GPT-5.1 Codex API - pricing, context window and best use cases

GPT-5.1 Codex is OpenAI's coding-specialized variant of the GPT-5.1 family, tuned for agentic software work: reading large repositories, planning multi-step edits, running tools, and producing diffs that actually apply. If you write code for a living, it behaves less like a chatbot and more like a teammate that holds the whole project in its head. This guide covers how the API is priced, how big its context window is, and where it earns its keep.

Pricing: how token billing works

Like the rest of the GPT-5.1 line, Codex is billed per token, split into input and output. Input tokens (your prompt, system message, and any code you paste in) are the cheap side; output tokens (the model's generated reply) cost several times more. Two details matter for real budgets:

• Cached input is heavily discounted. When you resend the same large prefix - a system prompt or a repo snapshot - the cached portion is billed at a fraction of the normal input rate. Agentic coding loops reuse context constantly, so caching is where most of your savings live.
• Reasoning tokens count as output. Codex thinks before it answers, and that hidden reasoning is billed at the output rate. Higher reasoning effort means better answers on hard bugs but a larger bill, so match the effort level to the task.

The simplest way to control spend is to keep prompts tight, lean on caching, and reserve maximum reasoning for genuinely hard problems. For current per-model numbers across providers, check the models list and compare pages rather than memorizing a table that changes.

Context window

GPT-5.1 Codex offers a very large context window - hundreds of thousands of tokens - which is the feature that makes it useful for whole-codebase work. In practice that means you can feed it many files at once and ask for changes that stay consistent across all of them, instead of editing one file blind to the rest.

A word of caution: a big window is not free attention. The more you stuff in, the more you pay and the more the model has to sift. Curate context - the relevant files, the failing test, the error trace - rather than dumping the entire tree. Quality of context beats quantity almost every time.

Best use cases

GPT-5.1 Codex shines when the task has structure and verifiable outcomes:

• Agentic, multi-file refactors - renaming an API across a project, migrating a framework version, or threading a new parameter through many call sites.
• Bug fixing from a failing test - give it the test, the stack trace, and the relevant source, and let it iterate until green.
• Code review and explanation - summarizing an unfamiliar repo, flagging risky patterns, or writing the missing docstrings.
• Scaffolding and glue - CLIs, config, CI pipelines, and the boilerplate nobody enjoys.

It is overkill for one-line snippets or quick syntax questions, where a smaller, cheaper model answers just as well. Routing trivial work to a lighter model and saving Codex for the hard parts is the most cost-effective pattern.

Calling it through AnyModel

AnyModel gives you one OpenAI-compatible endpoint - base URL https://anymodel.org/v1 - and a single API key that reaches every model we host (GPT, Claude, Gemini, DeepSeek, GLM, Kimi, Qwen, Grok). Switching models means changing the model id and nothing else, so you can pit Codex against another model on the same task without rewiring anything.

If you use the Codex CLI, the fastest setup is a one-line install:

bash <(curl -fsSL "https://anymodel.org/i?tool=codex") <YOUR_API_KEY>

The same installer supports tool=claude, tool=opencode, and tool=hermes. For editors like Cursor, Windsurf, Zed, Cline, Aider, Continue, or Gemini CLI, point them at the base URL above with your key using their standard OpenAI-compatible settings.

Privacy-sensitive code? Turn on Ghost Mode for opt-in zero-retention keys: we don't store your prompts or responses, only a token counter. Note the model provider still receives the prompt to generate a reply, so this isn't absolute secrecy - it's our side keeping nothing.

Getting started

You can start free: every signup gets 1,000,000 tokens, and linking Telegram brings the total to 6,000,000 - no credit card. After that it's pay-per-token, with no subscription and no minimums. Browse more guides on the blog, then create your account and run GPT-5.1 Codex on your own repo today.