Current as of March 2026. GPT-4o Mini killed GPT-3.5 Turbo. For most agentic tasks that don’t need serious reasoning, $0.15/M input is hard to argue with. It’s where I’d start any new OpenClaw project before deciding I need something heavier.
Specs
| Provider | OpenAI |
| Input cost | $0.15 / M tokens |
| Output cost | $0.60 / M tokens |
| Context window | 128K tokens |
| Max output | 16K tokens |
| Parameters | N/A |
| Features | function_calling, vision |
What it’s good at
Price
At $0.15/M input and $0.60/M output, it’s the cheapest way to get reliable OpenAI function calling. You can run a lot of agent turns before it becomes a line item worth caring about.
Solid Output Ceiling
16K max output is generous for a small model. Competitors in the same tier often cap out at 4K, which creates awkward chunking logic you don’t need here.
Function Calling
Follows tool schemas with enough consistency for production use. Not quite GPT-4o level, but close enough for most workflows.
Where it falls short
Reasoning Depth
Multi-step logical deduction falls apart — complex math, deep stack trace analysis, intricate architecture decisions. This is a pattern-matching model, not a thinking one.
Vision Detail
The vision support is there, but it misses fine-grained detail in complex images. If you need to read small text in a screenshot, step up to GPT-4o.
Best use cases with OpenClaw
- High-Volume Classification — Thousands of categorization tasks per hour without significant cost. Good fit for the filtering layer of a larger agent pipeline.
- Agentic Routing — Works well as the router node in an OpenClaw graph — quick, cheap decisions about which specialized agent handles a query.
- Simple Data Extraction — Structured JSON from unstructured text is the sweet spot, as long as the schema isn’t deeply nested.
Not ideal for
- Complex Software Engineering — Stack trace debugging and large codebase refactoring both require more reasoning depth than this model has.
- Creative Writing — The output is repetitive and flat. Claude 3.5 Sonnet is a better pick for anything the user will actually read.
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
OpenAI is the default provider in OpenClaw. Export the key, done.
export OPENAI_API_KEY="your-key-here"
That’s it. OpenClaw picks up OpenAI models automatically.
How it compares
- vs Claude 3 Haiku — 4o Mini benchmarks higher and has a 16K output limit; Haiku caps at 4K and is faster on short prompts.
- vs Gemini 1.5 Flash — Flash wins on context window size (1M vs 128K); 4o Mini is more consistent for structured tool use within OpenClaw.
Bottom line
Start here. If your agent works on 4o Mini, ship it. Only upgrade if you hit a real wall with reasoning or context — the cost savings are too good to skip.
For setup instructions, see our API key guide. For all available models, see the complete models guide.