth trying it out
 

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you know it might be worth trying it out
right<00:03:26.560><c> now</c><00:03:26.720><c> cuz</c><00:03:26.959><c> you</c><00:03:27.120><c> get</c><00:03:27.200><c> double</c><00:03:27.519><c> the</c><00:03:27.680><c> usage</c>

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right now cuz you get double the usage
 

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right now cuz you get double the usage
in<00:03:28.159><c> the</c><00:03:28.319><c> first</c><00:03:28.480><c> week.</c><00:03:29.200><c> Now</c><00:03:29.440><c> if</c><00:03:29.599><c> we</c><00:03:29.680><c> were</c><00:03:29.840><c> to</c>

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in the first week. Now if we were to
 

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in the first week. Now if we were to
take<00:03:30.080><c> a</c><00:03:30.239><c> look</c><00:03:30.319><c> at</c><00:03:30.480><c> some</c><00:03:30.640><c> of</c><00:03:30.720><c> the</c><00:03:30.879><c> outputs</c><00:03:31.280><c> that</c>

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take a look at some of the outputs that
 

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take a look at some of the outputs that
people<00:03:31.760><c> have</c><00:03:32.000><c> been</c><00:03:32.159><c> generating</c><00:03:32.560><c> with</c><00:03:32.879><c> Grock</c>

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people have been generating with Grock
 

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people have been generating with Grock
4.6,<00:03:34.239><c> what</c><00:03:34.480><c> we're</c><00:03:34.640><c> looking</c><00:03:34.799><c> at</c><00:03:34.959><c> right</c><00:03:35.120><c> now</c><00:03:35.280><c> is</c>

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4.6, what we're looking at right now is
 

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4.6, what we're looking at right now is
a<00:03:35.920><c> Falcon</c><00:03:36.400><c> 9</c><00:03:36.640><c> booster</c><00:03:37.200><c> return</c><00:03:37.680><c> sequence</c>

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a Falcon 9 booster return sequence
 

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a Falcon 9 booster return sequence
simulation.<00:03:38.799><c> And</c><00:03:39.040><c> this</c><00:03:39.280><c> was</c><00:03:39.440><c> done</c><00:03:39.599><c> in</c><00:03:39.840><c> a</c>

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simulation. And this was done in a
 

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simulation. And this was done in a
single<00:03:40.319><c> HTML</c><00:03:41.040><c> file.</c><00:03:41.920><c> And</c><00:03:42.239><c> as</c><00:03:42.480><c> I</c><00:03:42.640><c> said,</c><00:03:42.959><c> if</c><00:03:43.200><c> you</c>

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single HTML file. And as I said, if you
 

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single HTML file. And as I said, if you
expected<00:03:43.840><c> to</c><00:03:44.080><c> get</c><00:03:44.239><c> this</c><00:03:44.480><c> type</c><00:03:44.640><c> of</c><00:03:44.799><c> output</c><00:03:45.120><c> from</c>

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expected to get this type of output from
 

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expected to get this type of output from
Grock<00:03:45.840><c> in</c><00:03:46.159><c> 2025,</c><00:03:47.360><c> you</c><00:03:47.599><c> would</c><00:03:47.760><c> be</c><00:03:47.920><c> kind</c><00:03:48.159><c> of</c>

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Grock in 2025, you would be kind of
 

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Grock in 2025, you would be kind of
surprised<00:03:49.040><c> because</c><00:03:49.280><c> you</c><00:03:49.519><c> wouldn't</c><00:03:49.760><c> expect</c>

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surprised because you wouldn't expect
 

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surprised because you wouldn't expect
something<00:03:50.319><c> like</c><00:03:50.480><c> this</c><00:03:50.720><c> to</c><00:03:50.879><c> be</c><00:03:51.040><c> generated</c><00:03:51.519><c> with</c>

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something like this to be generated with
 

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something like this to be generated with
Grock.<00:03:52.159><c> But</c><00:03:52.319><c> now</c><00:03:52.400><c> it</c><00:03:52.640><c> looks</c><00:03:52.799><c> like</c><00:03:52.879><c> we</c><00:03:53.120><c> have</c><00:03:53.200><c> to</c>

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Grock. But now it looks like we have to
 

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Grock. But now it looks like we have to
start<00:03:53.599><c> taking</c><00:03:53.920><c> the</c><00:03:54.080><c> Grock</c><00:03:54.480><c> team</c><00:03:54.640><c> a</c><00:03:54.879><c> little</c><00:03:54.959><c> bit</c>

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start taking the Grock team a little bit
 

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start taking the Grock team a little bit
more<00:03:55.280><c> serious</c><00:03:55.760><c> because</c><00:03:56.560><c> this</c><00:03:56.799><c> output</c><00:03:57.200><c> is</c>

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more serious because this output is
 

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more serious because this output is
quite<00:03:57.680><c> competitive.</c><00:03:58.560><c> And</c><00:03:58.720><c> obviously,</c><00:03:59.120><c> we're</c>

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quite competitive. And obviously, we're
 

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quite competitive. And obviously, we're
just<00:03:59.519><c> looking</c><00:03:59.760><c> at</c><00:03:59.920><c> a</c><00:04:00.159><c> simulation</c><00:04:00.640><c> and</c><00:04:00.879><c> we're</c>

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just looking at a simulation and we're
 

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just looking at a simulation and we're
just<00:04:01.280><c> basing</c><00:04:01.599><c> it</c><00:04:01.840><c> off</c><00:04:02.000><c> of</c><00:04:02.159><c> a</c><00:04:02.480><c> visual</c>

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just basing it off of a visual
 

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just basing it off of a visual
representation.<00:04:03.920><c> But</c><00:04:04.080><c> if</c><00:04:04.319><c> the</c><00:04:04.400><c> model</c><00:04:04.640><c> is</c><00:04:04.799><c> able</c>

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representation. But if the model is able
 

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representation. But if the model is able
to<00:04:05.120><c> produce</c><00:04:05.439><c> something</c><00:04:05.680><c> like</c><00:04:05.840><c> this</c>

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to produce something like this
 

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to produce something like this
consistently,<00:04:07.040><c> then</c><00:04:07.360><c> I</c><00:04:07.599><c> would</c><00:04:07.760><c> expect</c><00:04:08.080><c> a</c><00:04:08.319><c> lot</c>

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consistently, then I would expect a lot
 

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consistently, then I would expect a lot
of<00:04:08.560><c> people</c><00:04:08.720><c> to</c><00:04:09.040><c> start</c><00:04:09.280><c> adopting</c><00:04:09.760><c> Grock</c>

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of people to start adopting Grock
 

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of people to start adopting Grock
because<00:04:10.480><c> number</c><00:04:10.640><c> one,</c><00:04:11.040><c> it</c><00:04:11.280><c> is</c><00:04:11.439><c> cheaper</c><00:04:11.680><c> than</c>

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because number one, it is cheaper than
 

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because number one, it is cheaper than
the<00:04:12.080><c> other</c><00:04:12.239><c> labs</c><00:04:12.720><c> at</c><00:04:12.720><c> least</c><00:04:12.959><c> at</c><00:04:13.200><c> the</c><00:04:13.360><c> moment</c>

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the other labs at least at the moment
 

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the other labs at least at the moment
because<00:04:13.920><c> we</c><00:04:14.159><c> don't</c><00:04:14.239><c> know</c><00:04:14.319><c> if</c><00:04:14.480><c> this</c><00:04:14.720><c> pricing</c>

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because we don't know if this pricing
 

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because we don't know if this pricing
strategy<00:04:15.599><c> is</c><00:04:15.840><c> going</c><00:04:15.920><c> to</c><00:04:16.079><c> be</c><00:04:16.320><c> sustainable</c><00:04:16.799><c> for</c>

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strategy is going to be sustainable for
 

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strategy is going to be sustainable for
the<00:04:17.199><c> SpaceX</c><00:04:17.680><c> team</c><00:04:17.919><c> in</c><00:04:18.079><c> the</c><00:04:18.160><c> long</c><00:04:18.320><c> run.</c><00:04:18.959><c> But</c><00:04:19.040><c> at</c>

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the SpaceX team in the long run. But at
 

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the SpaceX team in the long run. But at
least<00:04:19.359><c> for</c><00:04:19.519><c> now,</c><00:04:19.759><c> their</c><00:04:20.000><c> models</c><00:04:20.320><c> are</c>

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least for now, their models are
 

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least for now, their models are
definitely<00:04:20.959><c> cheaper</c><00:04:21.440><c> compared</c><00:04:21.759><c> to</c><00:04:21.919><c> the</c>

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definitely cheaper compared to the
 

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definitely cheaper compared to the
others.<00:04:22.720><c> And</c><00:04:22.880><c> as</c><00:04:23.120><c> I</c><00:04:23.280><c> mentioned,</c><00:04:23.600><c> this</c><00:04:23.840><c> model</c>

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others. And as I mentioned, this model
 

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others. And as I mentioned, this model
excelled<00:04:24.639><c> at</c><00:04:24.800><c> the</c><00:04:25.040><c> artificial</c><00:04:25.520><c> analysis</c>

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excelled at the artificial analysis
 

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excelled at the artificial analysis
index.<00:04:26.800><c> This</c><00:04:27.040><c> model</c><00:04:27.360><c> jumped</c><00:04:27.759><c> to</c><00:04:28.240><c> probably</c>

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index. This model jumped to probably
 

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index. This model jumped to probably
number<00:04:29.199><c> four</c><00:04:29.520><c> model.</c><00:04:30.240><c> It's</c><00:04:30.479><c> tied</c><00:04:30.880><c> to</c><00:04:31.120><c> GPT</c><00:04:31.759><c> 5.6</c>

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number four model. It's tied to GPT 5.6
 

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number four model. It's tied to GPT 5.6
pretty<00:04:32.960><c> much</c><00:04:33.120><c> similar.</c><00:04:33.440><c> So</c><00:04:33.520><c> you</c><00:04:33.680><c> could</c><00:04:33.840><c> say</c>

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pretty much similar. So you could say
 

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pretty much similar. So you could say
number<00:04:34.240><c> three</c><00:04:34.479><c> as</c><00:04:34.720><c> well,</c><00:04:35.040><c> but</c><00:04:35.280><c> the</c><00:04:35.440><c> models</c>

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number three as well, but the models
 

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number three as well, but the models
before<00:04:36.160><c> that</c><00:04:36.400><c> are</c><00:04:36.639><c> Fable</c><00:04:37.120><c> 5</c><00:04:37.280><c> and</c><00:04:37.440><c> Opus</c><00:04:37.919><c> 5,</c>

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before that are Fable 5 and Opus 5,
 

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before that are Fable 5 and Opus 5,
which<00:04:38.880><c> are</c><00:04:39.040><c> only</c><00:04:39.280><c> above</c><00:04:39.600><c> the</c><00:04:39.759><c> model</c><00:04:40.080><c> by</c><00:04:40.479><c> about</c>

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which are only above the model by about
 

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which are only above the model by about
a<00:04:40.960><c> 1%</c><00:04:41.520><c> or</c><00:04:41.759><c> a</c><00:04:42.080><c> 2%</c><00:04:42.639><c> difference.</c><00:04:43.280><c> So</c><00:04:43.440><c> yeah,</c><00:04:43.680><c> even</c>

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a 1% or a 2% difference. So yeah, even
 

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a 1% or a 2% difference. So yeah, even
on<00:04:44.080><c> this</c><00:04:44.400><c> intelligence</c><00:04:44.960><c> index,</c><00:04:45.360><c> which</c><00:04:45.759><c> if</c>

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on this intelligence index, which if
 

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on this intelligence index, which if
you're<00:04:46.160><c> not</c><00:04:46.320><c> familiar</c><00:04:46.639><c> with</c><00:04:46.960><c> has</c><00:04:47.199><c> nine</c>

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you're not familiar with has nine
 

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you're not familiar with has nine
evaluations.<00:04:48.400><c> So</c><00:04:48.560><c> on</c><00:04:48.800><c> all</c><00:04:48.960><c> of</c><00:04:49.040><c> these</c>

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evaluations. So on all of these
 

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evaluations. So on all of these
evaluations,<00:04:50.160><c> it's</c><00:04:50.479><c> kind</c><00:04:50.639><c> of</c><00:04:50.800><c> matching</c>

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evaluations, it's kind of matching
 

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evaluations, it's kind of matching
almost<00:04:52.000><c> Fable</c><00:04:52.479><c> 5</c><00:04:52.720><c> performance,</c><00:04:53.199><c> which</c><00:04:53.440><c> is</c>

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almost Fable 5 performance, which is
 

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almost Fable 5 performance, which is
crazy<00:04:53.919><c> to</c><00:04:54.160><c> see.</c><00:04:54.880><c> Before</c><00:04:55.199><c> we</c><00:04:55.440><c> continue,</c><00:04:56.000><c> we</c>

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crazy to see. Before we continue, we
 

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crazy to see. Before we continue, we
just<00:04:56.479><c> launched</c><00:04:56.800><c> the</c><00:04:56.960><c> Universe</c><00:04:57.280><c> of</c><00:04:57.520><c> AI</c>

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just launched the Universe of AI
 

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just launched the Universe of AI
newsletter.<00:04:58.639><c> If</c><00:04:58.880><c> you</c><00:04:58.960><c> want</c><00:04:59.040><c> to</c><00:04:59.199><c> stay</c><00:04:59.360><c> on</c><00:04:59.520><c> top</c>

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newsletter. If you want to stay on top
 

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newsletter. If you want to stay on top
of<00:04:59.759><c> AI</c><00:05:00.160><c> news</c><00:05:00.479><c> without</c><00:05:00.800><c> having</c><00:05:01.040><c> to</c><00:05:01.199><c> hunt</c><00:05:01.440><c> for</c>

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of AI news without having to hunt for
 

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of AI news without having to hunt for
it,<00:05:02.000><c> link</c><00:05:02.240><c> is</c><00:05:02.400><c> in</c><00:05:02.560><c> the</c><00:05:02.720><c> description.</c><00:05:03.440><c> Don't</c>

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it, link is in the description. Don't
 

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it, link is in the description. Don't
miss<00:05:03.919><c> out.</c><00:05:04.560><c> And</c><00:05:04.720><c> what</c><00:05:04.880><c> you</c><00:05:05.040><c> see</c><00:05:05.120><c> on</c><00:05:05.360><c> screen</c>

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miss out. And what you see on screen
 

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miss out. And what you see on screen
right<00:05:05.759><c> now</c><00:05:05.840><c> is</c><00:05:06.000><c> a</c><00:05:06.240><c> racing</c><00:05:06.560><c> game</c><00:05:06.800><c> that</c><00:05:07.280><c> Grock</c>

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right now is a racing game that Grock
 

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right now is a racing game that Grock
4.6<00:05:08.479><c> build.</c><00:05:09.039><c> And</c><00:05:09.199><c> based</c><00:05:09.440><c> off</c><00:05:09.600><c> of</c><00:05:09.759><c> this</c><00:05:10.000><c> post,</c>

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4.6 build. And based off of this post,
 

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4.6 build. And based off of this post,
the<00:05:10.560><c> model</c><00:05:10.880><c> took</c><00:05:11.120><c> about</c><00:05:11.360><c> 1</c><00:05:11.600><c> minute</c><00:05:11.840><c> and</c><00:05:12.080><c> it</c><00:05:12.240><c> was</c>

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the model took about 1 minute and it was
 

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the model took about 1 minute and it was
a<00:05:12.479><c> fiveword</c><00:05:12.960><c> prompt,</c><00:05:13.360><c> which</c><00:05:13.520><c> was</c><00:05:13.680><c> a</c><00:05:13.919><c> create</c><00:05:14.160><c> a</c>

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a fiveword prompt, which was a create a
 

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a fiveword prompt, which was a create a
simple<00:05:14.720><c> racing</c><00:05:15.039><c> game</c><00:05:15.199><c> in</c><00:05:15.440><c> HTML.</c><00:05:16.320><c> So,</c><00:05:16.479><c> if</c>

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simple racing game in HTML. So, if
 

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simple racing game in HTML. So, if
you're<00:05:16.800><c> able</c><00:05:16.960><c> to</c><00:05:17.120><c> generate</c><00:05:17.440><c> something</c><00:05:17.680><c> like</c>

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you're able to generate something like
 

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you're able to generate something like
this<00:05:18.479><c> easily</c><00:05:19.199><c> using</c><00:05:19.520><c> Grock</c><00:05:19.919><c> 4.6,</c><00:05:20.560><c> I</c><00:05:20.720><c> think</c><00:05:20.800><c> a</c>

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this easily using Grock 4.6, I think a
 

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this easily using Grock 4.6, I think a
lot<00:05:21.039><c> of</c><00:05:21.120><c> people</c><00:05:21.280><c> will</c><00:05:21.520><c> be</c><00:05:21.680><c> happy.</c><00:05:22.400><c> And</c><00:05:22.560><c> this</c><00:05:22.720><c> is</c>

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lot of people will be happy. And this is
 

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lot of people will be happy. And this is
a<00:05:23.039><c> more</c><00:05:23.199><c> detailed</c><00:05:23.680><c> analysis</c><00:05:24.160><c> of</c><00:05:24.720><c> what</c><00:05:24.960><c> it</c>

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a more detailed analysis of what it
 

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a more detailed analysis of what it
costs<00:05:25.440><c> to</c><00:05:25.680><c> run</c><00:05:25.919><c> GPT</c><00:05:26.479><c> 5.6</c><00:05:26.960><c> 6</c><00:05:27.120><c> on</c><00:05:27.280><c> the</c><00:05:27.360><c> artificial</c>

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costs to run GPT 5.6 6 on the artificial
 

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costs to run GPT 5.6 6 on the artificial
analysis<00:05:28.400><c> index</c><00:05:28.800><c> and</c><00:05:29.039><c> what</c><00:05:29.280><c> it</c><00:05:29.520><c> produced</c>

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analysis index and what it produced
 

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analysis index and what it produced
meaning<00:05:30.160><c> the</c><00:05:30.479><c> output.</c><00:05:31.120><c> Both</c><00:05:31.360><c> of</c><00:05:31.440><c> these</c><00:05:31.680><c> models</c>

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meaning the output. Both of these models
 

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meaning the output. Both of these models
if<00:05:32.160><c> you</c><00:05:32.400><c> remember</c><00:05:32.880><c> scored</c><00:05:33.280><c> 61</c><00:05:33.840><c> on</c><00:05:34.000><c> the</c>

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if you remember scored 61 on the
 

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if you remember scored 61 on the
artificial<00:05:34.639><c> analysis</c><00:05:35.199><c> index.</c><00:05:35.680><c> Now</c><00:05:35.919><c> to</c><00:05:36.080><c> run</c>

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artificial analysis index. Now to run
 

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artificial analysis index. Now to run
the<00:05:36.400><c> whole</c><00:05:36.560><c> test</c><00:05:36.880><c> with</c><00:05:37.039><c> GPT</c><00:05:37.600><c> 5.6</c><00:05:38.479><c> it</c><00:05:38.639><c> cost</c>

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the whole test with GPT 5.6 it cost
 

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the whole test with GPT 5.6 it cost
about<00:05:39.199><c> 2.8K</c><00:05:40.400><c> and</c><00:05:40.560><c> then</c><00:05:40.720><c> with</c><00:05:40.960><c> Grock</c><00:05:41.360><c> 4.6</c><00:05:42.160><c> it</c>

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about 2.8K and then with Grock 4.6 it
 

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about 2.8K and then with Grock 4.6 it
cost<00:05:42.639><c> about</c><00:05:42.880><c> 1.1Kish.</c><00:05:44.320><c> And</c><00:05:44.560><c> this</c><00:05:44.800><c> tells</c><00:05:44.960><c> you</c>

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cost about 1.1Kish. And this tells you
 

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cost about 1.1Kish. And this tells you
that<00:05:45.520><c> you're</c><00:05:45.759><c> getting</c><00:05:46.080><c> similar</c><00:05:46.479><c> level</c><00:05:46.800><c> of</c>

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that you're getting similar level of
 

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that you're getting similar level of
performance<00:05:47.440><c> at</c><00:05:47.759><c> half</c><00:05:47.919><c> the</c><00:05:48.160><c> cost.</c><00:05:48.479><c> So</c><00:05:48.720><c> yeah,</c>

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performance at half the cost. So yeah,
 

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performance at half the cost. So yeah,
this<00:05:49.440><c> is</c><00:05:49.600><c> a</c><00:05:49.840><c> big</c><00:05:50.080><c> release</c><00:05:50.320><c> for</c><00:05:50.479><c> the</c><00:05:50.639><c> SpaceX</c>

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this is a big release for the SpaceX
 

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this is a big release for the SpaceX
team<00:05:51.280><c> because</c><00:05:51.520><c> they</c><00:05:51.759><c> just</c><00:05:51.840><c> proved</c><00:05:52.160><c> once</c><00:05:52.479><c> again</c>

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team because they just proved once again
 

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team because they just proved once again
that<00:05:52.880><c> they</c><00:05:53.120><c> are</c><00:05:53.199><c> a</c><00:05:53.360><c> lab</c><00:05:53.600><c> that</c><00:05:53.759><c> you</c><00:05:54.000><c> seriously</c>

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that they are a lab that you seriously
 

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that they are a lab that you seriously
start<00:05:54.720><c> need</c><00:05:54.800><c> to</c><00:05:55.039><c> considering,</c><00:05:55.680><c> especially</c><00:05:56.080><c> in</c>

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start need to considering, especially in
 

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start need to considering, especially in
2026.<00:05:57.360><c> And</c><00:05:57.520><c> I'm</c><00:05:57.680><c> going</c><00:05:57.759><c> to</c><00:05:57.919><c> talk</c><00:05:58.080><c> more</c><00:05:58.320><c> about</c>

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2026. And I'm going to talk more about
 

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2026. And I'm going to talk more about
Deep<00:05:58.880><c> Seek</c><00:05:59.199><c> version</c><00:05:59.520><c> 4</c><00:05:59.759><c> Pro</c><00:06:00.080><c> GA.</c><00:06:00.800><c> But</c>

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Deep Seek version 4 Pro GA. But
 

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Deep Seek version 4 Pro GA. But
basically<00:06:01.680><c> what</c><00:06:01.919><c> we're</c><00:06:02.160><c> seeing</c><00:06:02.400><c> today</c><00:06:02.639><c> is</c>

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basically what we're seeing today is
 

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basically what we're seeing today is
that<00:06:03.199><c> both</c><00:06:03.440><c> of</c><00:06:03.520><c> these</c><00:06:03.759><c> releases</c><00:06:04.319><c> kind</c><00:06:04.560><c> of</c>

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that both of these releases kind of
 

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that both of these releases kind of
emphasize<00:06:05.199><c> the</c><00:06:05.520><c> fact</c><00:06:05.680><c> that</c><00:06:06.400><c> performance</c><00:06:07.039><c> and</c>

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emphasize the fact that performance and
 

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emphasize the fact that performance and
all<00:06:07.520><c> above</c><00:06:07.840><c> that</c><00:06:08.080><c> is</c><00:06:08.319><c> the</c><00:06:08.479><c> price</c><00:06:08.800><c> at</c><00:06:09.039><c> what</c>

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all above that is the price at what
 

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all above that is the price at what
you're<00:06:09.440><c> getting</c><00:06:09.600><c> for</c><00:06:09.759><c> that</c><00:06:10.000><c> performance</c><00:06:10.400><c> is</c>

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you're getting for that performance is
 

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you're getting for that performance is
becoming<00:06:10.960><c> more</c><00:06:11.120><c> and</c><00:06:11.360><c> more</c><00:06:11.520><c> important</c><00:06:11.919><c> for</c><00:06:12.160><c> all</c>

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becoming more and more important for all
 

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becoming more and more important for all
users<00:06:13.120><c> because</c><00:06:13.440><c> we</c><00:06:13.680><c> see</c><00:06:13.840><c> many</c><00:06:14.160><c> labs</c><00:06:14.560><c> now</c>

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users because we see many labs now
 

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users because we see many labs now
focusing<00:06:15.280><c> on</c><00:06:15.680><c> creating</c><00:06:16.000><c> the</c><00:06:16.240><c> best</c><00:06:16.479><c> model</c><00:06:16.800><c> at</c>

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focusing on creating the best model at
 

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focusing on creating the best model at
the<00:06:17.199><c> cheapest</c><00:06:17.600><c> cost.</c><00:06:18.319><c> Last</c><00:06:18.479><c> year</c><00:06:18.639><c> in</c><00:06:18.880><c> 2025</c>

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the cheapest cost. Last year in 2025
 

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the cheapest cost. Last year in 2025
most<00:06:19.759><c> of</c><00:06:19.840><c> the</c><00:06:20.000><c> labs</c><00:06:20.400><c> were</c><00:06:20.560><c> just</c><00:06:20.800><c> focused</c><00:06:21.120><c> on</c><00:06:21.440><c> I</c>

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most of the labs were just focused on I
 

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most of the labs were just focused on I
would<00:06:21.840><c> say</c><00:06:22.479><c> creating</c><00:06:22.880><c> the</c><00:06:23.199><c> strongest</c><00:06:23.759><c> model.</c>

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would say creating the strongest model.
 

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would say creating the strongest model.
Yes,<00:06:24.720><c> cost</c><00:06:24.960><c> was</c><00:06:25.199><c> important,</c><00:06:25.520><c> but</c><00:06:25.759><c> I</c><00:06:25.919><c> think</c>

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Yes, cost was important, but I think
 

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Yes, cost was important, but I think
most<00:06:26.240><c> of</c><00:06:26.319><c> the</c><00:06:26.479><c> times</c><00:06:26.800><c> the</c><00:06:26.960><c> frontier</c><00:06:27.440><c> labs,</c>

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most of the times the frontier labs,
 

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most of the times the frontier labs,
meaning<00:06:28.080><c> OpenAI,</c><00:06:28.639><c> Enthropic,</c><00:06:29.520><c> were</c><00:06:29.840><c> kind</c><00:06:29.919><c> of</c>

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meaning OpenAI, Enthropic, were kind of
 

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meaning OpenAI, Enthropic, were kind of
more<00:06:30.240><c> lenient</c><00:06:30.639><c> on</c><00:06:30.800><c> that</c><00:06:31.039><c> fact</c><00:06:31.280><c> because</c><00:06:31.520><c> they</c>

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more lenient on that fact because they
 

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more lenient on that fact because they
didn't<00:06:32.000><c> have</c><00:06:32.240><c> as</c><00:06:32.560><c> strong</c><00:06:32.720><c> of</c><00:06:32.960><c> a</c><00:06:33.120><c> competition.</c>

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didn't have as strong of a competition.
 

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didn't have as strong of a competition.
Intelligent<00:06:34.639><c> models</c><00:06:35.039><c> that</c><00:06:35.280><c> are</c><00:06:35.759><c> maybe</c><00:06:36.160><c> not</c>

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Intelligent models that are maybe not
 

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Intelligent models that are maybe not
always<00:06:36.880><c> ahead</c><00:06:37.360><c> of</c><00:06:37.600><c> OpenAI</c><00:06:38.160><c> Enthropic,</c><00:06:38.800><c> but</c>

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always ahead of OpenAI Enthropic, but
 

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always ahead of OpenAI Enthropic, but
match<00:06:39.280><c> their</c><00:06:39.520><c> performance</c><00:06:39.919><c> at</c><00:06:40.160><c> a</c><00:06:40.319><c> fraction</c><00:06:40.560><c> of</c>

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match their performance at a fraction of
 

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match their performance at a fraction of
the<00:06:40.880><c> cost.</c><00:06:41.199><c> So</c><00:06:41.360><c> yes,</c><00:06:41.759><c> price</c><00:06:42.080><c> toerformance</c>

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the cost. So yes, price toerformance
 

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the cost. So yes, price toerformance
ratio<00:06:43.199><c> is</c><00:06:43.440><c> becoming</c><00:06:43.759><c> a</c><00:06:44.080><c> critical</c><00:06:44.880><c> I</c><00:06:45.039><c> would</c><00:06:45.199><c> say</c>

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ratio is becoming a critical I would say
 

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ratio is becoming a critical I would say
indicator<00:06:46.240><c> in</c><00:06:46.560><c> 2026.</c><00:06:47.840><c> Now,</c><00:06:48.080><c> this</c><00:06:48.240><c> is</c><00:06:48.400><c> the</c>

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indicator in 2026. Now, this is the
 

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indicator in 2026. Now, this is the
updated<00:06:49.120><c> benchmark</c><00:06:49.600><c> chart</c><00:06:49.919><c> after</c><00:06:50.240><c> the</c>

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updated benchmark chart after the
 

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updated benchmark chart after the
release<00:06:50.720><c> of</c><00:06:50.800><c> the</c><00:06:51.039><c> new</c><00:06:51.199><c> model.</c><00:06:51.680><c> And</c><00:06:51.919><c> one</c><00:06:52.160><c> thing</c>

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release of the new model. And one thing
 

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release of the new model. And one thing
you'll<00:06:52.479><c> see</c><00:06:52.639><c> across</c><00:06:52.960><c> the</c><00:06:53.199><c> board</c><00:06:53.360><c> is</c><00:06:53.600><c> that</c><00:06:53.759><c> it</c>

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you'll see across the board is that it
 

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you'll see across the board is that it
matches<00:06:54.319><c> the</c><00:06:54.560><c> top</c><00:06:54.880><c> level</c><00:06:55.199><c> performance</c><00:06:55.680><c> of</c>

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matches the top level performance of
 

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matches the top level performance of
many<00:06:56.080><c> of</c><00:06:56.240><c> the</c><00:06:56.400><c> models.</c><00:06:56.880><c> The</c><00:06:57.039><c> one</c><00:06:57.199><c> thing</c>

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many of the models. The one thing
 

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many of the models. The one thing
interesting<00:06:57.680><c> over</c><00:06:57.919><c> here</c><00:06:58.000><c> is</c><00:06:58.160><c> that</c><00:06:58.319><c> they</c>

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interesting over here is that they
 

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interesting over here is that they
haven't<00:06:58.639><c> put</c><00:06:58.800><c> Opus</c><00:06:59.280><c> 5</c><00:06:59.520><c> here</c><00:06:59.759><c> for</c><00:07:00.000><c> some</c><00:07:00.160><c> reason.</c>

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haven't put Opus 5 here for some reason.
 

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haven't put Opus 5 here for some reason.
There<00:07:00.800><c> is</c><00:07:00.960><c> Fable</c><00:07:01.360><c> 5</c><00:07:01.599><c> here</c><00:07:01.759><c> that</c><00:07:02.000><c> we</c><00:07:02.160><c> can</c>

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There is Fable 5 here that we can
 

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There is Fable 5 here that we can
compare<00:07:02.560><c> this</c><00:07:02.800><c> model</c><00:07:03.120><c> against.</c><00:07:03.680><c> But</c><00:07:03.840><c> one</c>

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compare this model against. But one
 

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compare this model against. But one
thing<00:07:04.160><c> you'll</c><00:07:04.400><c> notice</c><00:07:04.639><c> is</c><00:07:04.880><c> that</c><00:07:05.120><c> Deep</c><00:07:05.440><c> Seek,</c>

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thing you'll notice is that Deep Seek,
 

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thing you'll notice is that Deep Seek,
remember<00:07:06.560><c> this</c><00:07:06.800><c> model</c><00:07:07.120><c> costs</c><00:07:07.520><c> 43.5</c><00:07:08.639><c> cents</c><00:07:08.960><c> per</c>

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remember this model costs 43.5 cents per
 

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remember this model costs 43.5 cents per
million<00:07:09.520><c> input</c><00:07:09.919><c> tokens</c><00:07:10.560><c> and</c><00:07:10.880><c> 87</c><00:07:11.520><c> cents</c><00:07:11.840><c> per</c>

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million input tokens and 87 cents per
 

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million input tokens and 87 cents per
million<00:07:12.319><c> output</c><00:07:12.720><c> tokens.</c><00:07:13.440><c> While</c><00:07:13.599><c> the</c><00:07:13.759><c> other</c>

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million output tokens. While the other
 

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million output tokens. While the other
models<00:07:14.319><c> all</c><00:07:14.560><c> over</c><00:07:14.800><c> here</c><00:07:14.960><c> are</c><00:07:15.280><c> way</c><00:07:15.440><c> more</c>

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models all over here are way more
 

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models all over here are way more
expensive<00:07:16.160><c> than</c><00:07:16.319><c> that.</c><00:07:16.800><c> So</c><00:07:16.960><c> the</c><00:07:17.199><c> first</c><00:07:17.280><c> thing</c>

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expensive than that. So the first thing
 

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expensive than that. So the first thing
if<00:07:17.599><c> you</c><00:07:17.759><c> look</c><00:07:17.840><c> at</c><00:07:18.000><c> terminal</c><00:07:18.479><c> bench</c><00:07:18.880><c> 2.1</c><00:07:19.759><c> the</c>

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if you look at terminal bench 2.1 the
 

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if you look at terminal bench 2.1 the
model<00:07:20.319><c> scores</c><00:07:20.800><c> 87.9.</c>

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model scores 87.9.
 

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model scores 87.9.
The<00:07:22.560><c> older</c><00:07:22.880><c> version</c><00:07:23.039><c> of</c><00:07:23.199><c> the</c><00:07:23.360><c> model</c><00:07:23.599><c> was</c><00:07:23.840><c> 72.1</c>

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The older version of the model was 72.1
 

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The older version of the model was 72.1
and<00:07:25.199><c> the</c><00:07:25.360><c> flash</c><00:07:25.759><c> version</c><00:07:26.080><c> which</c><00:07:26.319><c> we</c><00:07:26.560><c> got</c><00:07:26.800><c> last</c>

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and the flash version which we got last
 

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and the flash version which we got last
week<00:07:27.440><c> was</c><00:07:27.759><c> 82.7.</c><00:07:29.199><c> And</c><00:07:29.360><c> what's</c><00:07:29.599><c> crazy</c><00:07:29.919><c> is</c><00:07:30.080><c> that</c>

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week was 82.7. And what's crazy is that
 

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week was 82.7. And what's crazy is that
Fable<00:07:30.800><c> 5</c><00:07:31.120><c> is</c><00:07:31.440><c> 88.</c><00:07:32.160><c> Yes,</c><00:07:32.560><c> 88.</c><00:07:33.199><c> So</c><00:07:33.440><c> this</c><00:07:33.599><c> model</c><00:07:33.840><c> is</c>

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Fable 5 is 88. Yes, 88. So this model is
 

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Fable 5 is 88. Yes, 88. So this model is
only.1%<00:07:35.199><c> behind</c><00:07:35.680><c> Fable</c><00:07:36.160><c> 5.</c><00:07:36.720><c> And</c><00:07:36.880><c> then</c><00:07:37.039><c> on</c><00:07:37.199><c> the</c>

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only.1% behind Fable 5. And then on the
 

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only.1% behind Fable 5. And then on the
Cyber<00:07:37.680><c> Gym,</c><00:07:37.919><c> which</c><00:07:38.080><c> is</c><00:07:38.160><c> Cyber</c><00:07:38.560><c> Security,</c><00:07:39.280><c> the</c>

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Cyber Gym, which is Cyber Security, the
 

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Cyber Gym, which is Cyber Security, the
model<00:07:39.840><c> actually</c><00:07:40.319><c> beats</c><00:07:40.639><c> Fable</c><00:07:41.120><c> 5.</c><00:07:41.599><c> Fable</c><00:07:42.080><c> 5</c>

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model actually beats Fable 5. Fable 5
 

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model actually beats Fable 5. Fable 5
sits<00:07:42.479><c> at</c><00:07:42.720><c> 83.1</c><00:07:43.919><c> while</c><00:07:44.160><c> Deepseek</c><00:07:44.720><c> version</c><00:07:45.039><c> 4</c>

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sits at 83.1 while Deepseek version 4
 

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sits at 83.1 while Deepseek version 4
Pro,<00:07:45.520><c> the</c><00:07:45.680><c> new</c><00:07:45.840><c> one</c><00:07:45.919><c> that</c><00:07:46.080><c> we</c><00:07:46.240><c> got</c><00:07:46.400><c> today,</c><00:07:46.880><c> is</c>

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Pro, the new one that we got today, is
 

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Pro, the new one that we got today, is
at<00:07:47.280><c> 83.3.</c><00:07:48.720><c> Now,</c><00:07:48.880><c> if</c><00:07:49.039><c> this</c><00:07:49.280><c> tells</c><00:07:49.520><c> you</c>

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at 83.3. Now, if this tells you
 

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at 83.3. Now, if this tells you
something<00:07:50.000><c> is</c><00:07:50.240><c> that</c><00:07:50.560><c> this</c><00:07:50.880><c> model</c><00:07:51.199><c> is</c><00:07:51.440><c> once</c>

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something is that this model is once
 

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something is that this model is once
again<00:07:51.919><c> really</c><00:07:52.240><c> geared</c><00:07:52.560><c> at</c><00:07:52.720><c> cyber</c><00:07:53.039><c> security.</c>

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again really geared at cyber security.
 

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again really geared at cyber security.
We<00:07:53.919><c> saw</c><00:07:54.080><c> the</c><00:07:54.240><c> Flash</c><00:07:54.560><c> model</c><00:07:54.879><c> also</c><00:07:55.120><c> be</c><00:07:55.280><c> geared</c>

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We saw the Flash model also be geared
 

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We saw the Flash model also be geared
towards<00:07:55.840><c> cyber</c><00:07:56.240><c> security</c><00:07:56.560><c> and</c><00:07:56.800><c> becoming</c><00:07:57.120><c> a</c>

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towards cyber security and becoming a
 

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towards cyber security and becoming a
model<00:07:57.440><c> that</c><00:07:57.680><c> was</c><00:07:57.840><c> quite</c><00:07:58.080><c> capable</c><00:07:58.400><c> in</c><00:07:58.639><c> that</c>

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model that was quite capable in that
 

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model that was quite capable in that
area.<00:07:59.360><c> And</c><00:07:59.520><c> we're</c><00:07:59.759><c> seeing</c><00:08:00.000><c> the</c><00:08:00.160><c> same</c><00:08:00.400><c> thing</c>

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area. And we're seeing the same thing
 

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area. And we're seeing the same thing
today<00:08:01.120><c> with</c><00:08:01.360><c> the</c><00:08:01.520><c> new</c><00:08:01.680><c> version</c><00:08:02.000><c> 4</c><00:08:02.240><c> Pro</c><00:08:02.479><c> which</c>

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today with the new version 4 Pro which
 

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today with the new version 4 Pro which
sits<00:08:02.879><c> at</c><00:08:03.120><c> 83.3</c><00:08:04.319><c> outperforming</c><00:08:05.039><c> Fable</c><00:08:05.440><c> 5</c><00:08:05.680><c> on</c>

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sits at 83.3 outperforming Fable 5 on
 

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sits at 83.3 outperforming Fable 5 on
the<00:08:06.000><c> benchmark.</c><00:08:06.800><c> On</c><00:08:07.039><c> deep</c><00:08:07.360><c> software</c>

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the benchmark. On deep software
 

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the benchmark. On deep software
engineering,<00:08:08.400><c> this</c><00:08:08.639><c> model</c><00:08:08.879><c> is</c><00:08:09.120><c> not</c><00:08:09.360><c> beating</c>

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engineering, this model is not beating
 

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engineering, this model is not beating
Fable<00:08:10.319><c> 5</c><00:08:10.560><c> because</c><00:08:10.800><c> Fable</c><00:08:11.199><c> 5</c><00:08:11.440><c> sits</c><00:08:11.680><c> at</c><00:08:11.919><c> 70,</c><00:08:12.400><c> but</c>

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Fable 5 because Fable 5 sits at 70, but
 

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Fable 5 because Fable 5 sits at 70, but
the<00:08:12.800><c> model</c><00:08:13.039><c> achieves</c><00:08:13.520><c> 62.7</c><00:08:14.879><c> which</c><00:08:15.039><c> is</c><00:08:15.199><c> ahead</c>

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the model achieves 62.7 which is ahead
 

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the model achieves 62.7 which is ahead
of<00:08:15.599><c> GLM</c><00:08:16.080><c> 5.2</c><00:08:16.960><c> and</c><00:08:17.199><c> a</c><00:08:17.360><c> bit</c><00:08:17.520><c> behind</c><00:08:17.840><c> Kimmy</c><00:08:18.240><c> K3</c>

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of GLM 5.2 and a bit behind Kimmy K3
 

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of GLM 5.2 and a bit behind Kimmy K3
which<00:08:18.879><c> sits</c><00:08:19.039><c> at</c><00:08:19.360><c> 67.5.</c>

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which sits at 67.5.
 

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which sits at 67.5.
But<00:08:21.039><c> one</c><00:08:21.280><c> thing</c><00:08:21.440><c> again,</c><00:08:21.840><c> this</c><00:08:22.080><c> is</c><00:08:22.160><c> a</c><00:08:22.400><c> big</c><00:08:22.560><c> jump</c>

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But one thing again, this is a big jump
 

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But one thing again, this is a big jump
compared<00:08:23.199><c> to</c><00:08:23.440><c> the</c><00:08:23.680><c> preview</c><00:08:24.000><c> version</c><00:08:24.240><c> of</c><00:08:24.400><c> the</c>

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compared to the preview version of the
 

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compared to the preview version of the
model<00:08:25.039><c> which</c><00:08:25.280><c> was</c><00:08:25.440><c> at</c><00:08:25.680><c> 12.8.</c><00:08:26.639><c> So</c><00:08:26.879><c> yeah,</c><00:08:27.120><c> they</c>

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model which was at 12.8. So yeah, they
 

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model which was at 12.8. So yeah, they
have<00:08:27.520><c> really</c><00:08:27.759><c> trained</c><00:08:28.080><c> this</c><00:08:28.319><c> model</c><00:08:28.560><c> and</c><00:08:28.800><c> they</c>

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have really trained this model and they
 

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have really trained this model and they
have<00:08:29.120><c> improved</c><00:08:29.520><c> it</c><00:08:29.599><c> on</c><00:08:29.759><c> the</c><00:08:29.919><c> back</c><00:08:30.080><c> end</c><00:08:30.319><c> because</c>

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have improved it on the back end because
 

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have improved it on the back end because
we<00:08:30.879><c> can</c><00:08:30.960><c> clearly</c><00:08:31.440><c> see</c><00:08:31.759><c> in</c><00:08:32.000><c> this</c><00:08:32.240><c> deep</c><00:08:32.479><c> software</c>

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we can clearly see in this deep software
 

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we can clearly see in this deep software
engineering<00:08:33.360><c> benchmark.</c><00:08:34.320><c> And</c><00:08:34.560><c> there's</c><00:08:34.719><c> a</c>

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engineering benchmark. And there's a
 

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engineering benchmark. And there's a
couple<00:08:35.039><c> of</c><00:08:35.200><c> other</c><00:08:35.360><c> benchmarks,</c><00:08:35.919><c> but</c><00:08:36.240><c> one</c><00:08:36.479><c> key</c>

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couple of other benchmarks, but one key
 

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couple of other benchmarks, but one key
benchmark<00:08:37.120><c> that</c><00:08:37.360><c> this</c><00:08:37.680><c> model</c><00:08:38.080><c> excels</c><00:08:38.479><c> at</c><00:08:38.880><c> is</c>

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benchmark that this model excels at is
 

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benchmark that this model excels at is
the<00:08:39.440><c> automation</c><00:08:40.000><c> bench</c><00:08:40.560><c> where</c><00:08:40.800><c> the</c><00:08:41.039><c> model</c>

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the automation bench where the model
 

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the automation bench where the model
achieves<00:08:41.760><c> 31.8</c><00:08:43.039><c> and</c><00:08:43.279><c> Fable</c><00:08:43.680><c> 5</c><00:08:44.159><c> 29.1.</c><00:08:45.279><c> So</c><00:08:45.440><c> once</c>

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achieves 31.8 and Fable 5 29.1. So once
 

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achieves 31.8 and Fable 5 29.1. So once
again,<00:08:45.920><c> the</c><00:08:46.160><c> model</c><00:08:46.399><c> outperforms</c><00:08:47.040><c> Fable</c><00:08:47.519><c> 5.</c>

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again, the model outperforms Fable 5.
 

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again, the model outperforms Fable 5.
Now<00:08:48.240><c> this</c><00:08:48.399><c> is</c><00:08:48.560><c> a</c><00:08:48.720><c> fraction</c><00:08:48.959><c> of</c><00:08:49.120><c> the</c><00:08:49.279><c> cost</c><00:08:49.519><c> as</c><00:08:49.760><c> I</c>

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Now this is a fraction of the cost as I
 

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Now this is a fraction of the cost as I
mentioned.<00:08:50.240><c> This</c><00:08:50.399><c> is</c><00:08:50.560><c> 43</c><00:08:50.959><c> cents</c><00:08:51.200><c> and</c><00:08:51.519><c> 87</c><00:08:52.240><c> for</c>

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mentioned. This is 43 cents and 87 for
 

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mentioned. This is 43 cents and 87 for
input<00:08:52.800><c> and</c><00:08:53.040><c> output</c><00:08:53.440><c> respectively</c><00:08:54.240><c> versus</c>

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input and output respectively versus
 

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input and output respectively versus
Fable<00:08:55.040><c> 5</c><00:08:55.279><c> which</c><00:08:55.519><c> is</c><00:08:55.680><c> at</c><00:08:56.000><c> $10.50.</c>

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Fable 5 which is at $10.50.
 

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Fable 5 which is at $10.50.
So<00:08:57.680><c> yes,</c><00:08:57.920><c> if</c><00:08:58.080><c> we</c><00:08:58.160><c> look</c><00:08:58.240><c> at</c><00:08:58.399><c> the</c><00:08:58.560><c> terminal</c><00:08:58.880><c> bench</c>

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So yes, if we look at the terminal bench
 

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So yes, if we look at the terminal bench
for<00:08:59.360><c> example,</c><00:09:00.160><c> we</c><00:09:00.399><c> are</c><00:09:00.560><c> seeing</c><00:09:00.800><c> this</c><00:09:00.959><c> model</c>

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for example, we are seeing this model
 

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for example, we are seeing this model
achieve<00:09:01.600><c> a</c><00:09:01.839><c> result</c><00:09:02.160><c> which</c><00:09:02.399><c> is</c><00:09:02.640><c> 0.1</c><00:09:03.120><c> behind</c><00:09:03.440><c> the</c>

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achieve a result which is 0.1 behind the
 

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achieve a result which is 0.1 behind the
best<00:09:03.839><c> model</c><00:09:04.240><c> out</c><00:09:04.399><c> there</c><00:09:04.640><c> Fable</c><00:09:05.040><c> 5</c><00:09:05.680><c> and</c><00:09:05.920><c> the</c>

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best model out there Fable 5 and the
 

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best model out there Fable 5 and the
pricing<00:09:06.560><c> is</c><00:09:06.880><c> insane</c><00:09:07.279><c> to</c><00:09:07.440><c> look</c><00:09:07.600><c> at</c><00:09:08.160><c> $43</c><00:09:09.040><c> versus</c>

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pricing is insane to look at $43 versus
 

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pricing is insane to look at $43 versus
$10<00:09:10.480><c> 87</c><00:09:11.279><c> versus</c><00:09:11.839><c> $50</c><00:09:12.640><c> per</c><00:09:12.880><c> million</c><00:09:13.120><c> input</c>

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$10 87 versus $50 per million input
 

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$10 87 versus $50 per million input
tokens.<00:09:14.080><c> So</c><00:09:14.240><c> when</c><00:09:14.480><c> we</c><00:09:14.560><c> turn</c><00:09:14.800><c> that</c><00:09:14.959><c> into</c><00:09:15.200><c> a</c>

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tokens. So when we turn that into a
 

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tokens. So when we turn that into a
fraction,<00:09:16.160><c> this</c><00:09:16.399><c> model</c><00:09:16.720><c> is</c><00:09:17.040><c> 57</c><00:09:17.760><c> times</c>

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fraction, this model is 57 times
 

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fraction, this model is 57 times
cheaper.<00:09:18.959><c> And</c><00:09:19.200><c> earlier</c><00:09:19.600><c> last</c><00:09:19.920><c> week,</c><00:09:20.080><c> I</c><00:09:20.240><c> think</c>

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cheaper. And earlier last week, I think
 

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cheaper. And earlier last week, I think
I<00:09:20.560><c> made</c><00:09:20.720><c> a</c><00:09:20.880><c> video</c><00:09:21.040><c> about</c><00:09:21.279><c> how</c><00:09:21.600><c> DeepSeek</c>

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I made a video about how DeepSeek
 

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I made a video about how DeepSeek
version<00:09:22.480><c> 4</c><00:09:22.720><c> Pro</c><00:09:23.279><c> was</c><00:09:23.600><c> going</c><00:09:23.760><c> to</c><00:09:23.920><c> achieve</c><00:09:24.240><c> a</c>

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version 4 Pro was going to achieve a
 

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version 4 Pro was going to achieve a
result<00:09:24.720><c> like</c><00:09:24.959><c> this</c><00:09:25.200><c> because</c><00:09:25.440><c> there</c><00:09:25.600><c> was</c><00:09:25.680><c> a</c>

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result like this because there was a
 

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result like this because there was a
investor<00:09:26.399><c> report</c><00:09:26.640><c> that</c><00:09:26.880><c> leaked.</c><00:09:27.360><c> And</c><00:09:27.519><c> when</c><00:09:27.680><c> I</c>

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investor report that leaked. And when I
 

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investor report that leaked. And when I
saw<00:09:28.000><c> the</c><00:09:28.160><c> pricing</c><00:09:28.560><c> where</c><00:09:28.720><c> it</c><00:09:28.959><c> said</c><00:09:29.040><c> that</c><00:09:29.200><c> it</c>

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saw the pricing where it said that it
 

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saw the pricing where it said that it
was<00:09:29.519><c> going</c><00:09:29.600><c> to</c><00:09:29.760><c> match</c><00:09:30.000><c> Fable</c><00:09:30.399><c> 5</c><00:09:30.560><c> or</c><00:09:30.800><c> even</c>

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was going to match Fable 5 or even
 

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was going to match Fable 5 or even
outperform<00:09:31.519><c> it</c><00:09:31.920><c> and</c><00:09:32.160><c> be</c><00:09:32.320><c> at</c><00:09:32.640><c> 57</c><00:09:33.120><c> times</c><00:09:33.440><c> cheaper</c>

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outperform it and be at 57 times cheaper
 

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outperform it and be at 57 times cheaper
per<00:09:34.000><c> cost.</c><00:09:34.640><c> When</c><00:09:34.800><c> I</c><00:09:34.959><c> was</c><00:09:35.040><c> reading</c><00:09:35.200><c> the</c>

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per cost. When I was reading the
 

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per cost. When I was reading the
investor<00:09:35.920><c> report,</c><00:09:36.320><c> I</c><00:09:36.480><c> didn't</c><00:09:36.640><c> really</c><00:09:36.880><c> believe</c>

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investor report, I didn't really believe
 

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investor report, I didn't really believe
it.<00:09:37.680><c> But</c><00:09:37.839><c> today,</c><00:09:38.080><c> it</c><00:09:38.240><c> is</c><00:09:38.399><c> proven</c><00:09:38.720><c> because</c><00:09:39.040><c> at</c>

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it. But today, it is proven because at
 

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it. But today, it is proven because at
least<00:09:39.360><c> on</c><00:09:39.519><c> the</c><00:09:39.680><c> benchmark</c><00:09:40.160><c> so</c><00:09:40.320><c> far,</c><00:09:40.640><c> yes,</c><00:09:40.800><c> I'm</c>

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least on the benchmark so far, yes, I'm
 

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least on the benchmark so far, yes, I'm
just<00:09:41.120><c> saying</c><00:09:41.360><c> the</c><00:09:41.519><c> benchmarks,</c><00:09:42.480><c> we</c><00:09:42.720><c> are</c>

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just saying the benchmarks, we are
 

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just saying the benchmarks, we are
seeing<00:09:43.200><c> similar</c><00:09:43.680><c> level</c><00:09:44.000><c> performance.</c><00:09:44.800><c> Now,</c>

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seeing similar level performance. Now,
 

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seeing similar level performance. Now,
if<00:09:45.279><c> this</c><00:09:45.519><c> model</c><00:09:45.839><c> actually</c><00:09:46.240><c> performs</c><00:09:46.720><c> like</c>

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if this model actually performs like
 

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if this model actually performs like
that<00:09:47.200><c> in</c><00:09:47.440><c> production,</c><00:09:48.160><c> it's</c><00:09:48.399><c> a</c><00:09:48.560><c> little</c><00:09:48.720><c> too</c>

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that in production, it's a little too
 

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that in production, it's a little too
early<00:09:49.200><c> to</c><00:09:49.440><c> tell</c><00:09:49.680><c> yet.</c><00:09:50.000><c> We</c><00:09:50.160><c> still</c><00:09:50.320><c> would</c><00:09:50.560><c> have</c>

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early to tell yet. We still would have
 

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early to tell yet. We still would have
to<00:09:50.800><c> give</c><00:09:50.959><c> it</c><00:09:51.040><c> a</c><00:09:51.200><c> couple</c><00:09:51.360><c> of</c><00:09:51.519><c> weeks</c><00:09:51.760><c> and</c><00:09:52.000><c> see</c><00:09:52.160><c> how</c>

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to give it a couple of weeks and see how
 

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to give it a couple of weeks and see how
it's<00:09:52.640><c> performing</c><00:09:53.040><c> in</c><00:09:53.279><c> the</c><00:09:53.440><c> long</c><00:09:53.600><c> run</c><00:09:54.080><c> because</c>

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it's performing in the long run because
 

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it's performing in the long run because
sometimes<00:09:54.800><c> on</c><00:09:54.959><c> the</c><00:09:55.120><c> release</c><00:09:55.440><c> day</c><00:09:55.760><c> the</c><00:09:56.000><c> models</c>

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sometimes on the release day the models
 

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sometimes on the release day the models
perform<00:09:56.880><c> quite</c><00:09:57.200><c> good,</c><00:09:57.600><c> but</c><00:09:57.839><c> over</c><00:09:58.080><c> time</c><00:09:58.240><c> they</c>

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perform quite good, but over time they
 

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perform quite good, but over time they
kind<00:09:58.640><c> of</c><00:09:58.720><c> deteriorate</c><00:09:59.200><c> in</c><00:09:59.440><c> their</c><00:09:59.680><c> quality.</c>

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kind of deteriorate in their quality.
 

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kind of deteriorate in their quality.
So,<00:10:00.399><c> we</c><00:10:00.560><c> hope</c><00:10:00.800><c> DeepS</c><00:10:01.360><c> doesn't</c><00:10:01.519><c> do</c><00:10:01.680><c> that.</c>

00:10:01.990 --> 00:10:02.000 align:start position:0%
So, we hope DeepS doesn't do that.
 

00:10:02.000 --> 00:10:03.670 align:start position:0%
So, we hope DeepS doesn't do that.
Historically,<00:10:02.560><c> they</c><00:10:02.800><c> haven't</c><00:10:03.120><c> done</c><00:10:03.279><c> that,</c>

00:10:03.670 --> 00:10:03.680 align:start position:0%
Historically, they haven't done that,
 

00:10:03.680 --> 00:10:05.590 align:start position:0%
Historically, they haven't done that,
but<00:10:03.920><c> let's</c><00:10:04.160><c> just</c><00:10:04.320><c> see.</c><00:10:04.720><c> Just</c><00:10:04.959><c> going</c><00:10:05.200><c> to</c><00:10:05.279><c> put</c><00:10:05.440><c> it</c>

00:10:05.590 --> 00:10:05.600 align:start position:0%
but let's just see. Just going to put it
 

00:10:05.600 --> 00:10:07.110 align:start position:0%
but let's just see. Just going to put it
out<00:10:05.760><c> there</c><00:10:06.000><c> because</c><00:10:06.240><c> right</c><00:10:06.480><c> now</c><00:10:06.640><c> we're</c><00:10:06.800><c> basing</c>

00:10:07.110 --> 00:10:07.120 align:start position:0%
out there because right now we're basing
 

00:10:07.120 --> 00:10:09.750 align:start position:0%
out there because right now we're basing
this<00:10:07.360><c> off</c><00:10:07.600><c> of</c><00:10:07.760><c> benchmarks.</c><00:10:09.200><c> But</c><00:10:09.360><c> that's</c><00:10:09.600><c> it</c>

00:10:09.750 --> 00:10:09.760 align:start position:0%
this off of benchmarks. But that's it
 

00:10:09.760 --> 00:10:11.509 align:start position:0%
this off of benchmarks. But that's it
for<00:10:10.000><c> today's</c><00:10:10.320><c> video.</c><00:10:10.880><c> Make</c><00:10:11.120><c> sure</c><00:10:11.200><c> you</c><00:10:11.360><c> guys</c>

00:10:11.509 --> 00:10:11.519 align:start position:0%
for today's video. Make sure you guys
 

00:10:11.519 --> 00:10:13.509 align:start position:0%
for today's video. Make sure you guys
are<00:10:11.760><c> subscribed</c><00:10:12.080><c> to</c><00:10:12.240><c> the</c><00:10:12.480><c> channel.</c><00:10:13.120><c> Follow</c>

00:10:13.509 --> 00:10:13.519 align:start position:0%
are subscribed to the channel. Follow
 

00:10:13.519 --> 00:10:15.110 align:start position:0%
are subscribed to the channel. Follow
our<00:10:13.760><c> new</c><00:10:13.920><c> newsletter</c><00:10:14.480><c> as</c><00:10:14.720><c> well</c><00:10:14.880><c> at</c>

00:10:15.110 --> 00:10:15.120 align:start position:0%
our new newsletter as well at
 

00:10:15.120 --> 00:10:17.350 align:start position:0%
our new newsletter as well at
universeofai.behive.com

00:10:17.350 --> 00:10:17.360 align:start position:0%
universeofai.behive.com
 

00:10:17.360 --> 00:10:19.030 align:start position:0%
universeofai.behive.com
as<00:10:17.600><c> well</c><00:10:17.680><c> as</c><00:10:17.839><c> subscribe</c><00:10:18.160><c> to</c><00:10:18.320><c> the</c><00:10:18.480><c> main</c><00:10:18.720><c> channel</c>

00:10:19.030 --> 00:10:19.040 align:start position:0%
as well as subscribe to the main channel
 

00:10:19.040 --> 00:10:21.509 align:start position:0%
as well as subscribe to the main channel
World<00:10:19.279><c> of</c><00:10:19.440><c> AI</c><00:10:20.079><c> and</c><00:10:20.320><c> support</c><00:10:20.640><c> us</c><00:10:20.800><c> on</c><00:10:21.040><c> X</c><00:10:21.360><c> by</c>

00:10:21.509 --> 00:10:21.519 align:start position:0%
World of AI and support us on X by
 

00:10:21.519 --> 00:10:23.750 align:start position:0%
World of AI and support us on X by
following<00:10:21.760><c> the</c><00:10:22.000><c> Universe</c><00:10:22.320><c> of</c><00:10:22.480><c> AIZ</c><00:10:23.120><c> as</c><00:10:23.279><c> well.</c>

00:10:23.750 --> 00:10:23.760 align:start position:0%
following the Universe of AIZ as well.
 

00:10:23.760 --> 00:10:25.350 align:start position:0%
following the Universe of AIZ as well.
Until<00:10:24.079><c> then,</c><00:10:24.399><c> I'll</c><00:10:24.560><c> see</c><00:10:24.720><c> you</c><00:10:24.880><c> guys</c><00:10:25.040><c> in</c><00:10:25.200><c> the</c>

00:10:25.350 --> 00:10:25.360 align:start position:0%
Until then, I'll see you guys in the
 

00:10:25.360 --> 00:10:27.519 align:start position:0%
Until then, I'll see you guys in the
next

