Model comparison
DeepSeek-R1 vs MiMo-V2-Omni
MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 42.3 on the Noometry Index.
Last verified . 17 shared benchmarks.
DeepSeekSummary
- 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 29.7 to 18.6.
- 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.
Side by side
| DeepSeek-R1 | MiMo-V2-Omni | |
|---|---|---|
| Provider | DeepSeek | Xiaomi |
| Noometry Index | 42.3 | 43.6 |
| Released | 2025-01-20 | 2026-03-18 |
| Weights | Proprietary | Proprietary |
| Context window | 164K | 262K |
| Max output | 64K | 131K |
| Input $ / M tokens | $0.50 | $0.14 |
| Output $ / M tokens | $2.15 | $0.28 |
| Results tracked | 52 | 18 |
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Category by category
Coding DeepSeek-R1 leads
DeepSeek-R1: 46.3 (#68), MiMo-V2-Omni: 43.3 (#89)
| Benchmark | DeepSeek-R1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Coding | 1427 | 1466 |
| Aider Polyglot | 71.4% | — |
| SciCode | 35.7% | — |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use Not comparable
DeepSeek-R1: 30.7 (#75), MiMo-V2-Omni: —
| Benchmark | DeepSeek-R1 | MiMo-V2-Omni |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
Reasoning MiMo-V2-Omni leads
DeepSeek-R1: 18.6 (#278), MiMo-V2-Omni: 29.7 (#88)
| Benchmark | DeepSeek-R1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Hard Prompts | 1416 | 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 | 141.29 | — |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math DeepSeek-R1 leads
DeepSeek-R1: 43.8 (#79), MiMo-V2-Omni: 39.1 (#115)
| Benchmark | DeepSeek-R1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Math | 1400 | 1430 |
| OTIS Mock AIME 2024-2025 | 66.4% | — |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |
Knowledge DeepSeek-R1 leads
DeepSeek-R1: 44.5 (#87), MiMo-V2-Omni: 40.5 (#118)
| 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 Not comparable
DeepSeek-R1: —, MiMo-V2-Omni: 38.6 (#63)
| Benchmark | DeepSeek-R1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Vision | — | 1228 |
Multilingual Too close to call
DeepSeek-R1: 52.4 (#85), MiMo-V2-Omni: 51.8 (#102)
| Benchmark | DeepSeek-R1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Non-English | 1412 | 1404 |
| LMArena Chinese | 1442 | 1465 |
| LMArena French | 1417 | 1447 |
| LMArena German | 1404 | 1399 |
| LMArena Japanese | 1391 | 1317 |
| LMArena Korean | 1360 | 1355 |
| LMArena Russian | 1423 | 1412 |
| LMArena Spanish | 1411 | 1434 |
Instruction Following MiMo-V2-Omni leads
DeepSeek-R1: 72.0 (#143), MiMo-V2-Omni: 75.2 (#66)
| Benchmark | DeepSeek-R1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Instruction Following | 1382 | 1428 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
Long Context DeepSeek-R1 leads
DeepSeek-R1: 45.4 (#36), MiMo-V2-Omni: 44.1 (#76)
| Benchmark | DeepSeek-R1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Longer Query | 1391 | 1442 |
| Fiction.LiveBench | 75% | — |
Writing & Preference Too close to call
DeepSeek-R1: 61.4 (#88), MiMo-V2-Omni: 61.4 (#87)
| Benchmark | DeepSeek-R1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Text | 1428 | 1423 |
| LMArena Creative Writing | 1405 | 1392 |
| LMArena Multi-Turn | 1405 | 1445 |
| Short-Story Creative Writing | 83% | — |
| EQ-Bench Creative Writing | 1500 | — |
| WildBench | 82.8% | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than MiMo-V2-Omni?
MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 42.3 on the Noometry Index.
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: 46.3 versus 43.3 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.