---
title: "O1 for OpenClaw: Pricing, Setup, and What It's Good At"
description: "Everything you need to know about using O1 with OpenClaw. Costs $15/M input, $60/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: "o1 openclaw, o1 pricing, o1 review, openai openclaw, o1 api"
category: model-guide
modelData:
  provider: "OpenAI"
  provider_slug: "openai"
  full_id: "openai/o1"
  input_cost_per_1m: 15
  output_cost_per_1m: 60
  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?"
    answer: "O1 supports a 200K token context window and can output up to 100K tokens per request."
  - question: "How much does it cost?"
    answer: "Input tokens are priced at $15 per million, while output tokens cost $60 per million."
---

Current as of March 2026. O1 is OpenAI's reasoning model: before it writes a word of output, it works through the problem internally. That hidden reasoning phase is why it's slower and more expensive than chat models — and also why it handles problems that make GPT-4o stumble. At $15/$60 per million tokens, it's not for casual use.

## Specs

| | |
|---|---|
| **Provider** | OpenAI |
| **Input cost** | $15 / M tokens |
| **Output cost** | $60 / M tokens |
| **Context window** | 200K tokens |
| **Max output** | 100K tokens |
| **Parameters** | N/A |
| **Features** | function_calling, vision, reasoning |

## What it's good at

#### Hard Reasoning Problems

Multi-step proofs, symbolic math, complex logic chains — this is what it's built for. If GPT-4o keeps getting the answer wrong, O1 often gets it right.

#### Long Output

100K max output tokens. You can generate substantial code or documentation in a single pass.

## Where it falls short

#### Latency

The internal reasoning phase can take seconds to minutes before the first output token. Users watching a blank screen will assume something broke.

#### Cost

$60/M output. High-volume agent loops will get expensive fast. Be selective about what you send here.

## Best use cases with OpenClaw

- **Architectural Refactoring** — Feed a 200K context of codebase and let it reason through dependency changes. The reasoning quality justifies the cost on hard problems.
- **Scientific and Mathematical Analysis** — Dense formulas, logical inconsistencies in research papers, proofs. This is where O1 earns its price.

## Not ideal for

- **Basic Summarization** — Wasteful. GPT-4o-mini handles this for pennies.
- **User-facing Chat** — The latency alone will kill the experience.

## 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

OpenClaw handles O1 natively through the OpenAI provider. Simply export your OPENAI_API_KEY and set the model ID to openai/o1 in your agent settings.

```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 much faster and better for everyday coding. O1 wins when the problem requires genuine reasoning depth, not just competent code generation.
- **vs DeepSeek-R1** — R1 gets close on reasoning benchmarks at a fraction of the price. If API reliability matters less to you, R1 is worth testing first.

## Bottom line

O1 is a specialized reasoning engine. Use it for the hard problems — debugging subtle logic, architectural decisions, math. Route everything else to cheaper models.

<a href="https://app.haimaker.ai/sign-up?utm_source=model_guide&utm_medium=cta&utm_campaign=o1-openclaw" class="cta-button">TRY O1 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).*