Llama 3.1 8B vs Mistral Medium 3.5
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 3.1 8B and Mistral Medium 3.5.
Llama 3.1 8B
Llama 3.1 8B is Meta's lightweight open-weight model from the Llama 3.1 generation, optimized for efficient deployment on consumer hardware and edge devices. Despite its compact 8-billion-parameter size, it delivers strong performance on instruction following, text summarization, and lightweight coding tasks. Lllama 3.1 8B is the most downloaded model in the Llama family and runs efficiently on laptops, single GPUs, and CPU via quantization — making it the default choice for on-device AI applications and local prototyping.
Mistral Medium 3.5
Mistral Medium 3.5 is a dense 128B instruction-following model from Mistral AI. It supports text and image inputs with text output, and is designed for agentic workflows, coding, and complex...
Technical Specifications
| Specification | Llama 3.1 8B | Mistral Medium 3.5 |
|---|---|---|
| Provider | Meta | Mistral |
| Context Window | 131,072 tokens | 262,144 tokens |
| Agent Suitability | 74/100 | N/A |
| Time to First Token (TTFT) | 80 ms | N/A |
| Deployment Model | self hostable | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2024-07-23 | 2026-04-30 |
API Pricing Comparison
Input Price per Million Tokens
Llama 3.1 8B
$0.04
Mistral Medium 3.5
$1.50
Output Price per Million Tokens
Llama 3.1 8B
$0.04
Mistral Medium 3.5
$7.50
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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 3.1 8B Quirks & Gotchas
- ▸Perfect for CPU/edge deployment — runs on Raspberry Pi with quantization
- ▸Limited tool calling vs larger models — best for simple classification and chat
Mistral Medium 3.5 Quirks & Gotchas
No developer gotchas reported.