Introducing GPT-6.1 Sol

We’re introducing GPT-6.1 Sol , an upgrade to GPT-6 Sol that nearly matches GPT-6 Astra’s intelligence on agentic coding, computer use, and professional work at one-fifth of Astra’s standard input and output token prices. Cached input costs just $0.10 per million tokens —95% less than standard input pricing and 50% less than GPT-6 Sol’s cached input pricing—giving developers more room to build and run capable agents that reuse context across requests.

GPT-6.1 Sol offers a new balance of capability and cost for important everyday work. It delivers substantial improvements over GPT-6 Sol across complex professional tasks, from writing and debugging code to understanding documents and executing multi-step business workflows. On several of these evaluations, it approaches GPT-6 Astra’s performance at substantially lower cost.

On DeepSWE v1.1 , which evaluates complex software-engineering tasks in real codebases, GPT-6.1 Sol matches GPT-6 Astra at roughly one-fifth of the cost, while eclipsing GPT-6 Sol’s best score by 6.4 percentage points at a lower reasoning effort and cost.

GPT-6.1 Sol is available starting today to all Plus, Pro, Business, Enterprise, and Edu users in ChatGPT Work and Codex. GPT-6.1 Sol is not yet available in Chat. Developers can also access it through the OpenAI API as gpt-6.1-sol. Its standard API prices are $2 per million input tokens, $0.10 per million cached input tokens, and $10 per million output tokens. In the coming days, we’ll also offer GPT-6.1 Sol Ultrafast ⁠ , with up to 8x faster token generation compared to its standard speed in Codex.

On GDP.pdf , which measures how accurately models answer professional questions using complex PDF documents, including tables, charts, diagrams, and fine-print details, GPT-6.1 Sol scores higher than Opus 5.5 with fallbacks at less than half the cost per task across the tested reasoning settings. It also approaches GPT-6 Astra’s state-of-the-art performance at roughly one-fifth the cost per task.

On AutomationBench , which measures whether agents correctly complete multi-step business workflows, GPT-6.1 Sol scores 2.2 percentage points above Opus 5.5 at medium reasoning effort, at roughly a third of the cost. That score is also up 4.8 percentage points from GPT-6 Sol at the same setting.

GPT-6.1 Sol also makes substantial progress on tasks that require interacting with computer applications. On OSWorld 2.0 ’s offline set, which evaluates agents on demanding computer-use workflows, GPT-6.1 Sol outperforms GPT-6 Sol by seven percentage points at maximum reasoning effort at less than half the cost. It comes within 2.1 percentage points of Astra’s score at maximum reasoning effort at roughly one-seventh the cost per task.