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GPT-6.1 Sol vs Astra: Better Value Is Not the Same as Better Everywhere

GPT-6.1 Sol approaches Astra on OpenAI's coding and computer-use evaluations at a fraction of the API price, but benchmark scope and early user reports complicate the idea of an outright win.

2026.09.30 3 min read Reviewed 2026.09.30

Primary source OpenAI ↗ Published 2026.09.29

A cheaper model can match a flagship on a coding test and still lose on your hardest job. That is the interesting tension in OpenAI’s September 29 announcement of GPT-6.1 Sol: it moves close to GPT-6 Astra on several company-reported evaluations, while the standard API input and output token prices are one-fifth as high. Does that make Sol better? It depends on what you are trying to finish.

Where Sol closes the gap

OpenAI says Sol matches Astra on DeepSWE v1.1, a benchmark of long-running software-engineering tasks, at roughly one-fifth the cost per task. This is a vendor-reported agent benchmark, not an independent verdict on all coding work. On the OSWorld 2.0 v2026.08.08 offline set, Sol at maximum reasoning effort comes within 2.1 percentage points of Astra at roughly one-seventh the cost per task. That OSWorld result uses a partial reward metric, not a production success rate.

There is also a clear boundary to the headline. On Terminal-Bench Science 0.1, OpenAI says Astra at maximum reasoning effort has the highest score among the tested models, at 68.1%. Sol costs less per task in that comparison, but OpenAI still recommends Astra for the hardest scientific research tasks. “Better” here can mean more successful, cheaper, or a better balance of the two; those are different claims.

The bill is more than the sticker price

The Sol and Astra API pages list standard rates of $2/$10 versus $10/$50 per million input/output tokens. Cached input is $0.10 for Sol versus $1 for Astra. These are token rates, not guarantees of one-fifth the bill for a finished task: reasoning effort, retries, tool calls, and output length matter. Both model pages also say that prompts above 272,000 input tokens trigger higher rates for the full request, not just the excess tokens.

OpenAI lists Sol for ChatGPT Work, Codex, and the API, but says it is not yet in Chat. At launch, the company said a Sol Ultrafast option with up to eight-times-faster token generation in Codex would arrive in the coming days; that was a rollout promise, not a measured speed advantage for standard Sol. The endpoint and mode matter when comparing someone’s experience with an API price chart.

The launch post meets the user reports

In his X post, Tibo calls Sol an “absolute workhorse” with near-Astra intelligence at one-fifth of Astra’s price. He says Ultrafast is already available for Astra and coming soon for Sol. It is a useful statement of the product pitch, not an independent test.

One Reddit poster reports much lighter subscription-quota use with GPT-6.1 Sol than with GPT-6 Sol on what they describe as a similar workflow; that is not an Astra comparison. Another Plus user says planning and review no longer exhaust their allowance as quickly. But a separate thread reports slow actions and lengthy compactions. These are self-reports with different settings and tasks, not a controlled head-to-head. A low API token price cannot settle the subscription-quota or wall-clock question.

Choose by accepted work, not the model name

  • Try Sol first on representative coding and computer-use tasks where cost or quota is the constraint; keep Astra as a fallback when the result does not pass review.
  • Compare the same tasks, tools, context, and reasoning settings. Record accepted changes, retries, manual corrections, elapsed time, and total API spend or subscription usage.
  • Treat DeepSWE and the OSWorld offline partial reward as scoped, vendor-reported evidence; test your own failures before generalizing them.
  • Check availability and speed mode separately. A future Ultrafast option does not make standard Sol fast today.

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