Compare / MiMo-7B-RL vs DeepSeek-R1-Distill-Qwen-7B
MiMo-7B-RL vs DeepSeek-R1-Distill-Qwen-7B
Updated November 2026
Verdict
MiMo-7B-RL and DeepSeek-R1-Distill-Qwen-7B are roughly tied on the Small LM Index (40.7 vs 39.8). On the 3 benchmarks both labs report, MiMo-7B-RL wins 2 and DeepSeek-R1-Distill-Qwen-7B wins 1. Both need about 6.6 GB of memory at 4-bit quantization. Neither model has a hosted API on OpenRouter, so you would run both yourself.
Pick MiMo-7B-RL if…
- you mostly write code
Pick DeepSeek-R1-Distill-Qwen-7B if…
- you need math and reasoning
- you work with long documents
- you want the more widely used model (more quantized builds and community support)
Specifications
| MiMo-7B-RL | DeepSeek-R1-Distill-Qwen-7B | |
|---|---|---|
| Small LM Index | 40.7 (likely 36–45) | 39.8 (likely 35–45) |
| Lab | Xiaomi (MiMo) · China | DeepSeek · China |
| Released | 2025-04-30 | 2025-01-20 |
| Parameters | 7.8B (dense) | 7.6B (dense) |
| Memory at 4-bit | ~6.6 GB | ~5.7 GB |
| Context window | 32K tokens | 128K tokens |
| Input | text | text |
| Reasoning | Always thinks | Always thinks |
| License | MIT | MIT |
| API price / 1M tokens | Self-host only | Self-host only |
| Hugging Face downloads (30 days) | 31.9K | 321.5K |
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 | MiMo-7B-RL | DeepSeek-R1-Distill-Qwen-7B |
|---|---|---|
| MMLU-Pro Knowledge | 58.6 | ≈ 65 |
| GPQA Diamond Knowledge | 54.4 | 49.1 |
| AIME Math | 55.4 | 55.5 |
| LiveCodeBench Coding | 49.3 | 37.6 |
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, MiMo-7B-RL or DeepSeek-R1-Distill-Qwen-7B?
MiMo-7B-RL and DeepSeek-R1-Distill-Qwen-7B are roughly tied on the Small LM Index (40.7 vs 39.8). On the 3 benchmarks both labs report, MiMo-7B-RL wins 2 and DeepSeek-R1-Distill-Qwen-7B wins 1.
Which is cheaper to run?
Neither model has a hosted API on OpenRouter, so you would run both yourself. Both need about 6.6 GB of memory at 4-bit quantization.
Which has the longer context window?
DeepSeek-R1-Distill-Qwen-7B has the longer context window: 128K tokens against 32K.
Can I use MiMo-7B-RL and DeepSeek-R1-Distill-Qwen-7B commercially?
Both are released under the MIT license. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.
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