What is GPT-6.1 Sol?
GPT-6.1 Sol is OpenAI's upgraded Sol model for developers and professionals who need advanced reasoning and agentic capabilities without the higher cost of GPT-6 Astra. It is designed for coding, professional knowledge work, computer use, scientific research, factual question answering, and multi-step business workflows. OpenAI describes GPT-6.1 Sol as approaching GPT-6 Astra's intelligence across several important workloads while charging one-fifth of Astra's standard input and output token prices. On DeepSWE v1.1, which evaluates long-horizon software engineering in real codebases, GPT-6.1 Sol matches GPT-6 Astra at approximately one-fifth of the cost and exceeds GPT-6 Sol's best score by 6.4 percentage points. Its professional-work capabilities include understanding complex PDFs containing tables, charts, diagrams, and fine-print details across fields such as finance, healthcare, and legal work. On AutomationBench, GPT-6.1 Sol improves on GPT-6 Sol by 4.8 percentage points at the same reasoning setting and scores 2.2 points above Opus 5.5 at medium reasoning effort. Computer-use performance also advances significantly, with GPT-6.1 Sol outperforming GPT-6 Sol by seven percentage points on the OSWorld 2.0 offline set at maximum reasoning effort and coming within 2.1 points of GPT-6 Astra. For scientific research, the model can work with code and terminal tools on workflows involving data analysis, simulations, model fitting, and theorem proving, more than doubling GPT-6 Sol's Terminal-Bench Science 0.1 score at maximum effort. OpenAI also reports improved factual accuracy, including a reduction in the factual-error rate from 11.4% with GPT-6 Sol to 7.7% with GPT-6.1 Sol at low reasoning effort on its deliberately difficult factuality evaluation.
Pricing
Output: $10 per 1 million tokens
Cached Input: $0.10 per 1 million cached input tokens
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GPT-6.1 Sol Categories and Features
GPT-6.1 Sol Customer Reviews
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Would you Recommend to Others?1 2 3 4 5 6 7 8 9 10
Compares right up to Opus 5.5 and Astra
Date: Oct 01 2026SummaryOverall, GPT-6.1 Sol feels like the model I would use most often for serious work. It has the context, tools, reasoning, and agent capabilities I want, but the price is low enough that I can actually run it heavily instead of treating it like a special-occasion model.
PositiveThe biggest improvement for me is that it gets very close to Astra-level performance without Astra-level pricing. I can use it for serious coding, research, computer-use tasks, and long agent workflows without feeling like every run needs to be reserved for something mission-critical. The 1.05M-token context window is still a huge advantage. I can keep large repos, specs, logs, documentation, and a lot of prior agent work in context without constantly trimming things down. I also really like the new multi-agent support. Being able to let Sol delegate parts of a bigger task to subagents makes it much more useful for complicated projects where research, coding, testing, and analysis can happen in parallel.
NegativeThe main downside is that Astra is still the model I would reach for when I absolutely want maximum capability and cost is secondary. Sol also no longer supports the none or minimal reasoning settings, so it is less suited to truly lightweight work than Luna.
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