---
title: "Llama 4 Scout for OpenClaw: Pricing, Setup, and What It's Good At"
description: "Everything you need to know about using Llama 4 Scout with OpenClaw. Costs $0.08/M input, $0.30/M output. 328K 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-meta-llama-hero.jpg
keywords: "llama 4 scout openclaw, llama 4 scout pricing, llama 4 scout review, meta (llama) openclaw, llama 4 scout api"
category: model-guide
modelData:
  provider: "Meta (Llama)"
  provider_slug: "meta-llama"
  full_id: "meta-llama/llama-4-scout"
  input_cost_per_1m: 0.08
  output_cost_per_1m: 0.3
  max_input_tokens: 327680
  max_output_tokens: 16384
  features: ["function_calling", "vision"]
  parameter_count: null
  architecture: null
  license: null
faq:
  - question: "What is the actual cost per million tokens?"
    answer: "Input costs $0.08 and output costs $0.3 per million tokens, making it highly competitive for high-volume tasks."
  - question: "How much text can I actually fit in one request?"
    answer: "You can fit roughly 250,000 words into its 328K context window, which is enough for several large technical manuals."
  - question: "Does it support local hosting?"
    answer: "Yes, you can run Llama 4 Scout via Ollama or vLLM and connect it to OpenClaw via a local OpenAI-compatible API endpoint."
---

Current as of March 2026. Llama 4 Scout is Meta's play for the high-context, low-cost agent market, offering a massive 328K window for just $0.08 per million input tokens. It is built for developers who need to feed large codebases into OpenClaw without paying the premium for Claude or GPT-4o.

## Specs

| | |
|---|---|
| **Provider** | Meta (Llama) |
| **Input cost** | $0.08 / M tokens |
| **Output cost** | $0.30 / M tokens |
| **Context window** | 328K tokens |
| **Max output** | 16K tokens |
| **Parameters** | N/A |
| **Features** | function_calling, vision |

## What it's good at

#### Aggressive Pricing

At $0.08 per million input tokens and $0.3 per million output tokens, it is significantly cheaper than GPT-4o-mini or Claude 3 Haiku for bulk processing.

#### Deep Context Handling

The 328K context window allows for massive RAG injections or long-running OpenClaw agent sessions that would typically hit token limits on smaller models.

#### Reliable Function Calling

Tool use is snappy and follows JSON schemas strictly, making it a dependable choice for OpenClaw's automated tool execution.

## Where it falls short

#### Vision Latency

The vision processing feels slower than GPT-4o-mini, which can cause bottlenecks in agents that need to analyze screenshots frequently.

#### Output Coherence

While it can output 16K tokens, the logic starts to fray after about 4K tokens of continuous generation compared to Llama 3.3 70B.

## Best use cases with OpenClaw

- **Large-Scale Code Analysis** — The 328K context window lets you dump entire directories into the prompt for refactoring tasks at a fraction of the usual cost.
- **High-Frequency Background Agents** — Its low input cost makes it ideal for agents that need to poll APIs or monitor logs continuously without blowing the budget.

## Not ideal for

- **Real-time UI Automation** — The vision model's response time is too high for interactive tasks that require sub-second visual feedback.
- **Creative Long-form Writing** — The output tends to become repetitive and overly clinical when generating documents longer than 2,000 words.

## OpenClaw setup

Configure your OpenClaw provider to use api.haimaker.ai/v1 or point it to a local Ollama endpoint for self-hosting. Ensure your temperature is set below 0.7 to maintain strict adherence to function calling schemas.

```json
{
  "models": {
    "mode": "merge",
    "providers": {
      "meta-llama": {
        "baseUrl": "https://api.haimaker.ai/v1",
        "apiKey": "YOUR-META-(LLAMA)-API-KEY",
        "api": "openai-completions",
        "models": [
          {
            "id": "llama-4-scout",
            "name": "Llama 4 Scout",
            "cost": {
              "input": 0.08,
              "output": 0.3
            },
            "contextWindow": 327680,
            "maxTokens": 16384
          }
        ]
      }
    }
  }
}
```

## How it compares

- **vs GPT-4o-mini** — GPT-4o-mini is faster for short chat bursts, but Llama 4 Scout offers over double the context window (328K vs 128K) for a lower price.
- **vs [Claude 3 Haiku](/blog/claude-3-haiku-openclaw)** — Haiku has better nuance in short-form reasoning, but Scout wins on raw data throughput and cost efficiency for agentic loops.

## Bottom line

Llama 4 Scout is the current price-to-performance king for high-context OpenClaw agents that don't require the absolute highest reasoning capabilities of a flagship model.

<a href="https://app.haimaker.ai/sign-up?utm_source=model_guide&utm_medium=cta&utm_campaign=llama-4-scout-openclaw" class="cta-button">TRY LLAMA 4 SCOUT 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).*