Llama 4 Scout vs Mistral Small 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 Small 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 Small 3
Mistral Small 3 is a 24B-parameter language model optimized for low-latency performance across common AI tasks. Released under the Apache 2.0 license, it features both pre-trained and instruction-tuned versions designed...
Technical Specifications
| Specification | Llama 4 Scout | Mistral Small 3 |
|---|---|---|
| Provider | Meta | Mistral |
| Context Window | 10,000,000 tokens | 32,768 tokens |
| Agent Suitability | 82/100 | 84/100 |
| Time to First Token (TTFT) | 350 ms | 120 ms |
| Deployment Model | self hostable | managed api |
| Production Stability | beta | stable |
| API Available | Yes | Yes |
| Released Date | 2025-04-05 | 2025-01-30 |
API Pricing Comparison
Input Price per Million Tokens
Llama 4 Scout
$0.10
Mistral Small 3
$0.07
Output Price per Million Tokens
Llama 4 Scout
$0.30
Mistral Small 3
$0.20
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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 Scout Quirks & Gotchas
- ▸10M context causes significant VRAM pressure — recommend 4-bit quantization
- ▸Primarily designed for RAG, not agentic tool calling
Mistral Small 3 Quirks & Gotchas
- ▸Fastest TTFT at lowest cost — ideal for high-volume classification
- ▸Not designed for complex reasoning — route multi-step tasks to Mistral Large 3