---
title: "O3 for OpenClaw: Pricing, Setup, and What It's Good At"
description: "Everything you need to know about using O3 with OpenClaw. Costs $2.00/M input, $8.00/M output. 200K 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: "o3 openclaw, o3 pricing, o3 review, openai openclaw, o3 api"
category: model-guide
modelData:
  provider: "OpenAI"
  provider_slug: "openai"
  full_id: "openai/o3"
  input_cost_per_1m: 2
  output_cost_per_1m: 8
  max_input_tokens: 200000
  max_output_tokens: 100000
  features: ["function_calling", "vision", "reasoning"]
  parameter_count: null
  architecture: null
  license: null
faq:
  - question: "What is the context window for O3?"
    answer: "O3 features a 200K token context window and a massive 100K token maximum output limit."
  - question: "How much does it cost to run?"
    answer: "Input tokens are priced at $2 per million, while output tokens cost $8 per million."
---

Current as of March 2026. O3 is OpenAI's full-power reasoning model — internal chain-of-thought, 200K context, and a 100K output limit that makes it viable for generating large artifacts. It's expensive and slow. That's the deal.

## Specs

| | |
|---|---|
| **Provider** | OpenAI |
| **Input cost** | $2.00 / M tokens |
| **Output cost** | $8.00 / M tokens |
| **Context window** | 200K tokens |
| **Max output** | 100K tokens |
| **Parameters** | N/A |
| **Features** | function_calling, vision, reasoning |

## What it's good at

#### Logical Reasoning

O3 uses internal chain-of-thought before responding, which meaningfully reduces hallucinations on complex architectural problems. Tasks that trip up GPT-4o — long dependency chains, constraint satisfaction, multi-step derivations — are where O3 earns its price.

#### Output Ceiling

100K max output is large enough to generate entire codebases or comprehensive documentation sets in a single call. Combined with reasoning, this is useful for tasks that require both deep thinking and substantial output.

## Where it falls short

#### Latency

The reasoning phase adds real seconds before the first token appears. Fine for async agent steps; genuinely frustrating for anything a user is watching.

#### Cost

$8/M output tokens accumulates quickly in recursive loops. If your agent is calling O3 repeatedly on small tasks, you'll notice the bill.

## Best use cases with OpenClaw

- **Complex Refactoring** — Tracking cross-file dependencies across a large project while making coherent changes. The reasoning helps it not lose the thread.
- **Scientific and Mathematical Problems** — Multi-step derivations, logical verification, proof-checking. Precision over speed.

## Not ideal for

- **Simple Extraction** — Running O3 to pull fields from JSON is expensive and slow with no benefit over a cheaper model.
- **High-Frequency Tool Calls** — The thinking overhead makes rapid tool execution feel broken. Use GPT-4o or 4o-mini for that.

## Run it through Haimaker

Skip juggling API keys. One Haimaker key gives you access to every model on the platform. Tell OpenClaw:

```
Add Haimaker as a custom provider to my OpenClaw config. Use these details:

- Provider name: haimaker
- Base URL: https://api.haimaker.ai/v1
- API key: [PASTE YOUR HAIMAKER API KEY HERE]
- API type: openai-completions

Add the auto-router model:
- haimaker/auto (reasoning: false, context: 128000, max tokens: 32000)

Create an alias "auto" for easy switching. Apply the config when done.
```

Or skip model selection entirely — Haimaker's [auto-router](/blog/openclaw-auto-router-setup/) picks the best model for each task so you don't have to.

## OpenClaw setup

Set OPENAI_API_KEY and OpenClaw recognizes `openai/o3` immediately.

```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** — Sonnet is faster for general coding tasks; O3 has better reasoning for planning problems that require real logical depth.
- **vs DeepSeek-R1** — R1 is significantly cheaper for reasoning-heavy work, but O3's function calling and vision support are more reliable within OpenClaw.

## Bottom line

O3 is what you reach for when the problem is genuinely hard and you need the model to think it through. Not for everyday tasks — the cost and latency don't make sense there.

<a href="https://app.haimaker.ai/sign-up?utm_source=model_guide&utm_medium=cta&utm_campaign=o3-openclaw" class="cta-button">TRY O3 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).*