GPT-6.1 Sol Description
GPT-6.1 Sol is an upgraded OpenAI model that combines advanced intelligence with lower operating costs for coding, professional knowledge work, computer use, scientific research, and autonomous agents. OpenAI positions it as offering near-GPT-6 Astra intelligence at one-fifth of Astra's standard input and output token prices. The model delivers substantial improvements over GPT-6 Sol in software engineering, complex document understanding, business automation, and long-horizon computer-use workflows. On DeepSWE v1.1, GPT-6.1 Sol matches GPT-6 Astra at approximately one-fifth of the cost and surpasses GPT-6 Sol's highest score by 6.4 percentage points at lower reasoning effort. On AutomationBench, it scores 4.8 percentage points higher than GPT-6 Sol at the same reasoning setting and 2.2 points above Opus 5.5 at medium reasoning effort. GPT-6.1 Sol also improves computer use, coming within 2.1 percentage points of GPT-6 Astra on the OSWorld 2.0 offline set at maximum reasoning effort while costing roughly one-seventh as much per task. For scientific workflows, the model more than doubles GPT-6 Sol's Terminal-Bench Science 0.1 score at maximum effort while reducing average task cost by more than half. Factuality has also improved, with the share of responses containing a factual error at low reasoning effort falling from 11.4% with GPT-6 Sol to 7.7% with GPT-6.1 Sol on OpenAI's difficult error-focused evaluation. Developers can access GPT-6.1 Sol through the OpenAI API for $2 per million input tokens, $0.10 per million cached input tokens, and $10 per million output tokens, while eligible users can access it through ChatGPT Work and Codex.
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 Features and Options
GPT-6.1 Sol User Reviews
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Compares right up to Opus 5.5 and Astra Date: Oct 01 2026
Summary: Overall, 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.
Positive: The 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.
Negative: The 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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