---
title: "GPT 4o Mini for OpenClaw: Pricing, Setup, and What It's Good At"
description: "Everything you need to know about using GPT 4o Mini with OpenClaw. Costs $0.15/M input, $0.60/M output. 128K 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: "gpt 4o mini openclaw, gpt 4o mini pricing, gpt 4o mini review, openai openclaw, gpt 4o mini api"
category: model-guide
modelData:
  provider: "OpenAI"
  provider_slug: "openai"
  full_id: "openai/gpt-4o-mini"
  input_cost_per_1m: 0.15
  output_cost_per_1m: 0.6
  max_input_tokens: 128000
  max_output_tokens: 16384
  features: ["function_calling", "vision"]
  parameter_count: null
  architecture: null
  license: null
faq:
  - question: "What is the context window size?"
    answer: "The model supports up to 128K tokens, which is roughly equivalent to 300 pages of text."
  - question: "How much does it cost to use?"
    answer: "Input tokens are $0.15 per million and output tokens are $0.60 per million."
  - question: "Does it support image inputs?"
    answer: "Yes, it has native vision capabilities for OCR and general image description tasks."
---

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](/blog/openclaw-auto-router-setup/) 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.

```bash
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.

<a href="https://app.haimaker.ai/sign-up?utm_source=model_guide&utm_medium=cta&utm_campaign=gpt-4o-mini-openclaw" class="cta-button">TRY GPT 4O MINI 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).*