Model comparison
DeepSeek V4.1 Flash vs MiMo-V2-Pro
DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 43.0 on the Noometry Index.
Last verified . 20 shared benchmarks.
DeepSeekSummary
- They share 20 benchmarks with published results for both. DeepSeek V4.1 Flash scores higher in 8 categories and MiMo-V2-Pro in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4.1 Flash leads 50.2 to 22.1.
- The biggest single-benchmark swing is NYT Connections (extended): 89.6% for DeepSeek V4.1 Flash and 25.8% for MiMo-V2-Pro.
- DeepSeek V4.1 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.43 / $0.87 for MiMo-V2-Pro.
- MiMo-V2-Pro accepts more context: 1.05M tokens versus 1M.
- DeepSeek V4.1 Flash has downloadable open weights; the other is API-only.
Side by side
| DeepSeek V4.1 Flash | MiMo-V2-Pro | |
|---|---|---|
| Provider | DeepSeek | Xiaomi |
| Noometry Index | 52.8 | 43.0 |
| Released | 2026-09-09 | 2026-03-18 |
| Weights | Open | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 393K | 131K |
| Input $ / M tokens | $0.15 | $0.43 |
| Output $ / M tokens | $0.60 | $0.87 |
| Results tracked | 37 | 23 |
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Category by category
Coding DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 52.9 (#32), MiMo-V2-Pro: 43.8 (#83)
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2-Pro |
|---|---|---|
| LMArena WebDev | 1619 | 1433 |
| LMArena Coding | 1506 | 1476 |
| ALE-Bench | 1,092 | 785.17 |
| SciCode | 51.9% | — |
Agentic & Tool Use Not comparable
DeepSeek V4.1 Flash: 31.2 (#69), MiMo-V2-Pro: —
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2-Pro |
|---|---|---|
| APEX-Agents | 39.5% | — |
| GDP.pdf | 19.8% | — |
Reasoning DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 50.2 (#36), MiMo-V2-Pro: 22.1 (#206)
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2-Pro |
|---|---|---|
| NYT Connections (extended) | 89.6% | 25.8% |
| LMArena Hard Prompts | 1483 | 1457 |
| CritPt | 14.3% | — |
| Thematic Generalization | — | 45.9% |
| Mystery Game Puzzles | 43% | — |
| DTBench | 89.9% | — |
| LMCA | 47% | — |
| Surface Evolver Bench | 46.3% | — |
| Epoch Capabilities Index | 154.9 | — |
Math DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 66.7 (#25), MiMo-V2-Pro: 39.5 (#102)
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2-Pro |
|---|---|---|
| LMArena Math | 1477 | 1447 |
| FrontierMath (Tiers 1-3) | 67.4% | — |
| FrontierMath Tier 4 | 26.8% | — |
| OTIS Mock AIME 2024-2025 | 98.3% | — |
| ProofBench | 54% | — |
Knowledge DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 57.9 (#38), MiMo-V2-Pro: 41.4 (#111)
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2-Pro |
|---|---|---|
| LMArena Expert | 1506 | 1478 |
| GPQA Diamond | 89.8% | — |
Multimodal Not comparable
DeepSeek V4.1 Flash: 39.1 (#61), MiMo-V2-Pro: —
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2-Pro |
|---|---|---|
| LMArena Vision | 1277 | — |
| Furniture Assembly | 34.2% | — |
Multilingual DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 55.0 (#35), MiMo-V2-Pro: 52.7 (#81)
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2-Pro |
|---|---|---|
| LMArena Non-English | 1448 | 1416 |
| LMArena Chinese | 1497 | 1456 |
| LMArena French | 1452 | 1469 |
| LMArena German | 1484 | 1417 |
| LMArena Japanese | 1412 | 1366 |
| LMArena Korean | 1452 | 1400 |
| LMArena Russian | 1471 | 1427 |
| LMArena Spanish | 1459 | 1457 |
Instruction Following DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 77.3 (#26), MiMo-V2-Pro: 76.0 (#49)
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2-Pro |
|---|---|---|
| LMArena Instruction Following | 1474 | 1445 |
Long Context DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 45.2 (#47), MiMo-V2-Pro: 41.5 (#138)
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2-Pro |
|---|---|---|
| LMArena Longer Query | 1475 | 1455 |
| CL-bench | — | 15.7% |
| CL-bench Life | — | 6.9% |
Writing & Preference DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 65.4 (#48), MiMo-V2-Pro: 62.8 (#70)
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2-Pro |
|---|---|---|
| LMArena Text | 1462 | 1436 |
| LMArena Creative Writing | 1435 | 1415 |
| LMArena Multi-Turn | 1457 | 1456 |
| EQ-Bench Creative Writing | 1540 | — |
Frequently asked questions
Is DeepSeek V4.1 Flash better than MiMo-V2-Pro?
DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 43.0 on the Noometry Index.
Which is cheaper, DeepSeek V4.1 Flash or MiMo-V2-Pro?
DeepSeek V4.1 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; MiMo-V2-Pro lists at $0.43 and $0.87.
Is DeepSeek V4.1 Flash or MiMo-V2-Pro better for coding?
DeepSeek V4.1 Flash scores higher on coding benchmarks: 52.9 versus 43.8 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2-Pro does, with 1.05M tokens against 1M.
How many benchmarks do DeepSeek V4.1 Flash and MiMo-V2-Pro share?
20 benchmarks have published results for both models. DeepSeek V4.1 Flash has 37 scored results on Noometry and MiMo-V2-Pro has 23.