Model comparison
DeepSeek-V3 vs MiMo-V2.6-Pro
MiMo-V2.6-Pro is the stronger model overall, scoring 50.3 to 39.5 on the Noometry Index.
Last verified . 14 shared benchmarks.
DeepSeekSummary
- They share 14 benchmarks with published results for both. DeepSeek-V3 scores higher in 0 categories and MiMo-V2.6-Pro in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where MiMo-V2.6-Pro leads 43.1 to 20.5.
- The biggest single-benchmark swing is CritPt: 0% for DeepSeek-V3 and 26.6% for MiMo-V2.6-Pro.
- DeepSeek-V3 is cheaper at $0.24 / $0.90 per million input/output tokens, against $0.43 / $0.87 for MiMo-V2.6-Pro.
- MiMo-V2.6-Pro accepts more context: 1.05M tokens versus 164K.
Side by side
| DeepSeek-V3 | MiMo-V2.6-Pro | |
|---|---|---|
| Provider | DeepSeek | Xiaomi |
| Noometry Index | 39.5 | 50.3 |
| Released | 2024-12-26 | 2026-09-21 |
| Weights | Open | Open |
| Context window | 164K | 1.05M |
| Max output | 164K | 131K |
| Input $ / M tokens | $0.24 | $0.43 |
| Output $ / M tokens | $0.90 | $0.87 |
| Results tracked | 60 | 19 |
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Category by category
Coding MiMo-V2.6-Pro leads
DeepSeek-V3: 42.3 (#106), MiMo-V2.6-Pro: 55.5 (#23)
| Benchmark | DeepSeek-V3 | MiMo-V2.6-Pro |
|---|---|---|
| SciCode | 35.8% | 60.9% |
| LMArena Coding | 1368 | 1534 |
| Aider Polyglot | 55.1% | — |
| LMArena WebDev | — | 1629 |
| WeirdML | 36.1% | — |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| BigCodeBench Complete | 62.2% | — |
| ALE-Bench | — | 1,158 |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3: —, MiMo-V2.6-Pro: 37.5 (#35)
| Benchmark | DeepSeek-V3 | MiMo-V2.6-Pro |
|---|---|---|
| APEX-Agents | — | 59.5% |
| METR Time Horizons | 49.6% | — |
Reasoning MiMo-V2.6-Pro leads
DeepSeek-V3: 20.5 (#236), MiMo-V2.6-Pro: 43.1 (#50)
| Benchmark | DeepSeek-V3 | MiMo-V2.6-Pro |
|---|---|---|
| CritPt | 0% | 26.6% |
| LMArena Hard Prompts | 1365 | 1512 |
| SimpleBench | 27.2% | — |
| Kagi LLM Benchmark | 52.3% | — |
| 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-V2.6-Pro leads
DeepSeek-V3: 32.1 (#219), MiMo-V2.6-Pro: 54.5 (#45)
| Benchmark | DeepSeek-V3 | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Math | 1373 | 1494 |
| OTIS Mock AIME 2024-2025 | 37.8% | — |
| ProofBench | — | 70% |
| Omni-MATH | 40.3% | — |
| LiveBench Math | 73.5% | — |
| MATH Level 5 | 75.5% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |
Knowledge MiMo-V2.6-Pro leads
DeepSeek-V3: 37.5 (#155), MiMo-V2.6-Pro: 43.5 (#92)
| Benchmark | DeepSeek-V3 | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Expert | 1351 | 1543 |
| 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-V2.6-Pro: 40.8 (#43)
| Benchmark | DeepSeek-V3 | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Vision | — | 1264 |
Multilingual MiMo-V2.6-Pro leads
DeepSeek-V3: 48.5 (#143), MiMo-V2.6-Pro: 56.9 (#14)
| Benchmark | DeepSeek-V3 | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Non-English | 1358 | 1474 |
| LMArena Chinese | 1391 | 1529 |
| LMArena Russian | 1373 | 1480 |
| LMArena French | 1385 | — |
| LMArena German | 1374 | — |
| LMArena Japanese | 1333 | — |
| LMArena Korean | 1319 | — |
| LMArena Spanish | 1358 | — |
Instruction Following MiMo-V2.6-Pro leads
DeepSeek-V3: 72.8 (#130), MiMo-V2.6-Pro: 78.2 (#12)
| Benchmark | DeepSeek-V3 | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Instruction Following | 1345 | 1493 |
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |
Long Context MiMo-V2.6-Pro leads
DeepSeek-V3: 34.0 (#253), MiMo-V2.6-Pro: 46.0 (#27)
| Benchmark | DeepSeek-V3 | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Longer Query | 1352 | 1501 |
| Fiction.LiveBench | 50% | — |
Writing & Preference MiMo-V2.6-Pro leads
DeepSeek-V3: 57.4 (#130), MiMo-V2.6-Pro: 66.8 (#33)
| Benchmark | DeepSeek-V3 | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Text | 1375 | 1492 |
| LMArena Creative Writing | 1364 | 1468 |
| LMArena Multi-Turn | 1389 | 1464 |
| 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-V2.6-Pro?
MiMo-V2.6-Pro is the stronger model overall, scoring 50.3 to 39.5 on the Noometry Index.
Which is cheaper, DeepSeek-V3 or MiMo-V2.6-Pro?
DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; MiMo-V2.6-Pro lists at $0.43 and $0.87.
Is DeepSeek-V3 or MiMo-V2.6-Pro better for coding?
MiMo-V2.6-Pro scores higher on coding benchmarks: 55.5 versus 42.3 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2.6-Pro does, with 1.05M tokens against 164K.
How many benchmarks do DeepSeek-V3 and MiMo-V2.6-Pro share?
14 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and MiMo-V2.6-Pro has 19.