# DeepSeek-R1 vs MiMo-V2-Omni

> DeepSeek-R1 and MiMo-V2-Omni score almost the same on the Noometry Index (42.0 vs 42.9), so choose on price, context window or the category you care about most.

- Canonical page: https://noometry.com/compare/deepseek-r1-vs-mimo-v2-omni
- Last updated: 2026-10-10
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. DeepSeek-R1 scores higher in 5 categories and MiMo-V2-Omni in 3 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where MiMo-V2-Omni leads 28.6 to 18.7.
- MiMo-V2-Omni is cheaper at $0.14 / $0.28 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
- MiMo-V2-Omni accepts more context: 262K tokens versus 164K.

## Snapshot

| | DeepSeek-R1 | MiMo-V2-Omni |
|---|---|---|
| Provider | DeepSeek | Xiaomi |
| Noometry Index | 42.0 | 42.9 |
| Rank | 110 | 91 |
| Context | 164K | 262K |
| Input $/M | $0.50 | $0.14 |
| Output $/M | $2.15 | $0.28 |
| Weights | Proprietary | Proprietary |

## Coding

- DeepSeek-R1: 45.9 (#63)
- MiMo-V2-Omni: 42.7 (#87)

| Benchmark | DeepSeek-R1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Coding | 1427 | 1467 |
| Aider Polyglot | 71.4% | — |
| SciCode | 35.7% | — |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |

## Agentic & Tool Use

- DeepSeek-R1: 30.4 (#68)
- MiMo-V2-Omni: —

| Benchmark | DeepSeek-R1 | MiMo-V2-Omni |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |

## Reasoning

- DeepSeek-R1: 18.7 (#253)
- MiMo-V2-Omni: 28.6 (#86)

| Benchmark | DeepSeek-R1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Hard Prompts | 1415 | 1445 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| Kagi LLM Benchmark | 69.4% | — |
| ARC-AGI-1 | 21.2% | — |
| CritPt | 1.1% | — |
| LiveBench Reasoning | 83.2% | — |
| LiveBench Data Analysis | 69.8% | — |
| Epoch Capabilities Index | 141.3 | — |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |

## Math

- DeepSeek-R1: 42.0 (#70)
- MiMo-V2-Omni: 36.4 (#112)

| Benchmark | DeepSeek-R1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Math | 1401 | 1432 |
| OTIS Mock AIME 2024-2025 | 66.4% | — |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |

## Knowledge

- DeepSeek-R1: 44.3 (#81)
- MiMo-V2-Omni: 39.9 (#114)

| Benchmark | DeepSeek-R1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Expert | 1394 | 1449 |
| GPQA Diamond | 76.3% | — |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |

## Multimodal

- DeepSeek-R1: —
- MiMo-V2-Omni: 37.9 (#62)

| Benchmark | DeepSeek-R1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Vision | — | 1228 |

## Multilingual

- DeepSeek-R1: 52.6 (#80)
- MiMo-V2-Omni: 52.0 (#97)

| Benchmark | DeepSeek-R1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Non-English | 1412 | 1404 |
| LMArena Chinese | 1443 | 1469 |
| LMArena French | 1415 | 1448 |
| LMArena German | 1405 | 1398 |
| LMArena Japanese | 1394 | 1318 |
| LMArena Korean | 1359 | 1355 |
| LMArena Russian | 1423 | 1412 |
| LMArena Spanish | 1410 | 1432 |

## Instruction Following

- DeepSeek-R1: 72.2 (#138)
- MiMo-V2-Omni: 75.4 (#62)

| Benchmark | DeepSeek-R1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Instruction Following | 1382 | 1428 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |

## Long Context

- DeepSeek-R1: 45.4 (#36)
- MiMo-V2-Omni: 44.1 (#72)

| Benchmark | DeepSeek-R1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Longer Query | 1391 | 1442 |
| Fiction.LiveBench | 75% | — |

## Writing & Preference

- DeepSeek-R1: 61.5 (#84)
- MiMo-V2-Omni: 61.6 (#83)

| Benchmark | DeepSeek-R1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Text | 1428 | 1424 |
| LMArena Creative Writing | 1406 | 1393 |
| LMArena Multi-Turn | 1404 | 1446 |
| Short-Story Creative Writing | 83% | — |
| EQ-Bench Creative Writing | 1500 | — |
| WildBench | 82.8% | — |
| LiveBench Language | 48.5% | — |

## FAQ

### Is DeepSeek-R1 better than MiMo-V2-Omni?

DeepSeek-R1 and MiMo-V2-Omni score almost the same on the Noometry Index (42.0 vs 42.9), so choose on price, context window or the category you care about most.

### Which is cheaper, DeepSeek-R1 or MiMo-V2-Omni?

MiMo-V2-Omni is cheaper. It lists at $0.14 per million input tokens and $0.28 per million output tokens; DeepSeek-R1 lists at $0.50 and $2.15.

### Is DeepSeek-R1 or MiMo-V2-Omni better for coding?

DeepSeek-R1 scores higher on coding benchmarks: 45.9 versus 42.7 in the Noometry coding category.

### Which has the bigger context window?

MiMo-V2-Omni does, with 262K tokens against 164K.

### How many benchmarks do DeepSeek-R1 and MiMo-V2-Omni share?

17 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and MiMo-V2-Omni has 18.