Model comparison
DeepSeek V4.1 Flash vs MiMo-V2.5-Pro
DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 45.2 on the Noometry Index.
Last verified . 26 shared benchmarks.
DeepSeekSummary
- They share 26 benchmarks with published results for both. DeepSeek V4.1 Flash scores higher in 5 categories and MiMo-V2.5-Pro in 3 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek V4.1 Flash leads 66.7 to 40.0.
- The biggest single-benchmark swing is NYT Connections (extended): 89.6% for DeepSeek V4.1 Flash and 34.4% for MiMo-V2.5-Pro.
- DeepSeek V4.1 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.43 / $0.87 for MiMo-V2.5-Pro.
- MiMo-V2.5-Pro accepts more context: 1.05M tokens versus 1M.
Side by side
| DeepSeek V4.1 Flash | MiMo-V2.5-Pro | |
|---|---|---|
| Provider | DeepSeek | Xiaomi |
| Noometry Index | 52.8 | 45.2 |
| Released | 2026-09-09 | 2026-04-22 |
| Weights | Open | Open |
| 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 | 27 |
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Category by category
Coding DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 52.9 (#32), MiMo-V2.5-Pro: 47.4 (#60)
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2.5-Pro |
|---|---|---|
| LMArena WebDev | 1619 | 1479 |
| SciCode | 51.9% | 50.2% |
| LMArena Coding | 1506 | 1503 |
| ALE-Bench | 1,092 | 899.8 |
Agentic & Tool Use Not comparable
DeepSeek V4.1 Flash: 31.2 (#69), MiMo-V2.5-Pro: —
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2.5-Pro |
|---|---|---|
| APEX-Agents | 39.5% | — |
| GDP.pdf | 19.8% | — |
Reasoning DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 50.2 (#36), MiMo-V2.5-Pro: 26.8 (#130)
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2.5-Pro |
|---|---|---|
| NYT Connections (extended) | 89.6% | 34.4% |
| CritPt | 14.3% | 4% |
| LMArena Hard Prompts | 1483 | 1488 |
| DTBench | 89.9% | 84.5% |
| LMCA | 47% | 29.5% |
| Mystery Game Puzzles | 43% | — |
| Surface Evolver Bench | 46.3% | — |
| Epoch Capabilities Index | 154.9 | — |
Math DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 66.7 (#25), MiMo-V2.5-Pro: 40.0 (#96)
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2.5-Pro |
|---|---|---|
| ProofBench | 54% | 22% |
| LMArena Math | 1477 | 1481 |
| FrontierMath (Tiers 1-3) | 67.4% | — |
| FrontierMath Tier 4 | 26.8% | — |
| OTIS Mock AIME 2024-2025 | 98.3% | — |
Knowledge DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 57.9 (#38), MiMo-V2.5-Pro: 42.2 (#98)
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Expert | 1506 | 1503 |
| GPQA Diamond | 89.8% | — |
Multimodal Not comparable
DeepSeek V4.1 Flash: 39.1 (#61), MiMo-V2.5-Pro: —
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Vision | 1277 | — |
| Furniture Assembly | 34.2% | — |
Multilingual Too close to call
DeepSeek V4.1 Flash: 55.0 (#35), MiMo-V2.5-Pro: 55.1 (#34)
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Non-English | 1448 | 1449 |
| LMArena Chinese | 1497 | 1507 |
| LMArena French | 1452 | 1488 |
| LMArena German | 1484 | 1458 |
| LMArena Japanese | 1412 | 1412 |
| LMArena Korean | 1452 | 1437 |
| LMArena Russian | 1471 | 1450 |
| LMArena Spanish | 1459 | 1471 |
Instruction Following Too close to call
DeepSeek V4.1 Flash: 77.3 (#26), MiMo-V2.5-Pro: 77.5 (#21)
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Instruction Following | 1474 | 1477 |
Long Context Too close to call
DeepSeek V4.1 Flash: 45.2 (#47), MiMo-V2.5-Pro: 45.4 (#37)
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Longer Query | 1475 | 1483 |
Writing & Preference Too close to call
DeepSeek V4.1 Flash: 65.4 (#48), MiMo-V2.5-Pro: 65.3 (#49)
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Text | 1462 | 1465 |
| LMArena Creative Writing | 1435 | 1440 |
| EQ-Bench Creative Writing | 1540 | 1493 |
| LMArena Multi-Turn | 1457 | 1477 |
| EQ-Bench 4 | — | 1208 |
Frequently asked questions
Is DeepSeek V4.1 Flash better than MiMo-V2.5-Pro?
DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 45.2 on the Noometry Index.
Which is cheaper, DeepSeek V4.1 Flash or MiMo-V2.5-Pro?
DeepSeek V4.1 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; MiMo-V2.5-Pro lists at $0.43 and $0.87.
Is DeepSeek V4.1 Flash or MiMo-V2.5-Pro better for coding?
DeepSeek V4.1 Flash scores higher on coding benchmarks: 52.9 versus 47.4 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2.5-Pro does, with 1.05M tokens against 1M.
How many benchmarks do DeepSeek V4.1 Flash and MiMo-V2.5-Pro share?
26 benchmarks have published results for both models. DeepSeek V4.1 Flash has 37 scored results on Noometry and MiMo-V2.5-Pro has 27.