Model comparison
DeepSeek-R1-Distill-Llama-70B vs MiMo-V2.5
MiMo-V2.5 is the stronger model overall, scoring 43.4 to 37.8 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeekSummary
- The widest gap is in writing & preference, where MiMo-V2.5 leads 61.6 to 49.0.
Side by side
| DeepSeek-R1-Distill-Llama-70B | MiMo-V2.5 | |
|---|---|---|
| Provider | DeepSeek | Xiaomi |
| Noometry Index | 37.8 | 43.4 |
| Released | 2025-01-20 | 2026-04-22 |
| Weights | Open | Open |
| Context window | — | 1.05M |
| Max output | — | 131K |
| Input $ / M tokens | — | $0.14 |
| Output $ / M tokens | — | $0.28 |
| Results tracked | 13 | 23 |
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Category by category
Coding MiMo-V2.5 leads
DeepSeek-R1-Distill-Llama-70B: 36.8 (#202), MiMo-V2.5: 43.9 (#81)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | MiMo-V2.5 |
|---|---|---|
| LMArena WebDev | — | 1438 |
| SciCode | — | 43.1% |
| BigCodeBench Instruct | 35.3% | — |
| LiveBench Coding | 51.6% | — |
| LMArena Coding | — | 1469 |
| BigCodeBench Complete | 49.9% | — |
| ALE-Bench | — | 513.95 |
Reasoning MiMo-V2.5 leads
DeepSeek-R1-Distill-Llama-70B: 24.9 (#156), MiMo-V2.5: 28.6 (#101)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | MiMo-V2.5 |
|---|---|---|
| Kagi LLM Benchmark | 52.3% | — |
| CritPt | — | 3.7% |
| LiveBench Reasoning | 67.6% | — |
| LMArena Hard Prompts | — | 1450 |
| LiveBench Data Analysis | 55.9% | — |
| LiveBench | 54.5% | — |
Math Too close to call
DeepSeek-R1-Distill-Llama-70B: 36.0 (#176), MiMo-V2.5: 36.8 (#163)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | MiMo-V2.5 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 51.4% | — |
| ProofBench | — | 16% |
| LiveBench Math | 58.1% | — |
| LMArena Math | — | 1436 |
| MATH Level 5 | 89.9% | — |
Knowledge MiMo-V2.5 leads
DeepSeek-R1-Distill-Llama-70B: 30.7 (#225), MiMo-V2.5: 40.8 (#115)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | MiMo-V2.5 |
|---|---|---|
| GPQA Diamond | 55.7% | — |
| LMArena Expert | — | 1460 |
Multimodal Not comparable
DeepSeek-R1-Distill-Llama-70B: —, MiMo-V2.5: 39.8 (#54)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | MiMo-V2.5 |
|---|---|---|
| LMArena Vision | — | 1247 |
Multilingual Not comparable
DeepSeek-R1-Distill-Llama-70B: —, MiMo-V2.5: 51.9 (#99)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | MiMo-V2.5 |
|---|---|---|
| LMArena Non-English | — | 1404 |
| LMArena Chinese | — | 1468 |
| LMArena French | — | 1447 |
| LMArena German | — | 1421 |
| LMArena Japanese | — | 1306 |
| LMArena Korean | — | 1363 |
| LMArena Russian | — | 1395 |
| LMArena Spanish | — | 1416 |
Instruction Following MiMo-V2.5 leads
DeepSeek-R1-Distill-Llama-70B: 68.2 (#190), MiMo-V2.5: 75.5 (#60)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | MiMo-V2.5 |
|---|---|---|
| LiveBench Instruction Following | 69.9% | — |
| LMArena Instruction Following | — | 1434 |
Long Context Not comparable
DeepSeek-R1-Distill-Llama-70B: —, MiMo-V2.5: 44.2 (#73)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | MiMo-V2.5 |
|---|---|---|
| LMArena Longer Query | — | 1445 |
Writing & Preference MiMo-V2.5 leads
DeepSeek-R1-Distill-Llama-70B: 49.0 (#194), MiMo-V2.5: 61.6 (#86)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | MiMo-V2.5 |
|---|---|---|
| LMArena Text | — | 1428 |
| LMArena Creative Writing | — | 1393 |
| LMArena Multi-Turn | — | 1445 |
| LiveBench Language | 23.8% | — |
Frequently asked questions
Is DeepSeek-R1-Distill-Llama-70B better than MiMo-V2.5?
MiMo-V2.5 is the stronger model overall, scoring 43.4 to 37.8 on the Noometry Index.
Is DeepSeek-R1-Distill-Llama-70B or MiMo-V2.5 better for coding?
MiMo-V2.5 scores higher on coding benchmarks: 43.9 versus 36.8 in the Noometry coding category.
How many benchmarks do DeepSeek-R1-Distill-Llama-70B and MiMo-V2.5 share?
0 benchmarks have published results for both models. DeepSeek-R1-Distill-Llama-70B has 13 scored results on Noometry and MiMo-V2.5 has 23.