Model comparison
DeepSeek-R1 vs MiMo-V2-Flash
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 41.3 on the Noometry Index. MiMo-V2-Flash costs 5.2× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
DeepSeekSummary
- They share 20 benchmarks with published results for both. DeepSeek-R1 scores higher in 6 categories and MiMo-V2-Flash in 2 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where DeepSeek-R1 leads 46.3 to 36.1.
- The biggest single-benchmark swing is SciCode: 35.7% for DeepSeek-R1 and 25.9% for MiMo-V2-Flash.
- MiMo-V2-Flash is cheaper at $0.14 / $0.28 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
- MiMo-V2-Flash accepts more context: 262K tokens versus 164K.
- MiMo-V2-Flash has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-R1 | MiMo-V2-Flash | |
|---|---|---|
| Provider | DeepSeek | Xiaomi |
| Noometry Index | 42.3 | 41.3 |
| Released | 2025-01-20 | 2025-12-16 |
| Weights | Proprietary | Open |
| Context window | 164K | 262K |
| Max output | 64K | 66K |
| Input $ / M tokens | $0.50 | $0.14 |
| Output $ / M tokens | $2.15 | $0.28 |
| Results tracked | 52 | 21 |
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Category by category
Coding DeepSeek-R1 leads
DeepSeek-R1: 46.3 (#68), MiMo-V2-Flash: 36.1 (#211)
| Benchmark | DeepSeek-R1 | MiMo-V2-Flash |
|---|---|---|
| SciCode | 35.7% | 25.9% |
| LMArena Coding | 1427 | 1443 |
| ALE-Bench | 804.12 | 737.95 |
| Aider Polyglot | 71.4% | — |
| LMArena WebDev | — | 1330 |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use Not comparable
DeepSeek-R1: 30.7 (#75), MiMo-V2-Flash: —
| Benchmark | DeepSeek-R1 | MiMo-V2-Flash |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
Reasoning MiMo-V2-Flash leads
DeepSeek-R1: 18.6 (#278), MiMo-V2-Flash: 24.9 (#157)
| Benchmark | DeepSeek-R1 | MiMo-V2-Flash |
|---|---|---|
| CritPt | 1.1% | 0% |
| LMArena Hard Prompts | 1416 | 1420 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| Kagi LLM Benchmark | 69.4% | — |
| ARC-AGI-1 | 21.2% | — |
| 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-Flash: 38.3 (#139)
| Benchmark | DeepSeek-R1 | MiMo-V2-Flash |
|---|---|---|
| LMArena Math | 1400 | 1396 |
| 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-Flash: 39.7 (#131)
| Benchmark | DeepSeek-R1 | MiMo-V2-Flash |
|---|---|---|
| LMArena Expert | 1394 | 1425 |
| GPQA Diamond | 76.3% | — |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |
Multilingual DeepSeek-R1 leads
DeepSeek-R1: 52.4 (#85), MiMo-V2-Flash: 51.0 (#113)
| Benchmark | DeepSeek-R1 | MiMo-V2-Flash |
|---|---|---|
| LMArena Non-English | 1412 | 1392 |
| LMArena Chinese | 1442 | 1462 |
| LMArena French | 1417 | 1429 |
| LMArena German | 1404 | 1395 |
| LMArena Japanese | 1391 | 1325 |
| LMArena Korean | 1360 | 1358 |
| LMArena Russian | 1423 | 1387 |
| LMArena Spanish | 1411 | 1420 |
Instruction Following MiMo-V2-Flash leads
DeepSeek-R1: 72.0 (#143), MiMo-V2-Flash: 73.5 (#120)
| Benchmark | DeepSeek-R1 | MiMo-V2-Flash |
|---|---|---|
| LMArena Instruction Following | 1382 | 1392 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
Long Context DeepSeek-R1 leads
DeepSeek-R1: 45.4 (#36), MiMo-V2-Flash: 43.0 (#110)
| Benchmark | DeepSeek-R1 | MiMo-V2-Flash |
|---|---|---|
| LMArena Longer Query | 1391 | 1409 |
| Fiction.LiveBench | 75% | — |
Writing & Preference DeepSeek-R1 leads
DeepSeek-R1: 61.4 (#88), MiMo-V2-Flash: 59.7 (#106)
| Benchmark | DeepSeek-R1 | MiMo-V2-Flash |
|---|---|---|
| LMArena Text | 1428 | 1411 |
| LMArena Creative Writing | 1405 | 1375 |
| LMArena Multi-Turn | 1405 | 1404 |
| 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-Flash?
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 41.3 on the Noometry Index. MiMo-V2-Flash costs 5.2× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-R1 or MiMo-V2-Flash?
MiMo-V2-Flash 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-Flash better for coding?
DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 36.1 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2-Flash does, with 262K tokens against 164K.
How many benchmarks do DeepSeek-R1 and MiMo-V2-Flash share?
20 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and MiMo-V2-Flash has 21.