---
title: "GPT-5.3-Codex for OpenClaw: Pricing, Setup, and What It's Good At"
description: "Everything you need to know about using GPT-5.3-Codex with OpenClaw. Costs $1.75/M input, $14/M output. 400K context window. Here's when to use it and when to pick something else."
date: 2026-03-12
updatedDate: 2026-03-12
image: /images/models/provider-openai-hero.jpg
keywords: "gpt-5.3-codex openclaw, gpt-5.3-codex pricing, gpt-5.3-codex review, openai openclaw, gpt-5.3-codex api"
category: model-guide
modelData:
  provider: "OpenAI"
  provider_slug: "openai"
  full_id: "openai/gpt-5.3-codex"
  input_cost_per_1m: 1.75
  output_cost_per_1m: 14
  max_input_tokens: 400000
  max_output_tokens: 128000
  features: ["function_calling", "vision", "reasoning", "web_search"]
  parameter_count: null
  architecture: null
  license: null
faq:
  - question: "What is the cost for a full context window?"
    answer: "Filling the 400K input window costs exactly $0.70 per request at the $1.75/1M rate."
  - question: "Can it handle images?"
    answer: "Yes, it has vision capabilities for analyzing UI mockups or architectural diagrams alongside code."
---

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- **Legacy Code Migration** — The 400K context window allows the model to map dependencies across massive, outdated repositories effectively.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.

```bash
export OPENAI_API_KEY="your-key-here"
```

That's it. OpenClaw picks up OpenAI models automatically.

## How it compares

- **vs [Claude 3.5 Sonnet](/blog/claude-3-5-sonnet-openclaw)** — 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.

<a href="https://app.haimaker.ai/sign-up?utm_source=model_guide&utm_medium=cta&utm_campaign=gpt-5-3-codex-openclaw" class="cta-button">TRY GPT-5.3-CODEX ON HAIMAKER</a>

---

*For setup instructions, see our [API key guide](/blog/openclaw-api-key-setup). For all available models, see the [complete models guide](/blog/best-models-for-openclaw).*