Mistral 7B vs Llama 3.1 8B — the classic tiny local models, revisited. Below is the full side-by-side: specifications, API pricing, context window, local hardware requirements, and a clear, data-driven recommendation on which to pick.
| Spec | Mistral 7B | Llama 3.1 8B |
|---|---|---|
| Developer | Mistral AI | Meta |
| Type | LLM (dense) | LLM (dense) |
| Parameters | 7B | 8B |
| Context window | 32K | 128K |
| Modality | Text → Text | Text → Text |
| License | Apache 2.0 (open) | Llama 3.1 Community (open) |
| Open weights | ✅ Yes | ✅ Yes |
| Input price ($/1M) | $0.02 | $0.02 |
| Output price ($/1M) | $0.03 | $0.03 |
| VRAM (4-bit) | ~4.5 GB | ~5 GB |
| Min GPU (local) | Any 6GB GPU | Any 8GB GPU |
| Released | 2023 | 2024 |
Key differences
- Cost: Mistral 7B and Llama 3.1 8B are priced within ~15% of each other.
- Context: Llama 3.1 8B wins on context window (128K vs 32K) — better for long documents, large codebases and big RAG inputs.
- Openness: both are open-weight, so either can be self-hosted or fine-tuned. Compare their VRAM needs above to see what your GPU can run.
- Run Mistral 7B locally: ~~4.5 GB at 4-bit (min Any 6GB GPU).
- Run Llama 3.1 8B locally: ~~5 GB at 4-bit (min Any 8GB GPU).
Which should you choose?
Choose Mistral 7B if it fits your existing stack or you prefer Mistral AI.
Choose Llama 3.1 8B if you want the lower per-token cost for high-volume workloads, or you need the larger context window.
→ Estimate real costs in the API cost calculator · check local hardware in the VRAM calculator · browse all 30+ models.
All specs and prices are pulled live from our AI models database and kept current. Compare either model against others, or estimate your own monthly spend with the free calculators above.