Compare / Mellum2.1 12B-A2.5B Thinking vs Gemma 3 12B
Mellum2.1 12B-A2.5B Thinking vs Gemma 3 12B
Updated November 2026
Verdict
Mellum2.1 12B-A2.5B Thinking scores higher on the Small LM Index: 60.8 vs 29.5. On the 3 benchmarks both labs report, Mellum2.1 12B-A2.5B Thinking wins 3 and Gemma 3 12B wins 0. Both need about 9 GB of memory at 4-bit quantization. Only Gemma 3 12B has a hosted API on OpenRouter ($0.060 per million tokens); the other has to be self-hosted.
Pick Mellum2.1 12B-A2.5B Thinking if…
- you want the strongest model overall
- you mostly write code
- you need math and reasoning
- you work with long documents
- you need a permissive license (Apache-2.0)
Pick Gemma 3 12B if…
- you want a hosted API rather than running it yourself
- you need image input
- you want the more widely used model (more quantized builds and community support)
Specifications
| Mellum2.1 12B-A2.5B Thinking | Gemma 3 12B | |
|---|---|---|
| Small LM Index | 60.8 (likely 58–63) | 29.5 (likely 27–32) |
| Lab | JetBrains (Mellum) · Europe | Google · United States |
| Released | 2026-09-20 | 2025-03-12 |
| Parameters | 12.2B (2.5B active, MoE) | 12.2B (dense) |
| Memory at 4-bit | ~8.3 GB | ~9 GB |
| Context window | 131K tokens | 128K tokens |
| Input | text | text, image |
| Reasoning | Always thinks | No thinking |
| License | Apache-2.0 | Gemma |
| API price / 1M tokens | Self-host only | $0.060 ($0.040 in, $0.12 out) |
| Hugging Face downloads (30 days) | 10.5K | 359.4K |
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 | Mellum2.1 12B-A2.5B Thinking | Gemma 3 12B |
|---|---|---|
| MMLU-Pro Knowledge | ≈ 79 | 59.5✓ |
| GPQA Diamond Knowledge | 64.6 | 34.9✓ |
| Humanity's Last Exam Reasoning | ≈ 10 | 4.2✓ |
| AIME Math | 83.3 | 22.0✓ |
| LiveCodeBench Coding | 82.0 | 13.7✓ |
| SWE-bench Verified Coding | 47.0 | ≈ 36 |
| Terminal-Bench Agentic | 17.4 | — |
| τ²-Bench Agentic | — | 10.8✓ |
| IFBench Instruction following | — | 36.7✓ |
| MMLU Knowledge | 87.8 | — |
| IFEval Instruction following | 90.6 | — |
| BFCL Agentic | 62.3 | — |
| MATH-500 Math | — | 85.3✓ |
| AA-LCR Long context | — | 8.3✓ |
| SciCode Coding | — | 16.4✓ |
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, Mellum2.1 12B-A2.5B Thinking or Gemma 3 12B?
Mellum2.1 12B-A2.5B Thinking scores higher on the Small LM Index: 60.8 vs 29.5. On the 3 benchmarks both labs report, Mellum2.1 12B-A2.5B Thinking wins 3 and Gemma 3 12B wins 0.
Which is cheaper to run?
Only Gemma 3 12B has a hosted API on OpenRouter ($0.060 per million tokens); the other has to be self-hosted. Both need about 9 GB of memory at 4-bit quantization.
Which has the longer context window?
Mellum2.1 12B-A2.5B Thinking has the longer context window: 131K tokens against 128K.
Can I use Mellum2.1 12B-A2.5B Thinking and Gemma 3 12B commercially?
Mellum2.1 12B-A2.5B Thinking 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.