GPT-6.1 Sol 正式发布
原题Introducing GPT-6.1 Sol
推荐理由OpenAI 在 GPT-6 Sol 发布仅七天后推出 GPT-6.1 Sol:以 Astra 五分之一的价格(输入 2 美元、输出 10 美元、缓存输入低至 0.10 美元每百万 token)逼近其智能体编码、电脑操作与专业工作表现。DeepSWE v1.1 上追平 Astra,GDP.pdf 专业文档任务以不到一半成本胜过带回退的 Opus 5.5。前沿能力进入日用价,是个人智能体全天候随行的经济前提。适合做模型选型与智能体成本预算的开发者和架构师。
读前速览
OpenAI 推出 GPT-6.1 Sol,这是一款高效模型,在编码、专业工作和计算机操作方面提供接近 GPT-6 Astra 的智能水平,而成本仅为后者的五分之一。
大幅降低成本,性能接近旗舰级
GPT-6.1 Sol 为智能体任务提供接近 GPT-6 Astra 的智能水平,但输入/输出成本仅为后者的五分之一,使得高阶智能更易于规模化应用。
通过激进缓存策略优化智能体工作流
其缓存输入成本低至每百万 Token 0.10 美元(比标准输入价格低 95%),专为频繁复用大型上下文的智能体设计。
在多个专业基准测试中表现强劲
该模型在软件工程(DeepSWE)、复杂 PDF 分析(GDP.pdf)和科学研究(Terminal-Bench Science)等领域取得显著进步,往往以极低成本超越 Opus 5.5 等竞争对手。
增强事实准确性与安全对齐
在低推理强度下,该模型将事实错误率降低了约 32%,并在遵守安全约束和披露工具故障方面表现出更高的可靠性。
完整摘要
OpenAI 发布了 GPT-6.1 Sol,这是 Sol 模型的升级版,旨在平衡高性能与显著降低的成本。它在智能体编码(DeepSWE v1.1)、专业文档分析(GDP.pdf)、业务工作流自动化(AutomationBench)和计算机使用(OSWorld 2.0)方面的表现几乎媲美旗舰模型 GPT-6 Astra,但价格仅为 Astra 标准 Token 定价的 20%。值得注意的是,该模型采用了极具竞争力的缓存输入价格,每百万 Token 仅需 0.10 美元,以支持上下文密集型智能体。此外,相较于 GPT-6 Sol,新模型在事实准确性和安全对齐方面均有提升,特别是在工具故障透明度和遵守用户约束条件方面表现更佳。
目录 · 8 节
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.
A more capable Sol across tasks
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.
Coding
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.
Professional work
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.
Computer use
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.
Scientific research
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.
Factuality
GPT‑6.1 Sol also improves factual accuracy on difficult prompts. Its largest factuality improvement over GPT‑6 Sol comes at low reasoning effort, where it reduces the share of responses containing a factual error from 11.4% to 7.7%—a reduction of approximately 32%. Across the tested reasoning settings, its error rate remains within 1.9 percentage points of GPT‑6 Astra’s, at less than one-fifth the cost per task.
This evaluation measures the share of answers containing at least one factual error on de-identified conversations where users flagged an earlier model’s error. These deliberately difficult prompts are not representative of typical usage.
We evaluate factuality on de-identified ChatGPT conversations where users had flagged a factual error from a prior model. These error-inducing conversations are not representative of typical usage, where factual errors are more rare.
Deploying GPT‑6.1 Sol safely
GPT‑6.1 Sol shows substantial improvements over GPT‑6 Sol in our alignment evaluations, bringing it closer to GPT‑6 Astra.
GPT‑6.1 Sol is more transparent about its limitations and more reliable at respecting user intent and safety constraints. In challenging evaluations, it shows lower failure rates than GPT‑6 Sol on transparency about broken search tools, respecting explicit restrictions, and avoiding unauthorized outcomes during agentic tasks. We observed no attempts to bypass an automated safety reviewer, matching GPT‑6 Astra and GPT‑6 Sol. Full details can be found in the GPT‑6.1 Sol system card addendum(opens in a new window).
The evaluations below deliberately test challenging situations and do not measure failure rates in typical use.
This evaluation tests whether agents tell the user when their search tool is broken instead of giving their best guess. GPT‑6.1 Sol fails to disclose the problem in 2.1% of cases, compared with 4.9% for GPT‑6 Sol, 1.5% for GPT‑6 Astra, and 28.7% for GPT‑6 Luna. Tasks are selected to elicit failures and do not represent typical usage. Effort was set to maximum.
Pricing and availability
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.