“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,” OpenAI said in a statement.

Sharing details about the model, OpenAI said cached input would cost just $0.10 per million tokens — 95% less than its standard input pricing and 50% less than the cached input price of GPT-6 Sol.

The company said the lower cost would give developers more room to build and run capable AI agents that can reuse context across multiple requests.

“GPT-6.1 Sol offers a new balance of capability and cost for important everyday work,” OpenAI said.

According to the company, GPT-6.1 Sol delivers substantial improvements over GPT-6 Sol across complex professional tasks, including writing and debugging code, understanding documents and executing multi-step business workflows.

“On several of these evaluations, it approaches GPT-6 Astra’s performance at substantially lower cost,” OpenAI said.

On DeepSWE v1.1, which evaluates complex software-engineering tasks in real-world codebases, GPT-6.1 Sol reportedly matches GPT-6 Astra while costing roughly one-fifth as much. It also surpasses GPT-6 Sol’s best score by 6.4 percentage points while operating at a lower reasoning effort and cost.

On GDP.pdf, an evaluation measuring how accurately AI models answer professional questions using complex PDF documents — including tables, charts, diagrams and fine-print details — GPT-6.1 Sol scored higher than Opus 5.5 with fallbacks at less than half the cost per task across the tested reasoning settings, according to OpenAI.

The company said the model also approaches GPT-6 Astra’s state-of-the-art performance on the benchmark at roughly one-fifth of the cost per task.

GPT-6.1 Sol also makes substantial progress on tasks requiring AI systems to interact with computer applications.

On OSWorld 2.0’s offline set, which evaluates agents on demanding computer-use workflows, GPT-6.1 Sol outperformed GPT-6 Sol by seven percentage points at maximum reasoning effort while costing less than half as much.

At maximum reasoning effort, the model came within 2.1 percentage points of GPT-6 Astra’s score while costing roughly one-seventh as much per task, OpenAI said.