Compare / Ministral 3 14B vs Gemma 3 12B
Ministral 3 14B vs Gemma 3 12B
Updated November 2026
Verdict
Ministral 3 14B scores higher on the Small LM Index: 60.0 vs 28.4. On the 2 benchmarks both labs report, Ministral 3 14B wins 2 and Gemma 3 12B wins 0. Both need about 10.6 GB of memory at 4-bit quantization. Gemma 3 12B is cheaper through an API: $0.060 per million tokens against $0.20.
Pick Ministral 3 14B if…
- you want the strongest model overall
- you mostly write code
- you work with long documents
- you need a permissive license (Apache-2.0)
Pick Gemma 3 12B if…
- API cost matters most
Specifications
| Ministral 3 14B | Gemma 3 12B | |
|---|---|---|
| Small LM Index | 60.0 (likely 56–64) | 28.4 (likely 22–36) |
| Lab | Mistral AI · Europe | Google · United States |
| Released | 2025-12-02 | 2025-03-12 |
| Parameters | 14B (dense) | 12.2B (dense) |
| Memory at 4-bit | ~10.6 GB | ~9 GB |
| Context window | 256K tokens | 128K tokens |
| Input | text, image | text, image |
| Reasoning | Hybrid (thinking on/off) | No thinking |
| License | Apache-2.0 | Gemma |
| API price / 1M tokens | $0.20 ($0.20 in, $0.20 out) | $0.060 ($0.040 in, $0.12 out) |
| Hugging Face downloads (30 days) | 294.8K | 359.7K |
Benchmarks
Higher is better. Bold marks the winner where both labs report a score. ≈ marks the index model's estimate for a score that wasn't reported, ✓ an independent measurement and * a score measured by another lab.
| Benchmark | Ministral 3 14B | Gemma 3 12B |
|---|---|---|
| MMLU-Pro Knowledge | ≈ 79 | 60.6 |
| GPQA Diamond Knowledge | 71.2 | 40.9 |
| AIME Math | 85.0 | ≈ 30 |
| LiveCodeBench Coding | 64.6 | 24.6 |
Scores are mostly reported by each lab with its own settings, so small gaps are noise. See the methodology. Open in the interactive comparison to add more models.
Questions
Which is better, Ministral 3 14B or Gemma 3 12B?
Ministral 3 14B scores higher on the Small LM Index: 60.0 vs 28.4. On the 2 benchmarks both labs report, Ministral 3 14B wins 2 and Gemma 3 12B wins 0.
Which is cheaper to run?
Gemma 3 12B is cheaper through an API: $0.060 per million tokens against $0.20. Both need about 10.6 GB of memory at 4-bit quantization.
Which has the longer context window?
Ministral 3 14B has the longer context window: 256K tokens against 128K.
Can I use Ministral 3 14B and Gemma 3 12B commercially?
Ministral 3 14B is released under Apache-2.0 and Gemma 3 12B under Gemma. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.