# DeepSeek-V3.1 vs MiMo-V2-Pro

> DeepSeek-V3.1 and MiMo-V2-Pro score almost the same on the Noometry Index (42.8 vs 43.0), so choose on price, context window or the category you care about most.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-vs-mimo-v2-pro
- Last updated: 2026-10-11
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 2 categories and MiMo-V2-Pro in 6 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek-V3.1 leads 27.9 to 22.1.
- DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $0.43 / $0.87 for MiMo-V2-Pro.
- MiMo-V2-Pro accepts more context: 1.05M tokens versus 164K.
- DeepSeek-V3.1 has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-V3.1 | MiMo-V2-Pro |
|---|---|---|
| Provider | DeepSeek | Xiaomi |
| Noometry Index | 42.8 | 43.0 |
| Rank | 108 | 103 |
| Context | 164K | 1.05M |
| Input $/M | $0.25 | $0.43 |
| Output $/M | $0.95 | $0.87 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3.1: 40.3 (#144)
- MiMo-V2-Pro: 43.8 (#83)

| Benchmark | DeepSeek-V3.1 | MiMo-V2-Pro |
|---|---|---|
| LMArena Coding | 1417 | 1476 |
| LMArena WebDev | — | 1433 |
| WeirdML | 38.4% | — |
| ALE-Bench | — | 785.17 |

## Reasoning

- DeepSeek-V3.1: 27.9 (#110)
- MiMo-V2-Pro: 22.1 (#206)

| Benchmark | DeepSeek-V3.1 | MiMo-V2-Pro |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1457 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| NYT Connections (extended) | — | 25.8% |
| Thematic Generalization | — | 45.9% |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| Epoch Capabilities Index | 139.92 | — |
| ForecastBench | 58 | — |

## Math

- DeepSeek-V3.1: 38.9 (#122)
- MiMo-V2-Pro: 39.5 (#102)

| Benchmark | DeepSeek-V3.1 | MiMo-V2-Pro |
|---|---|---|
| LMArena Math | 1420 | 1447 |

## Knowledge

- DeepSeek-V3.1: 43.7 (#90)
- MiMo-V2-Pro: 41.4 (#111)

| Benchmark | DeepSeek-V3.1 | MiMo-V2-Pro |
|---|---|---|
| LMArena Expert | 1405 | 1478 |
| Vectara Hallucination Rate | 5.5% | — |

## Multilingual

- DeepSeek-V3.1: 51.6 (#106)
- MiMo-V2-Pro: 52.7 (#81)

| Benchmark | DeepSeek-V3.1 | MiMo-V2-Pro |
|---|---|---|
| LMArena Non-English | 1400 | 1416 |
| LMArena Chinese | 1469 | 1456 |
| LMArena French | 1447 | 1469 |
| LMArena German | 1411 | 1417 |
| LMArena Japanese | 1378 | 1366 |
| LMArena Korean | 1337 | 1400 |
| LMArena Russian | 1405 | 1427 |
| LMArena Spanish | 1431 | 1457 |

## Instruction Following

- DeepSeek-V3.1: 73.9 (#110)
- MiMo-V2-Pro: 76.0 (#49)

| Benchmark | DeepSeek-V3.1 | MiMo-V2-Pro |
|---|---|---|
| LMArena Instruction Following | 1400 | 1445 |

## Long Context

- DeepSeek-V3.1: 36.3 (#232)
- MiMo-V2-Pro: 41.5 (#138)

| Benchmark | DeepSeek-V3.1 | MiMo-V2-Pro |
|---|---|---|
| LMArena Longer Query | 1422 | 1455 |
| Fiction.LiveBench | 52.8% | — |
| CL-bench | — | 15.7% |
| CL-bench Life | — | 6.9% |

## Writing & Preference

- DeepSeek-V3.1: 60.3 (#98)
- MiMo-V2-Pro: 62.8 (#70)

| Benchmark | DeepSeek-V3.1 | MiMo-V2-Pro |
|---|---|---|
| LMArena Text | 1420 | 1436 |
| LMArena Creative Writing | 1401 | 1415 |
| LMArena Multi-Turn | 1408 | 1456 |
| EQ-Bench Creative Writing | 1436 | — |

## FAQ

### Is DeepSeek-V3.1 better than MiMo-V2-Pro?

DeepSeek-V3.1 and MiMo-V2-Pro score almost the same on the Noometry Index (42.8 vs 43.0), so choose on price, context window or the category you care about most.

### Which is cheaper, DeepSeek-V3.1 or MiMo-V2-Pro?

DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; MiMo-V2-Pro lists at $0.43 and $0.87.

### Is DeepSeek-V3.1 or MiMo-V2-Pro better for coding?

MiMo-V2-Pro scores higher on coding benchmarks: 43.8 versus 40.3 in the Noometry coding category.

### Which has the bigger context window?

MiMo-V2-Pro does, with 1.05M tokens against 164K.

### How many benchmarks do DeepSeek-V3.1 and MiMo-V2-Pro share?

17 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and MiMo-V2-Pro has 23.