Compare / Mellum2.1 12B-A2.5B Thinking vs Gemma 4 12B
Mellum2.1 12B-A2.5B Thinking vs Gemma 4 12B
Updated November 2026
Verdict
Mellum2.1 12B-A2.5B Thinking scores higher on the Small LM Index: 60.8 vs 58.4, although their likely ranges overlap, so the lead is not certain. On the 3 benchmarks both labs report, Mellum2.1 12B-A2.5B Thinking wins 2 and Gemma 4 12B wins 1. Both need about 8.9 GB of memory at 4-bit quantization. Neither model has a hosted API on OpenRouter, so you would run both yourself.
Pick Mellum2.1 12B-A2.5B Thinking if…
- you want the strongest model overall
- you mostly write code
- you need math and reasoning
Pick Gemma 4 12B if…
- you work with long documents
- 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 4 12B | |
|---|---|---|
| Small LM Index | 60.8 (likely 58–63) | 58.4 (likely 56–61) |
| Lab | JetBrains (Mellum) · Europe | Google · United States |
| Released | 2026-09-20 | 2026-06-03 |
| Parameters | 12.2B (2.5B active, MoE) | 11.9B (dense) |
| Memory at 4-bit | ~8.3 GB | ~8.9 GB |
| Context window | 131K tokens | 256K tokens |
| Input | text | text, image, audio |
| Reasoning | Always thinks | Hybrid (thinking on/off) |
| License | Apache-2.0 | Apache-2.0 |
| API price / 1M tokens | Self-host only | Self-host only |
| Hugging Face downloads (30 days) | 10.5K | 1.8M |
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 4 12B |
|---|---|---|
| MMLU-Pro Knowledge | ≈ 79 | 77.2 |
| GPQA Diamond Knowledge | 64.6 | 75.3✓ |
| Humanity's Last Exam Reasoning | ≈ 10 | 15.7✓ |
| AIME Math | 83.3 | 77.5 |
| LiveCodeBench Coding | 82.0 | 72.0 |
| SWE-bench Verified Coding | 47.0 | ≈ 52 |
| Terminal-Bench Agentic | 17.4 | — |
| τ²-Bench Agentic | — | 36.3✓ |
| IFBench Instruction following | — | 73.5✓ |
| MMMU-Pro Vision | — | 69.1 |
| MMLU Knowledge | 87.8 | — |
| IFEval Instruction following | 90.6 | — |
| BFCL Agentic | 62.3 | — |
| AA-LCR Long context | — | 63.7✓ |
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 4 12B?
Mellum2.1 12B-A2.5B Thinking scores higher on the Small LM Index: 60.8 vs 58.4, although their likely ranges overlap, so the lead is not certain. On the 3 benchmarks both labs report, Mellum2.1 12B-A2.5B Thinking wins 2 and Gemma 4 12B wins 1.
Which is cheaper to run?
Neither model has a hosted API on OpenRouter, so you would run both yourself. Both need about 8.9 GB of memory at 4-bit quantization.
Which has the longer context window?
Gemma 4 12B has the longer context window: 256K tokens against 131K.
Can I use Mellum2.1 12B-A2.5B Thinking and Gemma 4 12B commercially?
Both are released under the Apache-2.0 license. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.