Llama 4 Scout vs Mistral Small 3.2 24B
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 3.2 24B.
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 3.2 24B
Mistral-Small-3.2-24B-Instruct-2506 is an updated 24B parameter model from Mistral optimized for instruction following, repetition reduction, and improved function calling. Compared to the 3.1 release, version 3.2 significantly improves accuracy on...
Technical Specifications
| Specification | Llama 4 Scout | Mistral Small 3.2 24B |
|---|---|---|
| Provider | Meta | Mistral |
| Context Window | 10,000,000 tokens | 128,000 tokens |
| Agent Suitability | 82/100 | N/A |
| Time to First Token (TTFT) | 350 ms | N/A |
| Deployment Model | self hostable | self hostable |
| Production Stability | beta | stable |
| API Available | Yes | Yes |
| Released Date | 2025-04-05 | 2025-06-20 |
API Pricing Comparison
Input Price per Million Tokens
Llama 4 Scout
$0.10
Mistral Small 3.2 24B
$0.07
Output Price per Million Tokens
Llama 4 Scout
$0.30
Mistral Small 3.2 24B
$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 3.2 24B Quirks & Gotchas
No developer gotchas reported.