Llama 4 MaverickvsLlama 4 Scout
Meta vs Meta — Side-by-side model comparison
Head-to-Head Comparison
| Metric | Llama 4 Maverick | Llama 4 Scout |
|---|---|---|
| Provider | Meta | Meta |
| Arena Rank | #7 | #12 |
| Context Window | 1M | 10M |
| Input Pricing | Free/1M tokens | Free/1M tokens |
| Output Pricing | Free/1M tokens | Free/1M tokens |
| Parameters | 400B MoE (17B active) | 109B (17B active) |
| Open Source | Yes | Yes |
| Best For | Open source, self-hosted, multilingual | Long context, open source, multilingual |
| Release Date | Apr 5, 2025 | Apr 5, 2025 |
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.
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.
Key Differences: Llama 4 Maverick vs Llama 4 Scout
Llama 4 Maverick ranks higher in arena benchmarks (#7) indicating stronger overall performance.
Llama 4 Scout supports a larger context window (10M), allowing it to process longer documents in a single request.
Llama 4 Maverick has 400B MoE (17B active) parameters vs Llama 4 Scout's 109B (17B active), which affects inference speed and capability.
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
When to use Llama 4 Scout
- +You need to process long documents (10M context)
- +Your use case involves long context, open source, multilingual
Cost Analysis
Both models have similar pricing. For a typical enterprise workload processing 100M tokens per month:
Llama 4 Maverick monthly cost
$0
100M tokens/mo (50/50 in/out)
Llama 4 Scout 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