Model comparison
DeepSeek LLM 67B vs MiMo-V2.5-Pro
MiMo-V2.5-Pro is the stronger model overall, scoring 45.2 to 24.9 on the Noometry Index.
Last verified . 10 shared benchmarks.
DeepSeekSummary
- They share 10 benchmarks with published results for both. DeepSeek LLM 67B scores higher in 0 categories and MiMo-V2.5-Pro in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where MiMo-V2.5-Pro leads 42.2 to 7.0.
Side by side
| DeepSeek LLM 67B | MiMo-V2.5-Pro | |
|---|---|---|
| Provider | DeepSeek | Xiaomi |
| Noometry Index | 24.9 | 45.2 |
| Released | 2023-11-29 | 2026-04-22 |
| Weights | Open | Open |
| Context window | — | 1.05M |
| Max output | — | 131K |
| Input $ / M tokens | — | $0.43 |
| Output $ / M tokens | — | $0.87 |
| Results tracked | 15 | 27 |
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Category by category
Coding MiMo-V2.5-Pro leads
DeepSeek LLM 67B: 31.9 (#278), MiMo-V2.5-Pro: 47.4 (#60)
| Benchmark | DeepSeek LLM 67B | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Coding | 1096 | 1503 |
| LMArena WebDev | — | 1479 |
| SciCode | — | 50.2% |
| ALE-Bench | — | 899.8 |
Reasoning MiMo-V2.5-Pro leads
DeepSeek LLM 67B: 16.5 (#304), MiMo-V2.5-Pro: 26.8 (#130)
| Benchmark | DeepSeek LLM 67B | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Hard Prompts | 1070 | 1488 |
| NYT Connections (extended) | — | 34.4% |
| CritPt | — | 4% |
| Chess Puzzles | 0% | — |
| DTBench | — | 84.5% |
| LMCA | — | 29.5% |
| Epoch Capabilities Index | 110.5 | — |
Math MiMo-V2.5-Pro leads
DeepSeek LLM 67B: 8.7 (#324), MiMo-V2.5-Pro: 40.0 (#96)
| Benchmark | DeepSeek LLM 67B | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Math | 1108 | 1481 |
| OTIS Mock AIME 2024-2025 | 0.8% | — |
| ProofBench | — | 22% |
| MATH Level 5 | 6.4% | — |
Knowledge MiMo-V2.5-Pro leads
DeepSeek LLM 67B: 7.0 (#313), MiMo-V2.5-Pro: 42.2 (#98)
| Benchmark | DeepSeek LLM 67B | MiMo-V2.5-Pro |
|---|---|---|
| GPQA Diamond | 24.6% | — |
| LMArena Expert | — | 1503 |
Multilingual MiMo-V2.5-Pro leads
DeepSeek LLM 67B: 29.4 (#267), MiMo-V2.5-Pro: 55.1 (#34)
| Benchmark | DeepSeek LLM 67B | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Non-English | 1073 | 1449 |
| LMArena Chinese | 1132 | 1507 |
| LMArena French | — | 1488 |
| LMArena German | — | 1458 |
| LMArena Japanese | — | 1412 |
| LMArena Korean | — | 1437 |
| LMArena Russian | — | 1450 |
| LMArena Spanish | — | 1471 |
Instruction Following MiMo-V2.5-Pro leads
DeepSeek LLM 67B: 55.4 (#277), MiMo-V2.5-Pro: 77.5 (#21)
| Benchmark | DeepSeek LLM 67B | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Instruction Following | 1079 | 1477 |
Long Context MiMo-V2.5-Pro leads
DeepSeek LLM 67B: 33.1 (#265), MiMo-V2.5-Pro: 45.4 (#37)
| Benchmark | DeepSeek LLM 67B | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Longer Query | 1092 | 1483 |
Writing & Preference MiMo-V2.5-Pro leads
DeepSeek LLM 67B: 31.6 (#282), MiMo-V2.5-Pro: 65.3 (#49)
| Benchmark | DeepSeek LLM 67B | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Text | 1105 | 1465 |
| LMArena Creative Writing | 1067 | 1440 |
| LMArena Multi-Turn | 1082 | 1477 |
| EQ-Bench Creative Writing | — | 1493 |
| EQ-Bench 4 | — | 1208 |
Frequently asked questions
Is DeepSeek LLM 67B better than MiMo-V2.5-Pro?
MiMo-V2.5-Pro is the stronger model overall, scoring 45.2 to 24.9 on the Noometry Index.
Is DeepSeek LLM 67B or MiMo-V2.5-Pro better for coding?
MiMo-V2.5-Pro scores higher on coding benchmarks: 47.4 versus 31.9 in the Noometry coding category.
How many benchmarks do DeepSeek LLM 67B and MiMo-V2.5-Pro share?
10 benchmarks have published results for both models. DeepSeek LLM 67B has 15 scored results on Noometry and MiMo-V2.5-Pro has 27.