OpenAI 发布 GPT-6.1 Sol：智能体编码与电脑操作接近 Astra，价格仅为五分之一
OpenAI Releases GPT-6.1 Sol: Near-Astra Coding and Computer Use at One-Fifth of Astra's Token Price - MarkTechPost
OpenAI 本周发布 GPT-6.1 Sol，定位 GPT-6 家族中端模型，在智能体编码、电脑操作和专业工作上接近 GPT-6 Astra 的表现，标准输入输出价格为 Astra 的五分之一，缓存输入降至每百万 token 0.10 美元，比 GPT-6 Sol 低 50%。该模型已在 OpenAI API 以 gpt-6.1-sol 上线，并接入 ChatGPT Work 和 Codex。OpenAI 还计划数日内在 Codex 推出 GPT-6.1 Sol Ultrafast 选项，称 token 生成速度最高可达标准速度的 8 倍。
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原文列出 GPT-6 三档定价与缓存价格变化，读者可据此判断智能体场景下的成本结构。
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This week, OpenAI released GPT-6.1 Sol . It upgrades GPT-6 Sol, the mid-tier model in the GPT-6 family. OpenAI’s claim is specific: near-Astra results on agentic coding, computer use, and professional work. The price is one-fifth of GPT-6 Astra’s standard input and output rates. Cached input drops to $0.10 per million tokens, 50% below GPT-6 Sol.
Is it deployable? Yes, as a hosted model. It is live today in the OpenAI API as gpt-6.1-sol , and in ChatGPT Work and Codex.
OpenAI now ships 3 GPT-6 tiers. GPT-6 Astra costs $10 input, $50 output, and $1 cached input per million tokens. GPT-6.1 Sol costs $2, $10, and $0.10. GPT-6 Luna costs $0.10, $0.50, and $0.01.
The cached price is quite important most for agents. Agents resend the same system prompt, tool schemas, and history on every step. Cached reads now cost 5% of the uncached input rate, down from 10% on GPT-6 Sol.
All figures below are vendor-reported in OpenAI’s launch post. OpenAI states that competitor numbers came from public reports.
From the GPT-6.1 Sol model page :
OpenAI also plans a GPT-6.1 Sol Ultrafast option in Codex within days. It promises up to 8x faster token generation than standard speed.
Standard first-party API list prices, verified September 30, 2026.
Claude Sonnet 5.5 matches Sol’s $2 and $10 list price, but Sol’s cached input costs half as much. OpenAI’s benchmarks compare Sol against Opus 5.5, not Sonnet 5.5. Gemini 3.1 Pro matches Sol on input, charges $12 for output, and remains in preview. List prices are not direct cost comparisons. Anthropic notes its newer tokenizer produces roughly 30% more tokens for the same text.
Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.