---
title: "GPT-5 Pro for OpenClaw: Pricing, Setup, and What It's Good At"
description: "Everything you need to know about using GPT-5 Pro with OpenClaw. Costs $15/M input, $120/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 pro openclaw, gpt-5 pro pricing, gpt-5 pro review, openai openclaw, gpt-5 pro api"
category: model-guide
modelData:
  provider: "OpenAI"
  provider_slug: "openai"
  full_id: "openai/gpt-5-pro"
  input_cost_per_1m: 15
  output_cost_per_1m: 120
  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 are the token limits?"
    answer: "The model supports a 400K token context window and can generate up to 128K tokens in a single response."
  - question: "Is it expensive to run?"
    answer: "Yes, while input is $15 per million tokens, the $120 per million output cost makes it one of the most expensive models on the market."
---

Current as of March 2026. GPT-5 Pro is OpenAI's flagship reasoning model, offering a 400K context window and a massive 128K output limit. It is powerful but comes with a high price tag of $120 per million output tokens.

## Specs

| | |
|---|---|
| **Provider** | OpenAI |
| **Input cost** | $15 / M tokens |
| **Output cost** | $120 / 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

#### Reasoning Capability

The model excels at complex logic and multi-step problem solving that trips up smaller models.

#### Output Capacity

A 128K output limit allows for generating entire technical books or massive code modules in a single request.

## Where it falls short

#### Output Pricing

At $120 per million tokens, the output cost is eight times higher than the input cost, which burns through budgets quickly.

#### Inference Latency

The deep reasoning features cause a noticeable delay before the model starts streaming responses.

## Best use cases with OpenClaw

- **Large-Scale Refactoring** — Use this when you need to ingest 400K tokens of legacy code and output a completely rewritten architecture.
- **Complex Agent Tasks** — Its native web search and function calling make it the most reliable engine for OpenClaw agents performing research.

## Not ideal for

- **High-Volume Simple Tasks** — Basic classification or summarization should be offloaded to cheaper models like GPT-4o-mini to save money.
- **Low-Latency Apps** — If your users expect a response in under two seconds, the reasoning overhead here will be a dealbreaker.

## OpenClaw setup

OpenClaw includes native support for this model; just export your OPENAI_API_KEY and the framework handles the rest without custom 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)** — Claude is significantly more affordable and often faster, though it lacks the 128K output ceiling of GPT-5 Pro.
- **vs Gemini 1.5 Pro** — Gemini offers a much larger 2M context window for a lower price, but its reasoning is less consistent for complex coding tasks.

## Bottom line

Use GPT-5 Pro for the hardest reasoning problems where cost is secondary to accuracy and massive output length.

<a href="https://app.haimaker.ai/sign-up?utm_source=model_guide&utm_medium=cta&utm_campaign=gpt-5-pro-openclaw" class="cta-button">TRY GPT-5 PRO 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).*