GPT-6.1 Sol is not officially described as matching GPT-6 Astra on every task. OpenAI says Sol delivers “near-Astra performance” for complex coding, computer use, and professional work at lower cost, and recommends testing both models against your own workload. The API model, ID gpt-6.1-sol, was released on September 29, 2026.
What OpenAI means by “near-Astra”
OpenAI’s model page positions GPT-6.1 Sol as a lower-cost option for complex coding, computer use, and professional work. “Near-Astra” is a broad vendor description, not a finding that Sol and Astra produce identical results across every task. The page does not establish universal parity or provide task-by-task independent benchmark results.
As an Amazon Associate I earn from qualifying purchases.
For a real choice between the models, compare how well each completes the work you actually need done. A model that performs adequately on routine coding may behave differently on a large codebase, a multi-step computer-use workflow, or a task with strict output requirements. OpenAI explicitly recommends evaluating both models on your own tasks. OpenAI’s GPT-6.1 Sol model page gives the vendor’s positioning and API specifications.
How much cheaper is GPT-6.1 Sol?
OpenAI’s September 29, 2026 API changelog lists standard rates for prompts with up to 272K input tokens. The prices below are per million tokens:
#1 Best Overall
| Token type | GPT-6.1 Sol price |
|---|---|
| Input | $2 |
| Cached input | $0.10 |
| Cache write | $2.50 |
| Output | $10 |
For comparison, OpenAI’s published standard rates for Astra are $10 per million input tokens and $50 per million output tokens. That makes Sol’s listed standard input and output rates one-fifth of Astra’s; it does not establish equal quality for one-fifth the cost. The comparison also should not be treated as a complete bill: cached input and cache-write use have separate prices, and actual usage depends on the workload. Check the OpenAI API changelog for the September 29, 2026 release entry and current rates.
What the API specifications mean for deployment
OpenAI lists a 1,050,000-token context window and a maximum output of 128,000 tokens for GPT-6.1 Sol. Context capacity and maximum output are separate limits: the context window concerns the input and conversation the model can handle, while the output limit caps what it can generate in a response. A large context window may help with long documents or codebases, but it does not by itself prove better results or lower costs for those tasks.
The model page lists US and EU data residency support. Fast mode is unavailable with EU data residency, so teams that require EU residency should account for that limitation when configuring a deployment. Confirm the current specifications and eligibility on the model documentation before implementation; API features and availability can change.
Free tools Windows power users keep installed
One-click scans. No signup required.
How to compare Sol and Astra fairly
- Choose representative tasks. Use realistic prompts and inputs from the coding, computer-use, or professional workflows you plan to deploy, rather than relying on a generic impression of either model.
- Define success before testing. Decide what counts as a correct, complete, and usable result. For tool-using tasks, include whether the model reaches the intended outcome, not just whether its explanation sounds convincing.
- Keep the setup consistent. Use the same inputs, tool access, and relevant reasoning settings for both models. Record differences in configuration that could affect results.
- Measure cost and latency alongside quality. Include input, cached-input, cache-write, and output tokens in cost estimates where applicable. Measure response time in the configuration you expect to use; the cited model materials do not establish a universal latency advantage.
- Check limits and data requirements. Confirm that the task fits the context and output limits, and verify data-residency needs and any effect of those requirements on available modes.
- Repeat enough to judge reliability. A single successful response is not a reliable estimate of performance on variable tasks. Keep results for the same test set so model changes or configuration changes can be compared later.
What OpenAI’s safety addendum says
In its September 29, 2026 addendum, OpenAI classifies GPT-6.1 Sol as Critical capability in cybersecurity and High for biological and chemical capability. These are OpenAI’s classifications, not independent verification. The addendum also notes that evaluations conducted in a research environment or through the API may differ from production ChatGPT because prompts, available tools, and reasoning effort can vary. See the GPT-6.1 Sol safety addendum for the evaluation context.
Rank #3
Who should consider GPT-6.1 Sol?
Sol is worth evaluating when its lower listed API rates matter and your work resembles the complex coding, computer-use, or professional tasks OpenAI names. Astra remains a comparison point rather than a model Sol can be assumed to replace. The decision turns on the quality your tasks require, the full token cost, latency in your configuration, limits, and residency needs—not on the “near-Astra” phrase alone.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
