---
title: "GPT 4.1 Nano for OpenClaw: Pricing, Setup, and What It's Good At"
description: "Everything you need to know about using GPT 4.1 Nano with OpenClaw. Costs $0.10/M input, $0.40/M output. 1.0M 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 4.1 nano openclaw, gpt 4.1 nano pricing, gpt 4.1 nano review, openai openclaw, gpt 4.1 nano api"
category: model-guide
modelData:
  provider: "OpenAI"
  provider_slug: "openai"
  full_id: "openai/gpt-4.1-nano"
  input_cost_per_1m: 0.09999999999999999
  output_cost_per_1m: 0.4
  max_input_tokens: 1047576
  max_output_tokens: 32768
  features: ["function_calling", "vision"]
  parameter_count: null
  architecture: null
  license: null
faq:
  - question: "What is the exact pricing for this model?"
    answer: "Input costs approximately $0.10 per million tokens and output costs $0.40 per million tokens."
  - question: "How many tokens can it process at once?"
    answer: "It supports a 1,000,000 token context window and can generate up to 33,000 tokens in a single response."
  - question: "Does it support vision tasks?"
    answer: "Yes, it has native vision support, allowing you to include images within that 1M token context."
---

Current as of March 2026. GPT-4.1 Nano is where the 4.1 line hits its price floor — $0.10/M input for a full 1M token context window. That's a remarkable deal for bulk ingestion tasks. The model is not deep, but it doesn't need to be for the jobs it's suited for.

## Specs

| | |
|---|---|
| **Provider** | OpenAI |
| **Input cost** | $0.10 / M tokens |
| **Output cost** | $0.40 / M tokens |
| **Context window** | 1.0M tokens |
| **Max output** | 33K tokens |
| **Parameters** | N/A |
| **Features** | function_calling, vision |

## What it's good at

#### Context Value

$0.10/M input for a million-token window lets you skip RAG entirely for many problems and just feed raw data. That architectural simplification has real value when you're iterating fast.

#### Tool Use

OpenAI's function calling consistency extends to the Nano tier. For basic structured outputs and tool invocations, it holds up.

## Where it falls short

#### Shallow Reasoning

The model loses the thread on complex multi-step logic, and context doesn't help — it can get genuinely confused in the middle of a million-token prompt when asked for precision.

#### Output Ceiling

33K output is fine for most tasks, but if you need to generate large files or comprehensive documentation in one shot, you're capped.

## Best use cases with OpenClaw

- **Repository Auditing** — Load an entire project to scan for deprecated patterns or security issues without chunking. The economics make batch processing feasible.
- **High-Volume Tagging** — Processing millions of log lines or support tickets for classification. At $0.10/M, you can run a lot of rows for almost nothing.

## Not ideal for

- **Complex Code Logic** — Intricate algorithmic problems or niche library syntax will trip this model up. Use GPT-4o or Claude for that.
- **Creative Writing** — The output is dry. Fine for internal tooling, not suitable for anything user-facing without heavy editing.

## 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

Export OPENAI_API_KEY and point to `openai/gpt-4.1-nano`.

```bash
export OPENAI_API_KEY="your-key-here"
```

That's it. OpenClaw picks up OpenAI models automatically.

## How it compares

- **vs Claude 3 Haiku** — Haiku is faster on short prompts; Nano's 1M context window is the decisive difference for document-heavy work.
- **vs Gemini 1.5 Flash** — Flash also hits 1M context at similar pricing; Nano integrates more cleanly with OpenClaw's default tool-calling setup.

## Bottom line

GPT-4.1 Nano is the cheapest path to a million-token context window. For bulk, low-complexity processing, there's nothing more cost-effective in the OpenAI lineup.

<a href="https://app.haimaker.ai/sign-up?utm_source=model_guide&utm_medium=cta&utm_campaign=gpt-4.1-nano-openclaw" class="cta-button">TRY GPT 4.1 NANO 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).*