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GPT 6.1 Sol: Near-Astra intelligence for a fifth of the price

1066 points · 953 comments · crorella

  1. gradus_ad · · focus · HN ↗
    Ominous for the industry and investors that token price is becoming the main battleground. Could be Anthropic's rationale for IPOing this year.
    1. mixdup · · focus · HN ↗
      Another piece of evidence on the pile that the sudden panic and desire to "slow down" is because they're hitting the plateau on capability

      Which, honestly, is fine. A lot of juice to squeeze in efficiency and even if models got zero more capable, making the capability that is already here cheaper is a huge win for everyone (except Nvidia)

      1. sebzim4500 · · focus · HN ↗
        Is there anything that could happen that you wouldn't use as evidence that they are hitting a plateau?

        It just seems like these claims are constant and looking back the calls of 'plateau' between 2023 and 2025 were clearly false, why should we think it's different now?

        1. djdjdkdkfk · · focus · HN ↗
          I am a lawyer not a coder, but for me the new models make the exact dumb mistakes they did in 2023. Everytime I come here I feel I'm in an alternate reality.
          1. rspeele · · focus · HN ↗
            Both the rate of improvement and the actual effectiveness of LLMs is much higher in coding than in other fields. An LLM+coding harness can try something wrong and get rapidly, harmlessly caught by a compiler or by a unit test. Then it usually gets it right on the 2nd or 3rd attempt. This all happens before the user who requested the change is told it's done. And a lot of it goes right back into the training dataset for the next model release.

            In law if a model makes a mistake it pretty much takes a human to catch it, which is a vastly slower, riskier, and more frustrating feedback loop. So it doesn't surprise me they can stay dumb in that field while getting massively more capable in coding with multiple "step change" releases in the past 365 days.

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