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OpenAI: GPT-5.2-Codex

Server-rendered model summary page for indexing/share previews. Use the interactive explorer for full filtering and comparison.

Match confidence: UnmatchedSource type: openrouter_only
Context window
400K
Arena overall rank
Input price
$0.000 / 1M
Output price
$0.000 / 1M

Identifiers & provenance

Primary ID
openai/gpt-5.2-codex
OpenRouter ID
openai/gpt-5.2-codex
Canonical slug
openai/gpt-5.2-codex-20260114

Source semantics

  • Arena rank is a human-preference leaderboard signal, not a universal truth metric.
  • OpenRouter usage/popularity reflects adoption/traffic, not benchmark quality.
  • Pricing fields may differ by provider and can include extra modes beyond prompt/completion.

Read more on Methodology & data sources.

Description

GPT-5.2-Codex is an upgraded version of GPT-5.1-Codex optimized for software engineering and coding workflows. It is designed for both interactive development sessions and long, independent execution of complex engineering tasks. The model supports building projects from scratch, feature development, debugging, large-scale refactoring, and code review. Compared to GPT-5.1-Codex, 5.2-Codex is more steerable, adheres closely to developer instructions, and produces cleaner, higher-quality code outputs. Reasoning effort can be adjusted with the `reasoning.effort` parameter. Read the [docs here](https://openrouter.ai/docs/use-cases/reasoning-tokens#reasoning-effort-level) Codex integrates into developer environments including the CLI, IDE extensions, GitHub, and cloud tasks. It adapts reasoning effort dynamically—providing fast responses for small tasks while sustaining extended multi-hour runs for large projects. The model is trained to perform structured code reviews, catching critical flaws by reasoning over dependencies and validating behavior against tests. It also supports multimodal inputs such as images or screenshots for UI development and integrates tool use for search, dependency installation, and environment setup. Codex is intended specifically for agentic coding applications.

Raw fields snapshot

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