Compare
Mistral Small 24B Instruct vs Llama 3.1 8B Instruct
Same hardware, same arithmetic, side by side. Every memory figure is calculated from published model geometry rather than quoted from a marketing page.
Estimated
Side by side
On GeForce RTX 4090
| Property | Mistral Small 24B Instruct | Llama 3.1 8B Instruct |
|---|---|---|
| Organization | Mistral AI | Meta |
| Parameters | 23.6B | 8B |
| Architecture | Mistral · dense | Llama · dense |
| Context | 32k | 128k |
| Layers | 40 | 32 |
| Hidden size | 5,120 | 4,096 |
| KV heads | 8 of 32 | 8 of 32 |
| Licence | Apache 2.0 | Llama 3.1 Community License |
| Commercial use | Yes | Yes |
| Modalities | text | text |
| Released | 2025-01-30 | 2024-07-23 |
| Recommended quantization | Q4_K_M | Q8_0 |
| Memory needed | 16 GB | 9.5 GB |
| Estimated tok/s | 55 | 92 |
| Fit | Good fit | Excellent fit |
| Fine-tune here | Yes | Yes |
| MMLU (reported) | — | 69.4% |
| HUMANEVAL (reported) | — | 72.6% |
Benchmark rows are figures the model's authors published, not ModelLM measurements, and the two models may not have been evaluated under identical conditions.
Trade-offs
Mistral Small 24B Instruct
Built deliberately for low latency on a single card — fewer layers, wider FFN. Apache 2.0 at a size that usually is not.
Strengths
- Low-latency architecture
- Apache 2.0 at 24B
- Strong function calling
Limitations
- 32k context, short by 2025 standards
- Q4 required on 24 GB cards
Llama 3.1 8B Instruct
The most widely supported open model there is. If a tool, adapter or tutorial exists, it was written for this one first.
Strengths
- Unmatched ecosystem support
- 128k context
- Very stable fine-tuning behaviour
Limitations
- Benchmarks now behind newer 7–9B models
- Community licence with an acceptable-use policy
Common comparisons