Astra can do 34 consecutive simple math operations in latent space, 4x more than Sol
A recent benchmark reveals that GPT-6 Astra can perform 34 consecutive simple math operations in latent space without chain-of-thought, significantly outperforming other models. This is four times more than Sol, which managed only 8 operations. Even Claude Opus 4.6, the second-best performer, only achieved 12 steps. This marks a substantial leap in LLM capabilities for long chains of reasoning.
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PublishedOffset at this time: UTC+0Sep 9, 2026, 20:24 UTC
IngestedOffset at this time: UTC+0Sep 9, 2026, 23:00 UTC
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After hearing rumors that GPT-6 Astra uses some form of recurrent depth, I decided to make a benchmark that specifically forces LLMs to do long chains of reasoning in latent space, without using chain-of-thought. The results for Astra are crazy: it can do 34 consecutive simple math operations (add, subtract, multiply and divide, all with small numbers) without using chain-of-thought! For comparison, Sol can only do 8!
I tested 24 other models (also from Anthropic) and none got anywhere close to Astra's result. What's even stranger is that there has been barely any progress on this benchmark (the second best result was Claude Opus 4.6, with 12 steps), until the release of Astra.
You can see the full results here, along with some speculation about what might be happening inside Astra that could explain this result:
https://maartenbaert.github.io/LatentMathBench/