Llama 4 Scout for OpenClaw: Pricing, Setup, and What It's Good At

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.

{
  "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 — 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.

TRY LLAMA 4 SCOUT ON HAIMAKER

---

For setup instructions, see our API key guide. For all available models, see the complete models guide.