What GPT-5.1-Codex was
GPT-5.1-Codex was a coding-specialized model from OpenAI’s GPT-5.1 family. OpenAI positioned it for Codex and compatible agentic environments, where a model can inspect project files, call development tools, propose or apply changes, and iterate after seeing test results or other feedback.
That focus distinguishes it from a general conversational model. GPT-5.1-Codex was intended for practical software engineering across repositories, including feature implementation, debugging, code review, testing, migration work, and large refactors. Its purpose was not simply to suggest an isolated code snippet, but to help an engineering agent work through a task over multiple steps.
There is an important availability qualification: OpenAI deprecated gpt-5.1-codex on April 22, 2026, and API access ended on July 23, 2026. It is therefore a historical model rather than an available choice for a new production integration.
Where it fit in OpenAI’s lineup
GPT-5.1-Codex belonged to the GPT-5.1 family but was specialized for coding-agent workflows. It was available through the Responses API and was also associated with Codex-style environments. The model’s role was narrower than a general-purpose model: its design prioritized repository-level software work, tool use, and sustained task execution.
OpenAI listed GPT-5.6 Sol as the recommended replacement after GPT-5.1-Codex was retired. The supplied documentation does not provide a full feature-by-feature comparison between the two models, so GPT-5.6 Sol should be understood here as OpenAI’s stated successor recommendation, not as evidence that every capability or price was identical.
Core capabilities and technical limits
The verified context window was 400,000 tokens, while the maximum output was 128,000 tokens. A context window is the amount of conversation, instructions, source material, and tool-related information the model can consider in one request. This capacity was particularly relevant to large repositories, multi-file changes, long debugging sessions, and tasks requiring substantial project context.
- Context window: 400,000 tokens
- Maximum output: 128,000 tokens
- Model family: GPT-5.1
- Primary specialization: Agentic software engineering
- Reasoning: Reasoning-token support was documented
- Fine-tuning: Not supported
- Knowledge cutoff: September 30, 2024
The large limits did not guarantee that every task would be completed autonomously or correctly. An agent still needed suitable repository instructions, access to relevant tools, reliable tests, and appropriate safeguards before changes could be accepted. The model snapshot was also updated while it was available, meaning behavior could vary unless a supported snapshot identifier was used.
Inputs, outputs, and tool use
GPT-5.1-Codex accepted text and image input and returned text output. Image input could provide useful context for interface work, screenshots of visual bugs, diagrams, or design references. It did not natively produce images, audio, video, music, or other non-text media.
The model supported function and tool calling. In practical terms, a compatible application could expose operations such as reading files, searching a codebase, running tests, inspecting build output, or applying changes. The model could then decide when to request those operations as part of a larger workflow. Tool calling did not mean that the model independently had unrestricted access to a computer; the surrounding Codex or API application still determined which tools existed and what permissions they had.
Streaming responses were supported, allowing an application to display generated output incrementally instead of waiting for the entire response. Structured outputs were also documented, which could help applications request results in a defined schema when text alone was too ambiguous. The supplied research does not verify a separate JSON-mode capability, so structured outputs and JSON mode should not be treated as interchangeable features.
What it was good at
GPT-5.1-Codex was designed for tasks where code generation was only one part of the job. Suitable workflows included:
- Implementing a feature across several files
- Tracing and fixing bugs
- Writing or expanding automated tests
- Reviewing code and identifying likely problems
- Refactoring a large or unfamiliar repository
- Migrating code between APIs or architectural patterns
- Investigating failed builds, tests, or other development errors
- Working through longer-running Codex tasks with repeated tool calls
Its practical value came from combining coding ability with repository context and iteration. For example, an agent could inspect an existing implementation, make a proposed change, run tests through an available tool, examine the failure, and revise the change. That workflow is more demanding than producing a standalone function because the model must preserve task context and respond to evidence from the development environment.
Pricing and API availability
When it was available through the API, GPT-5.1-Codex was priced at $1.25 per million input tokens and $10 per million output tokens. Cached input tokens were priced at $0.125 per million tokens. These are token-based API prices rather than a subscription fee for an end-user coding application.
OpenAI documented the model as available on paid API tiers, with no free-tier support. Batch API support was listed, as were streaming, function calling, and structured outputs. Fine-tuning was not supported.
The output-token price was substantially higher than the input-token price. Applications could therefore reduce spending by avoiding unnecessary repeated output, limiting overly verbose responses, and taking advantage of cached input where the same context was reused. However, cost optimization should not be confused with current availability: API access ended on July 23, 2026.
Strengths and trade-offs
GPT-5.1-Codex’s main technical strengths were its coding specialization, large context window, high output ceiling, reasoning support, and compatibility with tool-driven workflows. Those characteristics made it better suited to repository-scale work than a lightweight model intended mainly for short answers or quick completions.
The trade-off was that agentic coding tasks can consume substantial context and output, especially when the system repeatedly reads files, runs tools, and revises its plan. Its documented input and output prices were reasonable only in relation to the complexity of the work being performed; a fast, small coding request could be more efficiently handled by a lower-cost or lower-latency option.
The available research includes editorial scores for reasoning, coding, speed, and cost, but those ratings are evaluations rather than OpenAI-published benchmark results. They should not be presented as official performance guarantees. The provider-documented facts are the model’s supported features, limits, prices, and retirement dates.
When to choose GPT-5.1-Codex
For a historical evaluation, GPT-5.1-Codex was the appropriate type of model when the task involved substantial software-engineering context rather than a single short code answer. Its intended users included teams building coding agents, developers handling multi-file changes, and applications that needed a model to combine reasoning with controlled tool calls.
It was less appropriate for image generation, audio or video processing, native multimedia output, or general-purpose media creation. It was also a poor choice for a new deployment after its retirement. Developers starting now should follow OpenAI’s stated recommendation to evaluate GPT-5.6 Sol, while independently checking that model’s current pricing, limits, tool support, and coding behavior.
For small, latency-sensitive coding tasks, a faster or less expensive current coding model may be more suitable than a large agentic model. Conversely, for repository-wide work, the relevant comparison should include context capacity, tool integration, reliability over multiple steps, and the cost of repeated outputs—not just the quality of a single code snippet.
Limitations and retirement status
GPT-5.1-Codex produced text only. Image input expanded the information it could analyze, but it did not turn the model into an image, audio, or video generator. It also depended on the surrounding application for tool access, permissions, file operations, and test execution.
Its knowledge cutoff was September 30, 2024, according to the official model documentation. External search or tools, where separately configured, would not change the underlying cutoff of the model itself. Finally, because API access has ended, any documentation or code referring to GPT-5.1-Codex should be treated as archival. The model’s specifications remain useful for understanding its design, but they do not indicate that it can still be provisioned for new API traffic.