GPT-6.1 Sol review evidence points to a capable coding model whose practical appeal depends on the effort setting and the time a developer spends waiting. Independent benchmark gains are substantial; published practitioner experience is less uniformly enthusiastic.
Research review, checked October 6, 2026. This article compares published evaluations and an attributed practitioner report. The Model Press has not run its own hands-on benchmark.
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What OpenAI changed
OpenAI’s release announcement positions Sol as a model for coding, computer use and professional work. Its coding evaluation places the update ahead of GPT-6 Sol and close to Astra. Those are OpenAI’s measurements, rather than an independent replication.
The professional-work emphasis matters: generating a plausible function is a narrower problem than navigating a repository, running tools and completing a document or spreadsheet. A strong score on one task family does not settle the others. The model record keeps release information separate from this assessment.
Coding improves, but maximum effort is not the obvious default
Artificial Analysis’s September 29 evaluation reports an Intelligence Index score of 52 at maximum effort, against 53 for Astra and four points above GPT-6 Sol. Its coding-agent results favor xhigh over max: xhigh finishes one point ahead of Astra, while max finishes two points behind.
That reversal is useful evidence against choosing a setting by its name. More deliberation can change a model’s approach without improving the final patch. A team testing Sol should preserve the harness and task set while varying effort.
Artificial Analysis also reports approximately 10–30% more output tokens than GPT-6 Sol, depending on effort. Improved task completion and increased generation can coexist. Neither the aggregate score nor token volume alone tells a developer how quickly a particular change will be finished.
A developer’s slower experience
In an October 3 X post, developer Pankaj Kumar describes Sol as good for most tasks but says his 3D work needed two or three iterations. He preferred Opus and Sonnet for that work and reported Sol generation around 20–30 tokens per second in his setup.
This is one person’s account. The post does not establish a controlled comparison of providers, load, prompts or effort settings. It is useful as a concrete workflow observation, not as a general speed ranking.
Artificial Analysis’s release measurements, checked October 6, list 58.3 output tokens per second for max. That measures generation in its test environment. An interactive agent also spends time on reasoning, tool calls, retries and repository operations, so the figures are not directly interchangeable.
The evaluation that would settle adoption
Sol deserves a trial on repository tasks with known outcomes: a bug fix with a regression test, a change spanning several files and a tool-dependent task. Compare xhigh and max, recording successful completion, elapsed time, unnecessary edits and human correction.
The published evidence supports taking the coding improvement seriously. It does not establish that Sol will feel faster in every development loop, or that the highest effort setting will produce the strongest result. For interactive work, both outcomes need measurement.
Evidence limits: Reddit discussions appeared in discovery, but their original contents could not be inspected and are excluded. The practitioner example above is not a community survey.
