Model comparison
DeepSeek-V3 vs MiMo-V2-Omni
MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 39.5 on the Noometry Index.
Last verified . 17 shared benchmarks.
DeepSeekSummary
- They share 17 benchmarks with published results for both. DeepSeek-V3 scores higher in 0 categories and MiMo-V2-Omni in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in long context, where MiMo-V2-Omni leads 44.1 to 34.0.
- MiMo-V2-Omni is cheaper at $0.14 / $0.28 per million input/output tokens, against $0.24 / $0.90 for DeepSeek-V3.
- MiMo-V2-Omni accepts more context: 262K tokens versus 164K.
- DeepSeek-V3 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3 | MiMo-V2-Omni | |
|---|---|---|
| Provider | DeepSeek | Xiaomi |
| Noometry Index | 39.5 | 43.6 |
| Released | 2024-12-26 | 2026-03-18 |
| Weights | Open | Proprietary |
| Context window | 164K | 262K |
| Max output | 164K | 131K |
| Input $ / M tokens | $0.24 | $0.14 |
| Output $ / M tokens | $0.90 | $0.28 |
| Results tracked | 60 | 18 |
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Category by category
Coding MiMo-V2-Omni leads
DeepSeek-V3: 42.3 (#106), MiMo-V2-Omni: 43.3 (#89)
| Benchmark | DeepSeek-V3 | MiMo-V2-Omni |
|---|---|---|
| LMArena Coding | 1368 | 1466 |
| Aider Polyglot | 55.1% | — |
| SciCode | 35.8% | — |
| WeirdML | 36.1% | — |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| BigCodeBench Complete | 62.2% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3: —, MiMo-V2-Omni: —
| Benchmark | DeepSeek-V3 | MiMo-V2-Omni |
|---|---|---|
| METR Time Horizons | 49.6% | — |
Reasoning MiMo-V2-Omni leads
DeepSeek-V3: 20.5 (#236), MiMo-V2-Omni: 29.7 (#88)
| Benchmark | DeepSeek-V3 | MiMo-V2-Omni |
|---|---|---|
| LMArena Hard Prompts | 1365 | 1445 |
| SimpleBench | 27.2% | — |
| Kagi LLM Benchmark | 52.3% | — |
| CritPt | 0% | — |
| LiveBench Reasoning | 65.8% | — |
| DTBench | 64.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| LMCA | 15.5% | — |
| BIG-Bench Hard | 87.5% | — |
| Epoch Capabilities Index | 135.94 | — |
| ForecastBench | 59.1 | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |
Math MiMo-V2-Omni leads
DeepSeek-V3: 32.1 (#219), MiMo-V2-Omni: 39.1 (#115)
| Benchmark | DeepSeek-V3 | MiMo-V2-Omni |
|---|---|---|
| LMArena Math | 1373 | 1430 |
| OTIS Mock AIME 2024-2025 | 37.8% | — |
| Omni-MATH | 40.3% | — |
| LiveBench Math | 73.5% | — |
| MATH Level 5 | 75.5% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |
Knowledge MiMo-V2-Omni leads
DeepSeek-V3: 37.5 (#155), MiMo-V2-Omni: 40.5 (#118)
| Benchmark | DeepSeek-V3 | MiMo-V2-Omni |
|---|---|---|
| LMArena Expert | 1351 | 1449 |
| GPQA Diamond | 67.6% | — |
| MMLU-Pro | 72.3% | — |
| Confabulations | 26.1% | — |
| Vectara Hallucination Rate | 6.1% | — |
| GPQA (HELM) | 53.8% | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |
Multimodal Not comparable
DeepSeek-V3: —, MiMo-V2-Omni: 38.6 (#63)
| Benchmark | DeepSeek-V3 | MiMo-V2-Omni |
|---|---|---|
| LMArena Vision | — | 1228 |
Multilingual MiMo-V2-Omni leads
DeepSeek-V3: 48.5 (#143), MiMo-V2-Omni: 51.8 (#102)
| Benchmark | DeepSeek-V3 | MiMo-V2-Omni |
|---|---|---|
| LMArena Non-English | 1358 | 1404 |
| LMArena Chinese | 1391 | 1465 |
| LMArena French | 1385 | 1447 |
| LMArena German | 1374 | 1399 |
| LMArena Japanese | 1333 | 1317 |
| LMArena Korean | 1319 | 1355 |
| LMArena Russian | 1373 | 1412 |
| LMArena Spanish | 1358 | 1434 |
Instruction Following MiMo-V2-Omni leads
DeepSeek-V3: 72.8 (#130), MiMo-V2-Omni: 75.2 (#66)
| Benchmark | DeepSeek-V3 | MiMo-V2-Omni |
|---|---|---|
| LMArena Instruction Following | 1345 | 1428 |
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |
Long Context MiMo-V2-Omni leads
DeepSeek-V3: 34.0 (#253), MiMo-V2-Omni: 44.1 (#76)
| Benchmark | DeepSeek-V3 | MiMo-V2-Omni |
|---|---|---|
| LMArena Longer Query | 1352 | 1442 |
| Fiction.LiveBench | 50% | — |
Writing & Preference MiMo-V2-Omni leads
DeepSeek-V3: 57.4 (#130), MiMo-V2-Omni: 61.4 (#87)
| Benchmark | DeepSeek-V3 | MiMo-V2-Omni |
|---|---|---|
| LMArena Text | 1375 | 1423 |
| LMArena Creative Writing | 1364 | 1392 |
| LMArena Multi-Turn | 1389 | 1445 |
| Short-Story Creative Writing | 77% | — |
| EQ-Bench Creative Writing | 1472 | — |
| WildBench | 83% | — |
| LiveBench Language | 49.1% | — |
Frequently asked questions
Is DeepSeek-V3 better than MiMo-V2-Omni?
MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 39.5 on the Noometry Index.
Which is cheaper, DeepSeek-V3 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-V3 lists at $0.24 and $0.90.
Is DeepSeek-V3 or MiMo-V2-Omni better for coding?
MiMo-V2-Omni scores higher on coding benchmarks: 43.3 versus 42.3 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-V3 and MiMo-V2-Omni share?
17 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and MiMo-V2-Omni has 18.