Codex Mini

Technical Specifications of codex-mini

codex-mini is CometAPI’s platform identifier for OpenAI’s Codex mini family, which OpenAI documents as a fast reasoning model optimized for Codex CLI workflows. The official OpenAI model page for codex-mini-latest describes it as a fine-tuned version of o4-mini, with text and image input support, text output, medium speed, and higher reasoning performance for coding-oriented tasks. OpenAI’s model docs also list pricing for codex-mini-latest at $1.50 per 1M input tokens and $6.00 per 1M output tokens.

OpenAI further states that codex-mini-latest is intended primarily for Codex CLI usage, and notes that for direct API usage developers may want to start with a more general model depending on the task. In OpenAI’s tooling guides, codex-mini-latest is specifically called out as supporting the local shell tool through the Responses API, where the model can return command instructions while execution remains under the developer’s control.

Historically, OpenAI positioned Codex models as coding-specialized models for software engineering tasks such as code generation, editing, review, and agentic development workflows. OpenAI’s Codex resources emphasize that Codex models are built for coding and engineering productivity, including use in CLI, SDK, IDE, and cloud-assisted development flows.

Because OpenAI has since introduced newer Codex variants, codex-mini should be understood as a lightweight coding-focused model identifier on CometAPI rather than the newest flagship coding model in OpenAI’s lineup. OpenAI’s deprecation page shows that codex-mini-latest was scheduled for removal on February 12, 2026, with a recommended replacement of gpt-5-codex-mini, so availability on aggregator platforms may depend on provider routing and compatibility layers.

What is codex-mini?

codex-mini is a compact coding-oriented language model route intended for developer workflows that need faster response times and lower cost than larger coding models. Based on OpenAI’s official descriptions of the underlying Codex mini line, it is designed for practical software engineering tasks such as writing code, modifying existing code, explaining code behavior, and assisting with terminal-centric development workflows.

In practical terms, this model is best suited for lightweight to mid-complexity coding assistance: generating functions, fixing bugs, drafting scripts, refactoring small modules, and helping developers work iteratively inside automated or semi-automated coding pipelines. OpenAI’s documentation around Codex and code generation consistently frames these models as tools for agentic coding and engineering acceleration rather than general-purpose conversational assistants first.

For CometAPI users, that means codex-mini can be treated as a coding-specialized model ID for applications that need code-aware reasoning without always paying the latency or cost of a larger frontier model. Since CometAPI abstracts provider access behind a unified API, the exact backend snapshot may vary, but the model family characteristics are those of OpenAI’s smaller Codex-tuned models. This is an inference based on CometAPI’s model identifier and OpenAI’s official Codex mini documentation.

Main features of codex-mini

• Coding-focused optimization: codex-mini is aligned with the Codex family, which OpenAI positions for software engineering tasks such as code generation, editing, review, and agentic development work.
• Fast reasoning profile: OpenAI describes the Codex mini line as a fast reasoning model, making it suitable for interactive developer tooling and iterative coding loops.
• Cost-efficient compared with larger coding models: OpenAI presents the mini variant as a lighter-weight option, with lower pricing than larger Codex-class models, which is useful for high-volume coding workloads.
• Text and image input support: OpenAI’s model page lists both text and image as supported inputs, which can help in workflows such as using screenshots, diagrams, or UI captures as part of coding assistance.
• Text output for code and explanations: The model returns text output, which covers generated code, patch suggestions, command plans, inline explanations, and debugging guidance.
• Useful for CLI-centered workflows: OpenAI specifically optimized codex-mini-latest for Codex CLI and documented support for the local shell tool in the Responses API.
• Agentic development potential: OpenAI’s broader Codex documentation highlights autonomous and semi-autonomous engineering workflows, so codex-mini is a fit for assistants that plan and propose coding actions even when used in a lighter-weight configuration.
• Best for lightweight and routine engineering tasks: Compared with larger coding models, the mini tier is generally better suited for smaller edits, code scaffolding, helpers, automation scripts, and rapid iterative use. This is a practical inference from OpenAI’s positioning of mini variants as smaller and more cost-effective.

How to access and integrate codex-mini

Step 1: Sign Up for API Key

To access the codex-mini API through CometAPI, first create a CometAPI account and generate your API key from the dashboard. After you have the key, store it securely in an environment variable such as COMETAPI_API_KEY so your application can authenticate requests without hardcoding secrets in source files.

Step 2: Send Requests to codex-mini API

Use CometAPI’s OpenAI-compatible endpoint and specify codex-mini as the model. A typical request looks like this:

curl https://api.cometapi.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $COMETAPI_API_KEY" \
  -d '{
    "model": "codex-mini",
    "messages": [
      {
        "role": "user",
        "content": "Write a Python function that validates whether a string is a palindrome."
      }
    ]
  }'

You can also call the same model from the OpenAI SDK by pointing the client to CometAPI’s base URL and keeping codex-mini as the model ID.

Step 3: Retrieve and Verify Results

After receiving the response, parse the returned message content and validate the generated output in your application workflow. For coding use cases, it is best practice to run tests, lint generated code, verify security-sensitive changes, and keep a human review step for production deployments. This is especially important for coding models, since OpenAI’s Codex tooling documentation emphasizes that execution and verification should remain under developer control.