OpenAI launches GPT-6.1 Sol, nearing GPT-6 Astra at a fifth of the price

Pricing and availability

Standard API prices for GPT-6.1 Sol are $2 per million input tokens and $10 per million output tokens. Cached input costs $0.10 per million tokens, which OpenAI says is 95% less than standard input pricing and 50% less than GPT-6 Sol's cached input pricing. The company says this gives developers more room to build agents that reuse context across requests.

The model is available starting today to all Plus, Pro, Business, Enterprise and Edu users in ChatGPT Work and Codex. It is not yet available in Chat. Developers can call it through the OpenAI API as gpt-6.1-sol.

In the coming days, OpenAI will also offer GPT-6.1 Sol Ultrafast, with up to 8x faster token generation compared to its standard speed in Codex.

Coding, professional work and computer use

OpenAI reports gains over GPT-6 Sol across several evaluations. All figures below come from the company.

• DeepSWE v1.1 (complex software-engineering tasks in real codebases): GPT-6.1 Sol matches GPT-6 Astra at roughly one-fifth of the cost, and beats GPT-6 Sol's best score by 6.4 percentage points at a lower reasoning effort and cost.
• GDP.pdf (professional questions over complex PDFs with tables, charts and fine print): it scores higher than Opus 5.5 with fallbacks at less than half the cost per task, and approaches Astra's performance at roughly one-fifth the cost per task.
• AutomationBench (multi-step business workflows): it scores 2.2 percentage points above Opus 5.5 at medium reasoning effort at roughly a third of the cost, and is up 4.8 percentage points from GPT-6 Sol at the same setting.
• OSWorld 2.0 offline set (computer-use workflows): it outperforms GPT-6 Sol by seven percentage points at maximum reasoning effort at less than half the cost, and comes within 2.1 percentage points of Astra at roughly one-seventh the cost per task.

OpenAI notes that the AutomationBench datapoint for Claude Fable 5.1 understates its actual cost, because it omits the cost of fallbacks, which occurred on about 40% of tasks.

Science and factuality

On Terminal-Bench Science 0.1, which covers data analysis, simulation and theorem proving, GPT-6.1 Sol more than doubles GPT-6 Sol's score at maximum reasoning effort at less than half the cost per task. At maximum effort it costs $5.47 per task on average, compared with $23.21 for Opus 5.5 and $23.80 for Astra. GPT-6 Astra still achieves the highest score among the models tested at 68.1%, and OpenAI says it should be used for the most difficult scientific research tasks.

On factuality, the largest improvement over GPT-6 Sol comes at low reasoning effort, where the share of responses containing a factual error falls from 11.4% to 7.7%, a reduction of approximately 32%. Across tested settings, the error rate stays within 1.9 percentage points of Astra's. The test uses de-identified ChatGPT conversations in which users had flagged an earlier model's error. OpenAI stresses that these deliberately difficult prompts are not representative of typical usage.

Safety and what it means

OpenAI says GPT-6.1 Sol shows substantial improvements over GPT-6 Sol in its alignment evaluations, bringing it closer to GPT-6 Astra. In challenging tests it showed lower failure rates than GPT-6 Sol on transparency about broken search tools, respecting explicit restrictions, and avoiding unauthorized outcomes during agentic tasks. The company observed no attempts to bypass an automated safety reviewer, matching GPT-6 Astra and GPT-6 Sol. Full details are in the system card addendum, and OpenAI notes the tests do not measure failure rates in typical use.

For developers and teams running agents, the pitch is a lower-cost option that, per OpenAI's benchmarks, comes close to its top model on many tasks. Astra remains the stronger choice for the hardest scientific work.