Llama 4 Maverick vs Llama 4 Scout
How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Llama 4 Maverick and Llama 4 Scout.
Llama 4 Maverick
Meta's next-generation open weights model. Delivers premium agentic capabilities, reasoning, and tool call compliance for local or self-hosted enterprise stacks.
Llama 4 Scout
Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...
Technical Specifications
| Specification | Llama 4 Maverick | Llama 4 Scout |
|---|---|---|
| Provider | Meta | Meta |
| Context Window | 1,048,576 tokens | 10,000,000 tokens |
| Agent Suitability | 89/100 | 82/100 |
| Time to First Token (TTFT) | 300 ms | 350 ms |
| Deployment Model | self hostable | self hostable |
| Production Stability | stable | beta |
| API Available | Yes | Yes |
| Released Date | 2026-05-25 | 2025-04-05 |
API Pricing Comparison
Input Price per Million Tokens
Llama 4 Maverick
$0.15
Llama 4 Scout
$0.10
Output Price per Million Tokens
Llama 4 Maverick
$0.60
Llama 4 Scout
$0.30
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Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
Llama 4 Maverick Quirks & Gotchas
- ▸Self-hostable via Ollama/Docker — ideal for on-premise deployments
- ▸Requires specific system prompt for optimal function calling reliability
Llama 4 Scout Quirks & Gotchas
- ▸10M context causes significant VRAM pressure — recommend 4-bit quantization
- ▸Primarily designed for RAG, not agentic tool calling