OpenAI: GPT-5 Codex
By Openai
Released: 2025-09-23
OpenAI GPT 5 Codex, developed by OpenAI, features 400k-token context window. GPT-5-Codex is a specialized version of GPT-5 optimised 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, 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 behaviour 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. Premium pricing at $1.25/1M tokens reflects its advanced capabilities.
Visit OpenAI: GPT-5 CodexAI-Powered
Leverages advanced AI technology to deliver cutting-edge capabilities and results.
Fast & Efficient
Optimized performance ensures quick results without compromising on quality.
Purpose-Built
Specifically designed for llms tasks and workflows.
Openai Model Timeline
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Specifications
AI Evaluation
Combines language understanding with search capabilities. Excels at finding and synthesizing information from various sources.
Pros
- 400k token context window
- Strong code generation and debugging
- Advanced logical reasoning
- Multi-modal input support
Cons
- Moderate API costs
- May lack creative flair
- Speed/quality trade-off
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