GPT-6.1 Sol gets close to Astra at a lower task cost

GPT-6.1 Sol launched on 29 September 2026, and the independent results give me a clearer reason to try it than the previous Sol release. Artificial Analysis puts it close to GPT-6 Astra on its intelligence tests, with a much lower measured cost per task. That is useful even though uncached input and output rates have not fallen. [1] [3] [4] [5]

What do the GPT-6.1 Sol benchmarks show?

Sol 6.1 makes a larger improvement over Sol 6 than Sol 6 made in the preceding release. In Artificial Analysis’ current Intelligence Index v4.3.2, the rounded score rises from 48 to 52 at maximum effort. Astra remains slightly ahead at 53. All three rows below come from the same current evaluation version. [4] [5] [6]

Sol 6.1 is close to Astra in this comparison while costing about 78% less per benchmark task. I calculated that percentage from $0.72 and $3.26. It explains why the release interests me: the cheaper model approaches the stronger model’s aggregate result without requiring the same measured spending.

The index covers ten evaluations, mainly in English and using text. Artificial Analysis weights agent work, coding, scientific reasoning and general tasks, while visual and multilingual capabilities are measured separately. A one-point gap in a rounded aggregate score does not establish that the models are interchangeable for every job. [7]

The cost measurement is also more useful than a token-price comparison alone. Artificial Analysis combines the tokens actually used with pricing and typical cache-hit rates, then weights the results across tasks. It therefore captures some of the extra work a model chooses to do. That is the distinction behind why AI benchmarks can miss practical model quality: the evaluator’s tasks still have to resemble the work you want done. [7]

Is GPT-6.1 Sol cheaper than GPT-6 Sol?

Uncached input and output prices are unchanged, but the measured task cost is lower in Artificial Analysis’ suite. OpenAI lists $2 per million uncached input tokens and $10 per million output tokens for Sol 6.1 when the input is at most 272,000 tokens. Cached reads do get cheaper: $0.10 per million tokens, half Sol 6’s cached rate. Longer inputs have a separate pricing tier. [1] [3]

For a hypothetical request with 100,000 uncached input tokens and 10,000 output tokens, those standard rates produce a $0.30 bill. That calculation is the same as for Sol 6. It says nothing about how many tokens the newer model needs to finish the request, which is where the independent cost result becomes interesting.

OpenAI’s own launch tests point in the same direction. The company reports that Sol 6.1 matches Astra on DeepSWE 1.1 at roughly one fifth of the task cost. Those are OpenAI’s results, with its evaluation setup, and its announcement says research-environment results can differ from ChatGPT. I would use that claim to choose a test, then use the independent comparison as a second check. [1]

This is a different argument from the GPT-6 Sol and Luna price cut. That release made each token cheaper. Sol 6.1 now has to demonstrate that it buys better accepted work with those tokens.

Where can you use Sol 6.1, and what has Reddit tested?

OpenAI’s launch covers ChatGPT Work, Codex and the API, with a gradual rollout on paid plans. It does not put Sol 6.1 in ordinary Chat at launch. The API model identifier is gpt-6.1-sol; the official model page lists a 1,050,000-token context window and up to 128,000 output tokens. [1] [2]

The launch-day r/ChatGPT thread is useful mainly as a record of expectations. One commenter asks whether anyone has access, while another questions the specialized benchmarks in the announcement. The opened discussion did not supply a verified firsthand comparison of Sol 6.1. I have not run a controlled comparison for this article either. [8]

That would also test whether the newer model avoids the unnecessary engineering I observed with GPT-5.6 Sol. The next useful measurement is the cost of a change I can merge, including the time I spend correcting it.