Llama 4 Scout vs Mistral Large 3
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 Scout and Mistral Large 3.
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...
Mistral Large 3
Mistral's flagship commercial model, boasting multilingual support and advanced coding and math skills. Designed for complex reasoning and enterprise tasks that require high compliance.
Technical Specifications
| Specification | Llama 4 Scout | Mistral Large 3 |
|---|---|---|
| Provider | Meta | Mistral |
| Context Window | 10,000,000 tokens | 262,144 tokens |
| Agent Suitability | 82/100 | 91/100 |
| Time to First Token (TTFT) | 350 ms | 250 ms |
| Deployment Model | self hostable | managed api |
| Production Stability | beta | stable |
| API Available | Yes | Yes |
| Released Date | 2025-04-05 | 2024-07-24 |
API Pricing Comparison
Input Price per Million Tokens
Llama 4 Scout
$0.10
Mistral Large 3
$0.50
Output Price per Million Tokens
Llama 4 Scout
$0.30
Mistral Large 3
$1.50
Want to test both models live?
Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.
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 Scout Quirks & Gotchas
- ▸10M context causes significant VRAM pressure — recommend 4-bit quantization
- ▸Primarily designed for RAG, not agentic tool calling
Mistral Large 3 Quirks & Gotchas
- ▸Top-tier structured JSON output — best multilingual function calling
- ▸Le Chat offers free consumer tier with same underlying model