Model comparison
DeepSeek-R1-Distill-Llama-70B vs MiMo-V2-Flash
MiMo-V2-Flash is the stronger model overall, scoring 40.8 to 37.4 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeekSummary
- The widest gap is in writing & preference, where MiMo-V2-Flash leads 59.9 to 49.0.
Side by side
| DeepSeek-R1-Distill-Llama-70B | MiMo-V2-Flash | |
|---|---|---|
| Provider | DeepSeek | Xiaomi |
| Noometry Index | 37.4 | 40.8 |
| Released | 2025-01-20 | 2025-12-16 |
| Weights | Open | Open |
| Context window | — | 262K |
| Max output | — | 66K |
| Input $ / M tokens | — | $0.14 |
| Output $ / M tokens | — | $0.28 |
| Results tracked | 13 | 21 |
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Category by category
Coding Too close to call
DeepSeek-R1-Distill-Llama-70B: 36.3 (#197), MiMo-V2-Flash: 35.8 (#205)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | MiMo-V2-Flash |
|---|---|---|
| LMArena WebDev | — | 1330 |
| SciCode | — | 25.9% |
| BigCodeBench Instruct | 35.3% | — |
| LiveBench Coding | 51.6% | — |
| LMArena Coding | — | 1443 |
| BigCodeBench Complete | 49.9% | — |
| ALE-Bench | — | 737.95 |
Reasoning Too close to call
DeepSeek-R1-Distill-Llama-70B: 24.1 (#150), MiMo-V2-Flash: 24.1 (#155)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | MiMo-V2-Flash |
|---|---|---|
| Kagi LLM Benchmark | 52.3% | — |
| CritPt | — | 0% |
| LiveBench Reasoning | 67.6% | — |
| LMArena Hard Prompts | — | 1420 |
| LiveBench Data Analysis | 55.9% | — |
| LiveBench | 54.5% | — |
Math MiMo-V2-Flash leads
DeepSeek-R1-Distill-Llama-70B: 34.3 (#161), MiMo-V2-Flash: 35.6 (#141)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | MiMo-V2-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 51.4% | — |
| LiveBench Math | 58.1% | — |
| LMArena Math | — | 1396 |
| MATH Level 5 | 89.9% | — |
Knowledge MiMo-V2-Flash leads
DeepSeek-R1-Distill-Llama-70B: 30.7 (#216), MiMo-V2-Flash: 39.1 (#126)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | MiMo-V2-Flash |
|---|---|---|
| GPQA Diamond | 55.7% | — |
| LMArena Expert | — | 1425 |
Multilingual Not comparable
DeepSeek-R1-Distill-Llama-70B: —, MiMo-V2-Flash: 51.1 (#109)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | MiMo-V2-Flash |
|---|---|---|
| LMArena Non-English | — | 1391 |
| LMArena Chinese | — | 1463 |
| LMArena French | — | 1429 |
| LMArena German | — | 1395 |
| LMArena Japanese | — | 1324 |
| LMArena Korean | — | 1358 |
| LMArena Russian | — | 1387 |
| LMArena Spanish | — | 1420 |
Instruction Following MiMo-V2-Flash leads
DeepSeek-R1-Distill-Llama-70B: 68.3 (#185), MiMo-V2-Flash: 73.7 (#115)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | MiMo-V2-Flash |
|---|---|---|
| LiveBench Instruction Following | 69.9% | — |
| LMArena Instruction Following | — | 1392 |
Long Context Not comparable
DeepSeek-R1-Distill-Llama-70B: —, MiMo-V2-Flash: 43.0 (#106)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | MiMo-V2-Flash |
|---|---|---|
| LMArena Longer Query | — | 1409 |
Writing & Preference MiMo-V2-Flash leads
DeepSeek-R1-Distill-Llama-70B: 49.0 (#189), MiMo-V2-Flash: 59.9 (#101)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | MiMo-V2-Flash |
|---|---|---|
| LMArena Text | — | 1411 |
| LMArena Creative Writing | — | 1375 |
| LMArena Multi-Turn | — | 1404 |
| LiveBench Language | 23.8% | — |
Frequently asked questions
Is DeepSeek-R1-Distill-Llama-70B better than MiMo-V2-Flash?
MiMo-V2-Flash is the stronger model overall, scoring 40.8 to 37.4 on the Noometry Index.
Is DeepSeek-R1-Distill-Llama-70B or MiMo-V2-Flash better for coding?
They score almost the same on coding (36.3 vs 35.8); test both on your own repository before choosing.
How many benchmarks do DeepSeek-R1-Distill-Llama-70B and MiMo-V2-Flash share?
0 benchmarks have published results for both models. DeepSeek-R1-Distill-Llama-70B has 13 scored results on Noometry and MiMo-V2-Flash has 21.