Latest / AI Fire Daily / #632 Neil: Grok 4.7 Challenges GPT And Claude With Coding Power

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#632 Neil: Grok 4.7 Challenges GPT And Claude With Coding Power · AI Fire Daily

• 0:00
  You look at the AI race over the last year, and
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  it's, well, it's felt almost settled of a mature
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  market even. Yeah, kind of locked in. Right.
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  ChatGPT and Claude have just been locked in this
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  endless subtle battle for the top spot. you know,
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  trading blows by a fraction of a percent. And
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  then Gemini suddenly re -entered the conversation.
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  Making it a three -way race, it feels incredibly
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  crowded at the frontier. Oh, absolutely. But
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  then the board gets flipped. Elon Musk and X
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  .A .I. drop a completely new weapon into the
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  ring. Yeah, they're calling it a honey badger,
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  a total beast, Grok 4 .7. But the real question
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  we have to answer today is whether the underlying
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  math actually supports the marketing. Welcome
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  to the Deep Dive. Today we are going to unpack
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  the launch claims of Grok 4 .7. We'll dissect
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  the hard -coding benchmarks, reveal a pretty
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  massive pricing advantage, and determine if this
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  is the model that should actually power your
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  daily workflows. Yeah, because every time a new
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  model drops, we get flooded with charts and graphs
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  claiming total dominance. But when you start
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  poking at the methodology, the story often changes.
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  With Grok 4 .7, XAI is making some very specific,
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  really aggressive claims about how it handles
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  complex reasoning, particularly for developers.
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  Let's pull apart those launch claims first. Because
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  understanding the architecture kind of starts
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  with understanding the intent. They didn't build
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  Grok 4 .7 to just be a conversational chatbot,
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  did they? Not at all. I mean, XAI has positioned
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  this as their ultimate digital worker. They are
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  heavily emphasizing coding and deep knowledge
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  work. Right. The standout claim... that really
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  made the machine learning community pause is
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  that Grok 4 .7 possesses this distinct internal
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  mechanism to check its own work before it ever
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  outputs a single character to the user. That's
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  a fascinating concept because, I mean, I still
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  wrestle with prompt drift myself. Well, we all
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  do. I'll give an AI a complex, multi -layered
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  research task, and it starts out incredibly strong.
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  But halfway through its response, it completely
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  forgets the original parameters I set and just
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  wanders off into generic territory. It's the
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  universal frustration of using these tools. We
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  have to remember that large language models,
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  at their core, they're just probabilistic prediction
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  engines. They're guessing the next most likely
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  token, just the next fragment of a word. So if
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  they make a slightly suboptimal guess early in
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  a long response, that error compounds. By the
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  time it finishes generating code or an essay,
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  the logic is completely unraveled. that errors
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  prompt drift. So how does Grok 4 .7 theoretically
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  solve that? Are they just forcing it to read
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  its own output and start over? Essentially yes,
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  but it's happening in a latent space we never
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  actually see. It runs an internal verification
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  loop, it drafts a potential solution, and then
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  a secondary distinct process acts as an adversarial
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  critic. This critic compares the drafted solution
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  against your original prompt. Wow. Yes, so if
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  it detects a logical flaw or a syntax error in
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  the code, it discards that draft and forces the
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  model to generate a totally new path. It does
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  all this heavy lifting in the background. I have
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  to ask, though, from a user experience standpoint,
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  is checking its own work just marketing speak
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  for longer processing times? Like, are we just
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  waiting longer for the same output? Well, mathematically
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  at a trade -off, yes, you might wait an extra
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  few seconds for the initial token to appear.
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  Right. But it's not just spinning its wheels.
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  By investing compute power into that internal
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  critique phase, you save the 10 minutes you would
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  have spent debugging the flawed code it otherwise
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  would have spat out. It basically shifts the
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  burden of verification from the human back to
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  the machine. So it essentially grades its own
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  homework before handing it in. Precisely. And
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  that grading system is put to the absolute limit
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  when we look at how it performs in structured
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  software engineering tests. I want to dig into
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  those numbers because if it really checks its
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  own work, that should be glaringly obvious in
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  the coding benchmarks. Let's look at Deep SWE
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  version 1 .1 first. This isn't just asking an
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  AI to write a simple Python script, is it? No,
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  no. Deep SWE is a brutal gauge of real -world
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  software engineering capability. We're moving
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  way past the era where AI just solves isolated
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  coding puzzles. DeepSWE tasks the model with
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  opening an entire complex code base, sometimes
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  with hundreds of interlocking files. finding
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  a specific bug reported by user, writing the
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  patch, and ensuring it doesn't break anything
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  else. It requires a really deep structural understanding
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  of how software architecture breathes. And the
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  margins here are incredibly tight. Grok 4 .7
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  hits a 71 .0 % success rate on these autonomous
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  tasks. When you stack that against the reigning
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  champions, GPT 5 .6, Sol Max score 72 .7%, and
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  Claude Fabel 5 .1 Max comes in at 70 .0%. Let's
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  just pause and look at cluster. Yeah. That is
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  less than a 3 % variance across the top three
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  models in the world. Right. Grok 4 .7 is outperforming
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  CloudFable in this specific metric. A 71 % success
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  rate on repository -level bug fixing means you
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  have a genuinely reliable senior developer sitting
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  inside your machine. But a 3 % difference between
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  Grok and GPT 5 .6, does a developer actually
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  feel that in their daily workflow? In a single
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  isolated task? Probably not. I mean, you might
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  just get lucky or unlucky on a specific prompt.
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  But aggregated over thousands of engineering
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  hours across an entire enterprise. It adds up.
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  Exactly. That gap represents a few dozen incredibly
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  stubborn bugs that GBT might catch autonomously
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  while Grok requires human intervention. Still,
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  placing in the 70 -plus percent tier proves Grok
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  is no longer just a vanity project. It's a frontier
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  level. But Deep SWE evaluates localized problem
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  -solving. It asks testing if the model can navigate
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  a specific bug. What happens when the timeline
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  stretches? Because cursorbench 4 .0 seems to
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  measure something entirely different. Cursorbench
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  is the stamina test. It evaluates how a model
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  handles long -running, sustained coding workflows
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  where context is constantly expanding. Think
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  about it. Every time you paste a new error log
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  or ask the AI to refactor a massive function,
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  you are filling up its context window. It's like
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  stacking Lego blocks of data. You have to remember
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  exactly where that very first red block went,
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  even after you have placed 10 ,000 more blocks
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  on top of it. That is a phenomenal way to visualize
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  context window degradation. Most models start
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  hallucinating once the tower gets too tall. they
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  forget the foundational instructions. Right.
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  In the cursor bench of S -Hi mode, which specifically
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  tests these massive memory heavy workflows, Grok
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  4 .7 scores 46 .3%. And here the hierarchy shifts.
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  It beats GPT 5 .6, which only hits 41 .7%, but
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  it gets noticeably eclipsed by Fable 5 .1 max,
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  which pulls a 51 .8%. Yeah, Claude Fable's architecture
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  has almost freakish ability to maintain attention
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  across vast amounts of text. When you are writing
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  a novel or architecting an entire application
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  from scratch in one session, Fable just doesn't
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  lose the plot. We also have data on professional
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  knowledge tasks which seem to echo this sort
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  of middle -of -the -pack positioning. They test
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  complex document analysis and workplace scenarios
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  using an ELO rating. Just to clarify, an ELO
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  rating is essentially a ranking system where
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  models steal points from each other by winning
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  direct head -to -head matchups much like chess
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  rankings. Correct. It's a really great way to
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  measure qualitative performance rather than just
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  binary pass -fail test. tests. On the GDPVL test,
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  Grok gets 1695 ELO, sitting just behind Fable
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  at 1735. On the AA briefcase test, it's seen
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  as a similar margin. Grok at 1657, Fable at 1678.
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  They're functionally operating the same intellectual
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  weight class. I'm trying to reconcile these different
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  performance metrics, though. Why does Grok beat
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  Fable in deep SWE but lose to it in cursor bench?
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  What's the mechanical difference? It really comes
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  down to how they handle working memory versus
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  deep logic. DeepSW relies heavily on that internal
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  checking its work mechanism we talked about.
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  Right, the verification loop. Exactly. Croc is
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  highly optimized to run local, contained logic
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  loops until it finds the perfect solution for
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  a specific file. Cursorbench, however, doesn't
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  let you just focus on one file. It demands the
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  model hold the entire sprawling project in its
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  head simultaneously. Fable just has a wider,
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  more stable attention mechanism for massive context.
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  Right, different tests prioritize different styles
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  of problem -solving stamina. Yes, and understanding
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  that limitation in grossed stamina actually acts
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  as a perfect map to its biggest vulnerability.
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  Because if it struggles to hold massive context
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  over long periods, I imagine it really falls
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  apart when the task isn't just long, but messy
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  and chaotic. Which brings us to Terminal Bench
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  4 .0. Yeah, this is where the honey badger meets
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  a brick wall. Let me set the stage here, just
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  so we are all on the same page. A text -based
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  screen where developers type direct commands
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  to computers. Yeah, no graphical interface, no
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  buttons to click, just a blinking cursor, raw
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  code, and system commands. Right. It is a totally
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  unforgiving environment. Terminal Bench 4 .0
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  doesn't just ask the AI to write code, it drops
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  the AI into that text -based screen and asks
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  us to achieve a goal autonomously. So it has
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  to act like a real user. Yes. The AI has to write
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  a command. It executes that command. It reads
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  the raw, often cryptic error message the system
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  spits back. Oh, wow. Yeah. And then it has to
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  hypothesize why it failed, write a new command
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  to test that hypothesis, and repeat that cycle
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  until the system works. It's a complex, multi
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  -step trial and error workflow. And the benchmark
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  results here are almost shocking compared to
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  how close everything else has been. ClaudeFable
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  5 .1 Max scores a massive 57 .9%. It completely
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  dominates. Really does. Meanwhile, Grok 4 .7
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  falls all the way down to 38 .0%, just barely
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  inching past GPT 5 .6 at 37 .3%. That is a nearly
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  20 point deficit. It's glaring limitation. Grok's
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  internal verification loop. That system that
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  works so incredibly well for writing a clean
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  block of code in DeepSysWE, it just seems to
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  buckle when it has to constantly pivot based
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  on external feedback. Interesting. When Fable
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  hits an unexpected error in the terminal, it
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  takes a step back. reads the entire context of
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  what is tried so far and creatively pivots. When
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  Grok hits an error, it tends to get tunnel vision.
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  Meaning it just tries the same broken command
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  over and over again. Exactly. It enters an error
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  loop. It gets stuck trying to brute force a solution
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  that fundamentally doesn't work because it's
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  losing the broader context of the terminal history.
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  It lacks that dynamic fluid reasoning required
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  to navigate a messy live environment autonomously.
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  Does a 38 % score make Grok essentially useless
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  for hardcore backend developers who live in the
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  terminal? Useless is too strong a word. It just
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  defines a boundary of trust. I mean, you assign
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  Grok a terminal task, go grab a coffee, and expect
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  the server architecture to be fully deployed
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  when you get back. Right. It requires human supervision.
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  You have to be the one who steps in, breaks the
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  air loop, and nudges it in the right direction.
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  Got it. So it's a co -pilot, not a complete replacement
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  yet. That is the crucial distinction. If you
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  want a fully economist agent roaming your back
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  end, Fable is the only real choice right now.
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  But if you want an assistant to write the individual
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  scripts while you manage the deployment, Grok
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  is still incredibly potent. Before we move on
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  to how all this impacts the actual market, we
• 11:14
  are going to take a brief pause for a word from
• 11:16
  our sponsors. Stick around. And we are back.
• 11:20
  So if Grok fails at complex autonomous pivoting,
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  and it doesn't have the massive context retention
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  of Claude Fabel, why is the developer community
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  so obsessed with it right now? Because the adoption
• 11:31
  rate is staggering. Because we've been ignoring
• 11:34
  the single most important variable in software
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  engineering. Right. Cavability only matters if
• 11:38
  you can actually afford to use it. The economics.
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  The economics. XAI didn't just build a model,
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  they built a pricing strategy designed to completely
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  upend the market. Let's break these numbers down
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  because they are jarring. AI models charge based
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  on tokens, which are essentially fragments of
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  words the AI reads and writes. Grok 4 .7 is priced
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  at $2 per million input tokens, what you send
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  it, and $6 per million output tokens, what it
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  generates. Now hold those numbers in your head
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  and let's look at what the competition is charging
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  for similar performance. GPT 5 .6. SolMax charges
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  $4 per million dollars on the input and $20 per
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  million dollars on the output. Right. And then
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  we have ClaudeFable 5 .1 Max, the model that
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  dominated the terminal benchmarks. Fable charges
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  $10 per million dollar input tokens and a staggering
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  $50 per million dollar output tokens. So when
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  you are generating code, which is incredibly
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  token dense, you are paying $50 for Fable versus
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  $6 for Grok. Wait. $50 versus $6? Whoa. Imagine
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  scaling to a billion queries. If I'm running
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  millions of automated code checks a month, Fable
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  would bankrupt my startup before I even launch.
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  That is the exact calculation happening in boardrooms
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  right now. If you are a solo developer writing
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  a single Python script on a Sunday afternoon,
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  the price difference doesn't matter. Sure. Pay
• 12:57
  the premium for Fable. But if you are building
• 12:59
  an enterprise application that calls the AI 10
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  times a second, Grok 4 .7 suddenly becomes the
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  only viable option. XAI has achieved 95 % of
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  Fable's capability for roughly 12 % of the cost.
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  That dramatically alters how people actually
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  use the technology in the real world. We aren't
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  just talking about theory anymore. We're seeing
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  it in real -world application data already. Because
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  the token cost is so negligible, developers are
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  changing their relationship with the AI. They
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  aren't just using it to answer isolated questions.
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  They're using it as a continuous, always -on
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  development partner. I saw a fascinating case
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  study where a user built a fully playable complex
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  game from scratch using Grok. They didn't write
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  a single line of code themselves. It's amazing.
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  They just continually conversed with the model,
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  having it write the core mechanics, then asking
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  it to refine the UI, then having it debug the
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  physics engine. And that kind of iterative back
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  and forth conversation requires millions and
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  millions of output tokens. With Fable, that weekend
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  project would have cost hundreds of dollars.
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  Right. With Grok, it probably costs less than
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  a cup of coffee. It completely removes the friction
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  from rapid prototyping. You don't have to spend
• 14:12
  a week architecting a perfect plan. You just
• 14:15
  start talking to the AI and you watch a tangible
• 14:18
  product materialize in real time. It lowers the
• 14:21
  barrier to entry for software creation to basically
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  zero. You can test a hypothesis, see it fail,
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  rewrite it, and test it again without constantly
• 14:31
  monitoring an API billing dashboard. But I wonder
• 14:33
  about the downside of that. Does this massive
• 14:35
  drop in price mean startups will just brute force
• 14:38
  test terrible ideas? Are we just going to see
• 14:40
  a flood of spaghetti being thrown at the wall
• 14:42
  because it's cheap to do so? Probably yes. OK.
• 14:45
  But if you study the history of innovation, throwing
• 14:47
  spaghetti at the wall is actually how breakthroughs
• 14:49
  happen. The problem has always been that throwing
• 14:51
  spaghetti used to be incredibly expensive. You
• 14:53
  had to hire a team of engineers years to build
• 14:56
  a prototype just to find out the market didn't
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  want it. Yeah. When the cost of executing an
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  idea drops to near zero, you can afford to test
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  a hundred bad ideas to find the one brilliant
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  unconventional concept that actually scales.
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  You want to lower the cost of being wrong. Yeah.
• 15:12
  Cheap failure actually accelerates finding the
• 15:15
  ideas that truly work. That is the real disruption
• 15:17
  here. It's not just a cheaper AI. It fundamentally
• 15:21
  changes the economics of human creativity. That
• 15:24
  feels like the perfect place to synthesize all
• 15:26
  of this. We started by asking if Grok 4 .7 numbers
• 15:30
  back up the hype of being a honey badger in the
• 15:33
  AI race. And the answer is nuanced. I mean, if
• 15:36
  you were looking strictly at which model has
• 15:38
  the absolute highest peak capability in complex,
• 15:40
  messy environments, no. Grok does not be Claude
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  Fable. Right. It struggles with stamina over
• 15:46
  massive contexts and it gets lost in terminal
• 15:48
  error loops. But the big idea here, the reason
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  Grok 4 .7 matters isn't about winning every single
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  benchmark. It's about breaking the cost barrier.
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  XAI has delivered a highly capable frontier -level
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  coding partner that operates at a fraction of
• 16:03
  the industry standard price. It proves that we
• 16:05
  are entering a totally new phase, the AI race.
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  It's no longer just a desperate scramble for
• 16:11
  pure raw intelligence. It is a race for economic
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  viability. Grok 4 .7 proves that top -tier performance
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  doesn't have to be a luxury good. It shifts the
• 16:22
  bottleneck of innovation entirely. Think about
• 16:24
  the implications of that for a second. If the
• 16:26
  cost of generating high -level code, debugging
• 16:29
  complex systems, and executing software engineering
• 16:31
  drops down to near zero with models like Grok,
• 16:34
  what happens to the value of human ideas? It's
• 16:36
  a crazy shift. When the execution of a project
• 16:38
  is virtually free, does our unique human creativity
• 16:42
  become the only currency that actually matters?
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  That is a profound - question to walk away with.
• 16:47
  When everyone has a free engineering team in
• 16:49
  their pocket, the only differentiator left is
• 16:51
  your imagination. Thank you for taking this deep
• 16:53
  dive with us today. We'll be back next time as
• 16:55
  we continue to track the frontier. Keep questioning
• 16:58
  and keep exploring.