Compare
Gemma 3 12B Instruct vs Mistral Nemo 12B 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 | Gemma 3 12B Instruct | Mistral Nemo 12B Instruct |
|---|---|---|
| Organization | Mistral AI / NVIDIA | |
| Parameters | 12.2B | 12.2B |
| Architecture | Gemma3 · dense | Mistral · dense |
| Context | 128k | 128k |
| Layers | 48 | 40 |
| Hidden size | 3,840 | 5,120 |
| KV heads | 8 of 16 | 8 of 32 |
| Licence | Gemma Terms of Use | Apache 2.0 |
| Commercial use | Yes | Yes |
| Modalities | text, vision | text |
| Released | 2025-03-12 | 2024-07-18 |
| Recommended quantization | Q8_0 | Q6_K |
| Memory needed | 16 GB | 12 GB |
| Estimated tok/s | 60 | 78 |
| Fit | Good fit | Excellent fit |
| Fine-tune here | Yes | Yes |
| MMLU (reported) | — | 68% |
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
Gemma 3 12B Instruct
Multimodal and long-context in a 12B footprint. Interleaved local/global attention keeps the KV cache affordable.
Strengths
- Reads images as well as text
- 128k context
- Very broad language coverage
Limitations
- Vision path needs runtime support
- Behind Qwen3 on pure reasoning
Mistral Nemo 12B Instruct
A 12B with a 128k context and the Tekken tokenizer, which compresses non-English text far better than Llama’s.
Strengths
- 128k context
- Efficient multilingual tokenizer
- Apache 2.0
Limitations
- Middling reasoning for its size
- Long context needs a large KV cache
Common comparisons