GPT-6.1 Sol Features: Context, Tools, API Pricing and Practical Limits

Thinking of trying GPT-6.1 Sol in your project? It is built for complex coding, computer use and professional work. OpenAI positions it as a lower-cost option with capability approaching GPT-6 Astra. Before integrating it, check the context limits, tool interface and pricing for long requests.

Core specifications

Item	GPT-6.1 Sol documentation
API model ID	gpt-6.1-sol
Context window	1,050,000 tokens
Maximum input / output	922,000 / 128,000 tokens
Input → output	Text and images → text
Reasoning levels	low, medium, high, xhigh, max; default medium
Tool-calling interface	Responses API; Chat Completions supports requests without tools

Source: GPT-6.1 Sol model documentation. The context window is a total capacity; a request can contain up to 922,000 input tokens and must leave room for output.

Coding: evaluate an engineering outcome

OpenAI reports Astra-level performance at approximately one-fifth the task cost on DeepSWE v1.1. The benchmark covers complex work in real repositories; test comparable tasks in your own environment to see how the result carries over. Launch evaluation

For a real repository, establish a specific defect, a fixed checkout and repeatable tests. Review whether the patch introduces a behavioral regression. Producing code is an intermediate step; passing acceptance checks and explaining the change are closer to the result a team needs.

Record the environment as well: tool permissions, network access, installed dependencies and the time budget. Otherwise an unsuccessful run can be a failure of setup rather than a useful measurement of reasoning.

Computer use and professional documents

The documented tool set includes computer use, web and file search, code interpreter, hosted shell, apply patch, MCP and tool search. Streaming and structured outputs are supported.

An application can combine tools into a workflow that gathers evidence, performs analysis and checks a deliverable. Tool support alone does not grant access: the application must configure the tools, process their results and define the permitted actions.

Image input and image generation are different capabilities. Sol can interpret images and return text. Calling an image-generation tool delegates that work to another tool; it does not make Sol a native image, audio or video output model.

For documents and research, check numerical accuracy, citations and treatment of missing information separately from prose quality. For recent information, provide current sources or use retrieval, then verify the answer.

Choosing a reasoning level

The default is medium; none and minimal are not supported.

Try these starting points based on the difficulty of your task:

Starting level	Candidate tasks	What to measure
low	Bounded requests with simple acceptance checks	Whether latency falls while accuracy holds
medium	Routine engineering, document analysis and tool workflows	Stability of quality and cost
high and above	Difficult debugging, conflicting evidence or many constraints	Whether extra reasoning reduces rework

The highest setting is not automatically the most economical. If a lower setting already passes reliably, more reasoning may add cost without changing the result. If a failure causes expensive human rework, a higher setting can still be worthwhile. Compare the total cost per accepted task.

Official API pricing and long context

Standard rates in US dollars per million tokens:

Input length	Uncached input	Cache reads	Cache writes	Output
Up to 272K tokens	2.00	0.10	2.50	10.00
Above 272K tokens	4.00	0.20	5.00	15.00

Source: OpenAI API pricing. Crossing the threshold changes the rates for the full request, not just the excess. Fast costs twice Standard; Batch and Flex have a 50% discount, with their own processing conditions.

For a hypothetical request with 100,000 uncached input tokens and 10,000 billable output tokens, the token charge is:

0.1 × $2 + 0.01 × $10 = $0.30

With 300,000 input tokens and the same billable output, it becomes:

0.3 × $4 + 0.01 × $15 = $1.35

These are calculations, not observed bills. They exclude tools, regional processing and other additional charges. Estimate from complete billable usage, not just the final text shown to the user.

Availability and the PoYo entry

OpenAI has announced API, ChatGPT Work and Codex access. Actual access depends on the plan, client and workspace settings.

The launch announcement excludes regular Chat and describes Sol Ultrafast as forthcoming. Keep that separate from the available Standard and Fast modes.

If you plan to use Sol through PoYo, start with its GPT-6.1 Sol model page. As of September 30, 2026, the page says “coming soon.” Once access opens, check that page and the PoYo console for the actual price, endpoints and tool support. The specifications and rates above describe OpenAI’s native API.

Browse other models on PoYo’s OpenAI provider page, or see currently available chat models in the AI Chat API collection.

What to check in your own trial

• Quality on complex tasks. Compare Sol with your current model on real coding or document work, and record the results and revisions needed. Test OpenAI’s “close to Astra” positioning against your own tasks.
• Accuracy of facts and citations. Spot-check recent facts, figures and sources, especially for work you plan to share.
• Time to finish the whole task. Include reasoning, queueing, tool calls and retries, and see how different input lengths affect the result.
• Finding details in long documents. Test retrieval across sections, omissions and citation locations. Context-window size is only a starting point.

Choose a representative set of tasks and apply the same criteria to completion rate, total tokens, elapsed time and human revisions. That will give you a clearer view of whether Sol fits your workflow. For a cross-provider decision, read GPT-6.1 Sol vs Claude Opus 5.5. For the release context, see our DevDay 2026 recap.

Frequently asked questions

Is GPT-6.1 Sol another name for GPT-6 Sol?

No. It is an upgrade with the API ID gpt-6.1-sol. Record the exact model in any evaluation.

Does it generate images or video directly?

Its native output is text. Image-generation tool access and native output modalities are separate capabilities.

Can Chat Completions call its tools?

Current official documentation directs tool-calling requests to the Responses API. Chat Completions supports requests without tools.