Current as of March 2026. GPT-5.3-Codex is OpenAI’s high-context workhorse for developers who need to feed entire codebases into an agent. At $1.75 per million input tokens and a 400K context window, it balances scale with high-level reasoning.
Specs
| Provider | OpenAI |
| Input cost | $1.75 / M tokens |
| Output cost | $14 / M tokens |
| Context window | 400K tokens |
| Max output | 128K tokens |
| Parameters | N/A |
| Features | function_calling, vision, reasoning, web_search |
What it’s good at
Massive Output Buffer
The 128K max output is significantly higher than standard models, allowing for full-module rewrites in a single pass.
Integrated Tooling
Native function calling and web search are tightly integrated, reducing the hallucination rate when the agent needs to verify external documentation.
Where it falls short
Output Pricing Skew
The $14 per million output cost is an 8x markup over input, which gets expensive fast for agents generating large chunks of code.
Latency Spikes
The reasoning layer adds a noticeable delay to the first token, making it feel sluggish for interactive chat use.
Best use cases with OpenClaw
- Legacy Code Migration — The 400K context window allows the model to map dependencies across massive, outdated repositories effectively.
- Autonomous Debugging — Its reasoning features excel at tracing logic errors through multiple files without losing the thread.
Not ideal for
- Simple Boilerplate — Using a model this expensive for basic CRUD operations is a waste of money compared to GPT-4o-mini.
- Real-time Coding Assistants — The high latency makes it frustrating for type-as-you-go autocomplete features.
OpenClaw setup
OpenClaw treats this as a first-class citizen. Set your OPENAI_API_KEY environment variable and you are ready to go without any extra configuration.
export OPENAI_API_KEY="your-key-here"
That’s it. OpenClaw picks up OpenAI models automatically.
How it compares
- vs Claude 3.5 Sonnet — Sonnet is often faster and better at stylistic Python, but GPT-5.3-Codex wins on raw context volume (400K vs 200K).
- vs Gemini 1.5 Pro — Gemini offers a larger 2M window, but GPT-5.3-Codex provides more reliable function calling and reasoning in complex logic branches.
Bottom line
This is the best choice for complex agentic workflows where context size and reasoning are more important than low-cost output.
For setup instructions, see our API key guide. For all available models, see the complete models guide.