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Llama 4 ScoutvsLlama 4 Maverick

Meta vs Meta — Side-by-side model comparison

Llama 4 Maverick leads 2/5 categories

Head-to-Head Comparison

MetricLlama 4 ScoutLlama 4 Maverick
Provider
Meta
Meta
Arena Rank
#12
#7
Context Window
10M
1M
Input Pricing
Free/1M tokens
Free/1M tokens
Output Pricing
Free/1M tokens
Free/1M tokens
Parameters
109B (17B active)
400B MoE (17B active)
Open Source
Yes
Yes
Best For
Long context, open source, multilingual
Open source, self-hosted, multilingual
Release Date
Apr 5, 2025
Apr 5, 2025

Llama 4 Scout

Llama 4 Scout, developed by Meta AI, is a Mixture-of-Experts model designed for efficient deployment with strong performance across general reasoning, coding, and multilingual tasks. The model uses sparse expert routing to maintain high capability while reducing inference compute requirements. As part of Meta's Llama 4 family, Scout represents the efficiency-optimized variant, targeting developers who need capable AI at manageable computational costs. The model supports long context processing and demonstrates improved instruction following compared to Llama 3 series models. Free and open-source under Meta's license, it can be deployed on enterprise hardware without API costs. Llama 4 Scout continues Meta's commitment to open-source AI development, providing the community with a model that balances capability and deployment practicality for production applications at scale.

Llama 4 Maverick

Llama 4 Maverick, developed by Meta AI, is a large Mixture-of-Experts model representing the most capable freely available AI for general-purpose tasks. As Meta's flagship open-source release, Maverick demonstrates strong performance across coding, reasoning, creative writing, and multilingual tasks, competing with proprietary models on standard benchmarks. The MoE architecture activates only a subset of its total parameters per token, enabling frontier-class capability with manageable inference costs. It can be downloaded, modified, fine-tuned, and deployed without API costs or licensing restrictions. The model has become a foundation for thousands of fine-tuned variants across the open-source community, powering applications in healthcare, education, content creation, and enterprise software. Llama 4 Maverick reflects Meta's strategic investment in open-source AI, building developer ecosystem engagement while advancing the accessibility of powerful AI models globally.

Key Differences: Llama 4 Scout vs Llama 4 Maverick

1

Llama 4 Maverick ranks higher in arena benchmarks (#7) indicating stronger overall performance.

2

Llama 4 Scout supports a larger context window (10M), allowing it to process longer documents in a single request.

3

Llama 4 Scout has 109B (17B active) parameters vs Llama 4 Maverick's 400B MoE (17B active), which affects inference speed and capability.

L

When to use Llama 4 Scout

  • +You need to process long documents (10M context)
  • +Your use case involves long context, open source, multilingual
View full Llama 4 Scout specs →
L

When to use Llama 4 Maverick

  • +You need the highest quality output based on arena rankings
  • +Your use case involves open source, self-hosted, multilingual
View full Llama 4 Maverick specs →

Cost Analysis

Both models have similar pricing. For a typical enterprise workload processing 100M tokens per month:

Llama 4 Scout monthly cost

$0

100M tokens/mo (50/50 in/out)

Llama 4 Maverick monthly cost

$0

100M tokens/mo (50/50 in/out)

The Verdict

Llama 4 Maverick wins our head-to-head comparison with 2 out of 5 category wins. It's the stronger choice for open source, self-hosted, multilingual, though Llama 4 Scout holds an edge in long context, open source, multilingual.

Last compared: April 2026 · Data sourced from public benchmarks and official pricing pages

Frequently Asked Questions

Which is better, Llama 4 Scout or Llama 4 Maverick?
In our head-to-head comparison, Llama 4 Maverick leads in 2 out of 5 categories (arena rank, context window, input pricing, output pricing, and parameters). Llama 4 Maverick excels at open source, self-hosted, multilingual, while Llama 4 Scout is better suited for long context, open source, multilingual. The best choice depends on your specific requirements, budget, and use case.
How does Llama 4 Scout pricing compare to Llama 4 Maverick?
Llama 4 Scout charges Free per 1M input tokens and Free per 1M output tokens. Llama 4 Maverick charges Free per 1M input tokens and Free per 1M output tokens. For high-volume production workloads, the pricing difference can significantly impact total cost of ownership.
What is the context window difference between Llama 4 Scout and Llama 4 Maverick?
Llama 4 Scout supports a 10M token context window, while Llama 4 Maverick supports 1M tokens. Llama 4 Scout can process longer documents, codebases, and conversations in a single request. Context window size matters most for tasks involving long documents, large codebases, or extended conversations.
Can I use Llama 4 Scout or Llama 4 Maverick for free?
Llama 4 Scout is available for free (open-source). Llama 4 Maverick is available for free (open-source). Open-source models can be self-hosted for free but require your own GPU infrastructure.
Which model has better benchmarks, Llama 4 Scout or Llama 4 Maverick?
Llama 4 Scout holds arena rank #12, while Llama 4 Maverick holds rank #7. Llama 4 Maverick performs better in overall arena benchmarks, which aggregate human preference ratings across coding, reasoning, and general tasks. Note that benchmarks don't capture every use case — we recommend testing both models on your specific tasks.
Is Llama 4 Scout or Llama 4 Maverick better for coding?
Llama 4 Scout's primary strength is long context, open source, multilingual. Llama 4 Maverick's primary strength is open source, self-hosted, multilingual. For coding specifically, arena rank and code-specific benchmarks are the best indicators of performance.