OpenAI Releases GPT-6.1 Sol: Near-Astra Coding and Computer Use at One-Fifth of Astra’s Token Price
OpenAI Releases GPT-6.1 Sol: Near-Astra Coding and Computer Use at One-Fifth of Astra’s Token Price
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…
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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.
Where Sol Sits in the GPT-6 Lineup
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.
Benchmark Results
All figures below are vendor-reported in OpenAI’s launch post. OpenAI states that competitor numbers came from public reports.
- Coding: On DeepSWE v1.1, GPT-6.1 Sol matches GPT-6 Astra at roughly one-fifth of the cost. It beats GPT-6 Sol’s best score by 6.4 percentage points, at a lower reasoning effort.
- Professional work: On GDP.pdf, which tests answers over complex professional PDFs, Sol beats Claude Opus 5.5 with fallbacks. It does so at less than half the cost per task. On AutomationBench 1.0.6, Sol scores 2.2 points above Opus 5.5 at medium effort. That result comes at roughly one-third the cost.
- Computer use: On the OSWorld 2.0 offline set, Sol gains 7 points over GPT-6 Sol at maximum effort. It lands within 2.1 points of Astra at roughly one-seventh the cost per task.
- Science: On Terminal-Bench Science 0.1, Sol more than doubles GPT-6 Sol’s score at max effort. Average cost per task is $5.47, versus $23.21 for Opus 5.5 and $23.80 for Astra. Astra still leads at 68.1%, and OpenAI recommends it for the hardest research.
- Factuality: At low effort, the share of responses with a factual error falls from 11.4% to 7.7%. That is a reduction of about 32%. The eval uses deliberately difficult conversations where users had flagged earlier model errors.
API Details Developers Need
From the GPT-6.1 Sol model page:
- Context window of 1,050,000 tokens, 128,000 max output tokens, and an April 30, 2026 knowledge cutoff.
- Text and image input; text output.
reasoning.effortaccepts low, medium (default), high, xhigh, and max. The none and minimal settings are not supported.- Use the Responses API for tool calling. Chat Completions works without tool calling.
- Prompts above 272K input tokens cost 2x input and cache rates, and 1.5x output, for the full request.
- Batch and Flex are 50% cheaper. Fast mode costs 2x standard.
- US and EU data residency are supported. Fast mode is unavailable with EU residency.
- Fine-tuning is not supported.
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.
Interactive Explainer
How GPT-6.1 Sol Compares
| Feature | GPT-6.1 Sol | Claude Opus 5.5 | Claude Sonnet 5.5 | Gemini 3.1 Pro Preview |
|---|---|---|---|---|
| Developer / API ID | OpenAI / gpt-6.1-sol | Anthropic / claude-opus-5-5 | Anthropic / claude-sonnet-5-5 | Google / gemini-3.1-pro-preview |
| Status | Generally available | Generally available | Generally available | Preview |
| Input price (per 1M) | $2.00 | $4.00 | $2.00 | $2.00 (≤200K prompt) |
| Output price (per 1M) | $10.00 | $20.00 | $10.00 | $12.00 (≤200K prompt) |
| Cached input read (per 1M) | $0.10 | $0.20 | $0.20 | $0.20 + $4.50/1M tokens/hr storage |
| Long-prompt surcharge | >272K input: 2x input and cache, 1.5x output | None; 1M at standard rates | None; 1M at standard rates | >200K: $4 input, $18 output |
| Context window | 1,050,000 | 1M | 1M | 1,048,576 |
| Max output tokens | 128,000 | 128K | 128K | 65,536 |
| Input modalities | Text, image | Text, image | Text, image | Text, image, video, audio, PDF |
| Reasoning control | low to max (5 levels), default medium | Adaptive thinking, always on; default medium | Adaptive thinking; default high | Thinking supported |
| Batch pricing (in / out) | 50% off standard | $2 / $10 | $1 / $5 | $1 / $6 |
| Knowledge cutoff | Apr 30, 2026 | Jun 2026 | Jun 2026 | Not listed |
| Open weights | No | No | No | No |
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.
Key Takeaways
- GPT-6.1 Sol matches GPT-6 Astra on DeepSWE v1.1 at about one-fifth the cost.
- Pricing is $2 input, $10 output, and $0.10 cached input per 1M tokens.
- It beats Claude Opus 5.5 on AutomationBench by 2.2 points at about one-third the cost.
- It offers a 1,050,000-token context, 128K output, and 5 reasoning effort levels.
- It is API-only and closed-weight; Astra still leads the hardest science tasks.
FAQ
- What is GPT-6.1 Sol? It is OpenAI’s mid-tier GPT-6 model, released September 29, 2026, for coding, computer use, and professional work.
- How much does GPT-6.1 Sol cost? Standard API pricing is $2 per million input tokens, $0.10 cached input, and $10 output.
- Can I self-host GPT-6.1 Sol? No. It is available only through the OpenAI API, ChatGPT Work, and Codex.
Michal Sutter
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.