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 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.
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.
For setup instructions, see our API key guide. For all available models, see the complete models guide.