Llama 4 ships two models that look similar on paper but serve very different purposes. Scout is the efficient long-context specialist. Maverick is the frontier-quality generalist. Choosing wrong means either paying too much or getting worse results.

Side-by-side comparison

	Scout	Maverick
Total parameters	109B	400B
Active parameters	17B	17B
Experts	16 (1 active)	128 (1 active)
Context window	10M tokens	1M tokens
LMArena score	~1350	~1400+
MMLU	82.1%	85.2%
HumanEval	78.1%	82.4%
API cost (Together)	~$0.10/1M	~$0.49/1M
RAM (quantized)	~25-32 GB	~60-80 GB
GPU (full precision)	4x A100 80GB	8x A100 80GB

Same active parameters (17B), very different total knowledge. Maverick’s 128 experts give it access to more specialized knowledge than Scout’s 16, which is why it scores higher on benchmarks.

When to use Scout

Long document processing. 10M tokens = ~7.5 million words. You can feed entire codebases, legal document collections, or research paper archives in a single request.

Cost-sensitive applications. At ~$0.10/1M tokens, Scout is 5x cheaper than Maverick and 25x cheaper than GPT-5.4.

Running locally on consumer hardware. Scout fits in 32GB RAM with Q4 quantization. Maverick needs 64GB+.

Retrieval-augmented generation. The massive context window means you can stuff more retrieved documents into context, reducing the need for sophisticated RAG chunking strategies.

When to use Maverick

Coding tasks. 82.4% HumanEval vs Scout’s 78.1%. The quality difference is noticeable on complex code generation.

General-purpose assistant. Maverick’s 128 experts give it broader knowledge across domains. For a chatbot or coding assistant that handles diverse queries, Maverick is the better choice.

Competing with proprietary models. If you’re replacing GPT-5.4 or Claude with an open model, Maverick is the closest in quality.

Multimodal tasks. Both support vision, but Maverick’s larger expert pool handles complex image understanding better.

Running both locally

# Scout: fits on a single machine with 32GB RAM
ollama pull llama4-scout:q4_k_m
ollama run llama4-scout "Analyze all files in this project for security issues"

# Maverick: needs more RAM or a GPU server
# Option 1: Heavy quantization on 64GB machine
ollama pull llama4-maverick:q3_k_m

# Option 2: Cloud GPU (recommended)
# RunPod A100 80GB: ~$1.50/hr
# Together AI API: $0.49/1M tokens

For hardware details, see our VRAM guide and GPU buying guide.

The practical setup: use both

The optimal approach is routing between them:

async def route_to_llama(message, context_length):
    if context_length > 1_000_000:
        # Only Scout handles >1M context
        return await call_model("llama4-scout", message)
    elif needs_high_quality(message):
        # Complex tasks get Maverick
        return await call_model("llama4-maverick", message)
    else:
        # Simple tasks get Scout (cheaper)
        return await call_model("llama4-scout", message)

This gives you Maverick quality when you need it and Scout efficiency when you don’t. Total cost stays low because most requests are simple enough for Scout.

Both vs the competition

For developers choosing between Llama 4 and other open models:

Need	Best choice
Best open-source coding	GLM-5.1 (58.4% SWE-bench Pro)
Longest context (open)	Llama 4 Scout (10M tokens)
Best open generalist	Llama 4 Maverick
Cheapest good model	Qwen 3.6 Plus (free API)
Best reasoning (small)	DeepSeek R1 14B
Runs on 8GB RAM	Qwen3 8B

Llama 4’s unique advantage is the combination of open weights + MoE efficiency + massive context. No other open model matches this combination.

FAQ

What’s the difference between Llama 4 Scout and Maverick?

Scout is the efficient long-context specialist (109B params, 16 experts, 10M token context). Maverick is the frontier-quality generalist (400B params, 128 experts, 1M token context). Both use 17B active parameters, but Maverick’s 128 experts give it access to more specialized knowledge, scoring higher on benchmarks.

Which is better for coding?

Maverick scores 82.4% on HumanEval vs Scout’s 78.1%. For code generation quality, Maverick is the better choice. However, Scout is 5x cheaper and handles most routine coding tasks well. Use Maverick for complex code generation and Scout for cost-sensitive or simple coding tasks.

Can I run both locally?

Yes, both are open-weight models. Scout fits in ~32GB RAM with Q4 quantization, making it practical on a Mac with 32GB+ unified memory. Maverick needs 64GB+ RAM or a dedicated GPU server. For most developers, Scout is the realistic local option while Maverick is better accessed via API.

Related: Llama 4 Complete Guide · How to Run Llama 4 Locally · Gemma 4 vs Llama 4 vs Qwen 3.5 · Falcon vs Llama vs Qwen · Best Open Source Coding Models · Best Free AI Models · VRAM Guide