Ötödáron kínál csúcsközeli intelligenciát az új 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. Our most intelligent model for the best results. Near-Astra intelligence for a fifth of the price. Fast and efficient everyday work at scale. 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. In DeepSWE 1.1⁠⁠(opens in a new window), AI agents solve original, long-horizon software engineering tasks. 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. In GDP.pdf⁠(opens in a new window), models must answer real-world prompts about complex PDFs pulled from professional workflows in finance, healthcare, legal, and seven other professional domains. 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. In AutomationBench 1.0.6⁠⁠(opens in a new window), AI agents are tested on end-to-end workflows using 47 tools across sales, marketing, operations, support, finance, and HR. The datapoint for Claude Fable 5.1 understates its actual cost, as it omits the cost of fallbacks, which occurred on ~40% of tasks. 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. In OSWorld 2.0⁠⁠(opens in a new window), AI agents attempt long-horizon computer-use workflows spanning everyday and professional tasks. We report the partial reward on the offline set from the v2026.08.08 release. On Terminal-Bench Science 0.1, which evaluates scientific workflows including 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, GPT-6.1 Sol costs $5.47 per task on average, compared with $23.21 for Opus 5.5 and $23.80 for Astra, delivering substantial scientific capability at over 75% lower cost than either model. GPT-6 Astra still achieves the highest score among the models tested at 68.1%, and should be used for the most difficult scientific research tasks. In Terminal-Bench Science 0.1⁠(opens in a new window), agents complete scientific research workflows using code and terminal tools, including analyzing data, running simulations, and fitting models. GPT