Meta has dropped two groundbreaking models with Llama 4 Scout and Maverick, representing significant advancements in open-source AI technology. Released on April 5, 2026, these models introduce Mixture-of-Experts (MoE) architecture and push the boundaries of what's possible with large language models.
What Makes Llama 4 Special
Llama 4 Scout and Maverick mark Meta's first models with native multimodal training and MoE architecture, a departure from previous single-model approaches. The MoE architecture allows these models to scale efficiently while maintaining performance.
- Scout: 17B active parameters with 16 experts, 109B total parameters
- Context Window: Up to 10 million tokens for Scout
- Performance: Strong benchmark performance against competing models
Key Features and Specifications
Llama 4 Scout
Model Details: - Architecture: Mixture-of-Experts with 16 experts - Active Parameters: 17B active out of 109B total - Context Length: 10 million tokens - Specialization: General-purpose with strong reasoning capabilities
Performance Highlights: - Coding Performance: Solid but not as strong as Maverick - Benchmarks: Outperforms Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1 on standard evaluations - Use Cases: Great for long-context tasks, general reasoning, and applications requiring broad understanding
Llama 4 Maverick
Model Details: - Architecture: Mixture-of-Experts with 128 experts - Active Parameters: Larger specialized architecture - Specialization: Optimized for complex reasoning and coding tasks
Performance Highlights: - Coding: 76.8-80.8 on SWE-bench (varies by evaluation variant) - Reasoning: Strong on mathematical and logical reasoning tasks - Specialization: Best suited for coding, technical applications, and research
Benchmark Performance Comparison
Coding Performance
On coding benchmarks, Llama 4 Maverick shows impressive results: - SWE-bench: 76.8-80.8 (range depends on evaluation variant) - LiveCodeBench: Strong performance, though slightly behind Gemma 4
Reasoning and Math
Compared to other models: - AIME 2026: Llama 4 Maverick vs. Gemma 4 (89.2% vs. 88.3% for Gemma 4) - GPQA Diamond: Llama 4 Maverick vs. Gemma 4 (84.3% vs. 82.3% for Llama 4) - Reasoning: Strong performance across complex reasoning tasks
Comparison with Competitors
Gemma 4 (Google):
- Math: AIME 2026 - 89.2% vs. 88.3% for Maverick
- Reasoning: GPQA Diamond - 84.3% vs. 82.3% for Maverick
- Coding: LiveCodeBench - 80.0% vs. 77.1% for Maverick
- License: Apache 2.0 (more permissive) vs. Llama Community License
Qwen 3.6 Plus: - SWE-bench: 78.8% (outperforms Llama 4 Maverick on coding) - Context: Standard context windows vs. Scout's 10M tokens
Available Variants
Meta has released multiple variants of both models:
Scout Variants
meta-llama/Llama-4-Scout-17B-16E-Instruct- 196K+ downloadsmeta-llama/Llama-4-Scout-17B-16E- Base versionRedHatAI/Llama-4-Scout-17B-16E-Instruct-quantized.w4a16- Quantizednvidia/Llama-4-Scout-17B-16E-Instruct-FP8- FP8 precisionunsloth/Llama-4-Scout-17B-16E-Instruct-GGUF- GGUF format
Maverick Variants
meta-llama/Llama-4-Maverick-17B-128E-Instruct- 16K+ downloadsmeta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8- 62K+ downloads- Additional quantized and optimized versions
Hardware Requirements
Scout Requirements
- Memory: 20-40GB depending on quantization
- GPU: Recommended for optimal performance
- Inference Cost: ~$0.19/Mtoken for 3:1 blended usage
Maverick Requirements
- Memory: Higher requirements due to larger MoE architecture
- GPU: More computational resources needed
- Deployment: Better suited for cloud deployment or high-end setups
Licensing Considerations
Llama 4 Community License is not true open source: - MAU Limit: Free for companies under 700 million monthly active users - Restrictions: Comes with usage limitations compared to Apache 2.0 licenses - Comparison: More restrictive than Gemma 4's Apache 2.0 license
Practical Applications
For Developers
Choose Scout when: - You need long-context understanding (up to 10M tokens) - General-purpose reasoning is sufficient - Resource constraints are a consideration - You need a balance of performance and efficiency
Choose Maverick when: - Coding and technical tasks are primary focus - Highest reasoning performance is required - You have sufficient computational resources - You need the absolute best performance on complex tasks
For Enterprises
Considerations before deployment: - MAU limits may affect business usage - Hardware requirements and costs - Support and maintenance needs - Integration with existing AI infrastructure
Future Outlook
Llama 4 Scout and Maverick represent significant steps forward in open-source AI. The MoE architecture is likely to become standard for future models, and the performance gap with proprietary models continues to narrow.
With proper optimization and deployment, these models could become the foundation for many AI applications, particularly in research and development environments.
Conclusion
Meta's Llama 4 models demonstrate the continued progress in open-source AI. While they have some limitations compared to Apache 2.0 licensed alternatives, their performance and architectural innovations make them important additions to the open-source ecosystem.
For developers and organizations, the choice between Scout and Maverick depends on specific use cases, resource constraints, and performance requirements. Both models represent significant advancements that will shape the future of open-source AI.
What are your thoughts on Llama 4 Scout and Maverick? Have you tried them yet? Share your experiences in the comments below!