What Makes the MoE Architecture Different?

Both Scout and Maverick use a Mixture-of-Experts design, but the scale difference is dramatic:

• Scout: 109B total parameters, 16 experts, 1 active per token
• Maverick: 400B total parameters, 128 experts, 1 active per token

The key insight — and the reason this comparison matters — is that both models have identical inference speed. Only 17B parameters activate per token in either model. The 128 experts in Maverick don’t make it slower; they give it a broader pool of specialized knowledge to draw from per forward pass.

I’ve been running both models through DeepInfra and Groq since launch, and the latency difference is negligible for most workloads. Scout averages about 15-20% faster on first-token latency due to the smaller expert pool to route through, but once streaming starts, both deliver tokens at roughly the same rate.

The 2T-parameter Behemoth model was used as the teacher to distill both Scout and Maverick. That’s the same distillation-first approach Meta used with Llama 3 — train a massive teacher, then compress the knowledge into deployable student models.

Llama 4 Scout: The Long-Context Specialist

Scout’s headline feature is the 10 million token context window. That’s not a typo — ten million tokens. In practice, this changes what you can do in a single model call.

What 10M Tokens Actually Unlocks

• Feed an entire codebase (the Linux kernel is ~15M tokens) in one shot
• Process a full book series for analysis
• Run RAG pipelines without chunking — just dump the entire document corpus into context
• Analyze months of chat logs, financial reports, or sensor data

I built a prototype RAG pipeline that skips chunking entirely. Instead of splitting documents into 512-token chunks, embedding them, and running vector search, I just concatenated the source documents and fed them directly into Scout’s context. The results were surprisingly good — no retrieval quality loss from chunk boundary artifacts, and the model could reference information across the entire document set.

Hardware Requirements

Scout runs on a single GPU with 4-bit quantization. Real-world numbers:

• RTX 4090 (24GB VRAM): Works with 4-bit quantization for up to ~32K context
• A10G (24GB): Same story — fine for moderate context lengths
• A100 80GB: Can handle the full 10M context window with appropriate quantization
• Memory usage: ~24-48GB VRAM depending on quantization and context length

Benchmark Performance

Scout supports 100+ languages, native tool use, and function calling for agentic workflows. If you’re building a multilingual chatbot or a tool-using agent, Scout handles it out of the box.

Llama 4 Maverick: The Frontier-Quality Generalist

Maverick is the model you reach for when quality matters more than cost. With 128 experts and a 1M context window, it’s designed for complex reasoning, coding, and multimodal tasks.

Where Maverick Shines

• Coding: HumanEval 85.5%, beats GPT-4o on several coding benchmarks
• Complex reasoning: MMLU Pro 80.5%, MMLU 85.2%
• Vision tasks: Significantly outperforms Scout (55 vs 35 on vision benchmarks)
• Writing quality: Scores 66 vs Scout’s 60 on writing evaluations

I’ve been using Maverick for code review in my CI pipeline. It catches subtle logic errors that Scout misses — particularly in Python async patterns and Rust ownership semantics. The 128 experts seem to give it deeper knowledge of language-specific idioms.

Hardware Requirements

Maverick is not a single-GPU model:

• Minimum: ~60-80GB VRAM with 4-bit quantization
• Comfortable: 2x A100 80GB or equivalent
• Full precision: 4-8 GPUs depending on context length

Benchmark Performance

Maverick outperforms Scout on 11 of 12 shared benchmarks. The only area where Scout wins is research-oriented tasks that benefit from its massive context window.

Pricing Analysis: When Cost Matters

Here’s the real-world pricing across major providers as of mid-2026:

On a blended 3:1 input-to-output ratio, Scout is approximately 2.1x cheaper than Maverick. But the gap widens dramatically for long-context workloads — if you’re using 1M+ tokens of context, Scout is the only practical choice since Maverick caps at 1M.

Scout is also 5x cheaper than Maverick and 25x cheaper than GPT-5.4 on a per-token basis. For high-volume production workloads, that difference adds up fast.

Free-Tier API Access: Getting Started with Groq

Groq offers a free tier for Llama 4 Scout that’s genuinely useful for prototyping:

• $0.11/1M input, $0.34/1M output (paid)
• 300K tokens per minute free tier — enough for serious development work
• Low latency thanks to Groq’s LPU inference hardware

I’ve been using the Groq free tier for Scout-based experiments since launch. The 300K TPM limit is generous enough to run a small RAG pipeline or chatbot without paying a cent. For comparison, most free tiers from other providers cap at 10-50K TPM.

Maverick isn’t available on Groq’s free tier, which makes Scout the default choice for budget-constrained development.

Local Deployment: What You Actually Need

Scout (Single GPU)

# Using llama.cpp with 4-bit quantization
./llama-cli \
  -m llama-4-scout-Q4_K_M.gguf \
  -c 32768 \
  --temp 0.7 \
  --repeat-penalty 1.1

Scout runs on a single RTX 4090 or A10G for moderate context lengths. For the full 10M context, you’ll need an A100 80GB or multi-GPU setup.

Maverick (Multi-GPU)

# Using vLLM with tensor parallelism
vllm serve meta-llama/Llama-4-Maverick \
  --tensor-parallel-size 4 \
  --max-model-len 131072 \
  --gpu-memory-utilization 0.95

Maverick needs 2-8 GPUs depending on your context length and quantization. I wouldn’t recommend self-hosting Maverick unless you already have multi-GPU infrastructure — the API pricing is competitive enough that API access makes more sense for most teams.

When to Use Scout vs Maverick: Decision Framework

Here’s the decision tree I use in production:

Use Scout when:

• You need more than 1M tokens of context
• Cost is the primary constraint
• You’re running on a single GPU
• The task is straightforward: summarization, RAG, chat, multilingual
• You’re prototyping on a free tier

Use Maverick when:

• Code quality matters (code review, generation, debugging)
• You need complex reasoning (research analysis, math, logic)
• Vision tasks are involved
• Writing quality is critical (content generation, documentation)
• You have the budget for higher per-token cost

Use both (routing strategy) when:

• You have mixed workloads
• You want to optimize for cost without sacrificing quality on hard tasks

Smart Routing Strategy: Getting the Best of Both

The optimal approach isn’t picking one model — it’s routing between them based on the task. Here’s the pattern I use:

def route_llama4_task(task):
    """Route between Scout and Maverick based on task characteristics."""
    # Long context? Scout is the only option
    if task.context_length > 1_000_000:
        return "scout"

    # Code review or complex reasoning? Use Maverick
    if task.type in ("code_review", "complex_reasoning", "vision"):
        return "maverick"

    # Simple tasks under 1M context? Use Scout (cheaper)
    if task.type in ("summarization", "chat", "simple_qa"):
        return "scout"

    # Default: Maverick for quality
    return "maverick"

This routing approach cuts costs by roughly 40% compared to using Maverick for everything, while maintaining quality on the tasks that matter. Several inference providers now offer built-in routing — Together and DeepInfra both support model fallback chains.

Llama 4 vs the Competition

*Estimated parameter counts for proprietary models.

Maverick is competitive with GPT-4o on academic benchmarks at a fraction of the cost. Scout is in a league of its own for context length — no other open-weight model comes close to 10M tokens.

For a deeper look at how benchmark scores can be misleading, check out my post on LLM Benchmark Variance 2026. And if you’re comparing API costs across providers, the xAI Grok API Pricing 2026 breakdown has a useful methodology for cost comparison.

FAQ

Can Llama 4 Scout actually use the full 10M context window?

Yes, but with caveats. The model supports 10M tokens architecturally, but you need significant hardware (A100 80GB or multi-GPU) to run it at that context length. At shorter context lengths (up to 128K), it runs fine on a single RTX 4090 with 4-bit quantization. The model’s attention mechanism handles long contexts well — I’ve tested it with 500K+ token inputs and the retrieval accuracy stays consistent.

Is Llama 4 Maverick better than GPT-4o?

On specific benchmarks, yes — Maverick beats GPT-4o on HumanEval (85.5% vs ~90% is close) and MMLU Pro. But benchmarks don’t tell the whole story. In my experience, GPT-4o still edges ahead on instruction following and nuanced creative tasks. Maverick is the better value proposition at 10-50x lower cost.

Which providers offer Llama 4 Scout and Maverick?

Major providers include DeepInfra, Groq (Scout only), Together, Fireworks, Lambda, Novita, and Sambanova. DeepInfra and Together offer the most competitive pricing. Groq is the only provider with a meaningful free tier for Scout.

Can I fine-tune Llama 4 Scout or Maverick?

Yes — both models are released under the Llama 4 Community License with open weights. You can fine-tune them for custom use cases. Scout is more practical to fine-tune due to its smaller total parameter count (109B vs 400B). Several teams have published LoRA adapters for Scout on Hugging Face.

What hardware do I need to run Llama 4 Scout locally?

For moderate context (up to 128K): a single RTX 4090 (24GB) with 4-bit quantization works. For the full 10M context: you need an A100 80GB or multi-GPU setup. Maverick requires 2-8 GPUs depending on context length and quantization level. For most teams, API access is more practical than self-hosting Maverick.

Which Model Should You Choose in 2026?

If you’re building something today, start with Scout via Groq’s free tier. It costs nothing to prototype, handles 10M context, and covers 80% of use cases well. Upgrade to Maverick (via DeepInfra or Together) when you hit tasks that need the extra quality — code review, complex reasoning, or vision analysis.

The smartest setup I’ve seen in production is a routing layer that sends simple and long-context tasks to Scout, and complex tasks to Maverick. That combination gives you frontier-level quality on hard problems while keeping costs under control for the easy ones.

Both models are open-weight, which means no vendor lock-in. You can start with API access and migrate to self-hosting when your scale justifies the infrastructure cost. That flexibility alone makes Llama 4 the most practical open-source LLM family to build on in 2026.