Current as of March 2026. O4 Mini has the same pricing as O3 Mini but adds vision — $1.10/M input, $4.40/M output, 200K context, 100K output. For agents that need to both reason and see, this is currently the most capable option in that price tier.
Specs
| Provider | OpenAI |
| Input cost | $1.10 / M tokens |
| Output cost | $4.40 / M tokens |
| Context window | 200K tokens |
| Max output | 100K tokens |
| Parameters | N/A |
| Features | function_calling, vision, reasoning |
What it’s good at
Reasoning Plus Vision
Most reasoning models drop vision support. O4 Mini keeps it, which opens up workflows where the agent needs to look at a UI, diagram, or chart and then reason about what to do next.
Output Ceiling
100K max output combined with reasoning capability is genuinely useful for tasks like rewriting large files or generating comprehensive technical specs while keeping logical consistency.
Multi-step Planning
Chain-of-thought reasoning reduces planning errors in OpenClaw loops. The agent thinks through tool dependencies before executing, which means fewer mid-workflow failures.
Where it falls short
Latency
The reasoning chain adds noticeable delay before first token. 7x the input cost of GPT-4o-mini combined with slower responses means you should be clear on why you need it before using it.
Cost for Simple Tasks
At $1.10/M input, it’s expensive for anything that doesn’t need reasoning. Extracting JSON from a predictable string, basic classification, high-volume routing — none of that benefits from chain-of-thought.
Best use cases with OpenClaw
- Agentic Planning — Works through tool dependencies before executing, which reduces loop errors on complex multi-step workflows.
- Complex Refactoring — The reasoning plus 100K output limit means it can rewrite large files while tracking logical consistency across the code.
Not ideal for
- Simple Extraction — Paying the reasoning premium for basic field extraction is wasteful. GPT-4o-mini handles that at $0.15/M.
- Real-time Chat — Time-to-first-token is too high for interactive UIs. Users will notice the thinking pause.
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 point to openai/o4-mini.
export OPENAI_API_KEY="your-key-here"
That’s it. OpenClaw picks up OpenAI models automatically.
How it compares
- vs Claude 3.5 Haiku — Haiku is faster and cheaper for straightforward tasks; O4 Mini wins on reasoning depth and context size.
- vs GPT-4o-mini — GPT-4o-mini is the right call for high-volume work without reasoning requirements. O4 Mini is for when logic depth actually matters.
Bottom line
O4 Mini is the best option when your OpenClaw agent needs to reason through a problem and look at images — that specific combination isn’t widely available at this price point.
For setup instructions, see our API key guide. For all available models, see the complete models guide.