Model comparison
DeepSeek-V3.2-Speciale vs MiMo-V2.5
MiMo-V2.5 is the stronger model overall, scoring 43.4 to 39.7 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 46.0.
- MiMo-V2.5 is cheaper at $0.14 / $0.28 per million input/output tokens, against $0.58 / $1.68 for DeepSeek-V3.2-Speciale.
- MiMo-V2.5 accepts more context: 1.05M tokens versus 128K.
Side by side
| DeepSeek-V3.2-Speciale | MiMo-V2.5 | |
|---|---|---|
| Provider | DeepSeek | Xiaomi |
| Noometry Index | 39.7 | 43.4 |
| Released | 2025-12-01 | 2026-04-22 |
| Weights | Open | Open |
| Context window | 128K | 1.05M |
| Max output | 128K | 131K |
| Input $ / M tokens | $0.58 | $0.14 |
| Output $ / M tokens | $1.68 | $0.28 |
| Results tracked | 3 | 23 |
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Category by category
Coding MiMo-V2.5 leads
DeepSeek-V3.2-Speciale: 40.4 (#140), MiMo-V2.5: 43.9 (#81)
| Benchmark | DeepSeek-V3.2-Speciale | MiMo-V2.5 |
|---|---|---|
| LMArena WebDev | — | 1438 |
| SciCode | — | 43.1% |
| WeirdML | 46.7% | — |
| LMArena Coding | — | 1469 |
| ALE-Bench | — | 513.95 |
Reasoning DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 32.9 (#73), MiMo-V2.5: 28.6 (#101)
| Benchmark | DeepSeek-V3.2-Speciale | MiMo-V2.5 |
|---|---|---|
| SimpleBench | 52.6% | — |
| CritPt | — | 3.7% |
| LMArena Hard Prompts | — | 1450 |
Math Not comparable
DeepSeek-V3.2-Speciale: —, MiMo-V2.5: 36.8 (#163)
| Benchmark | DeepSeek-V3.2-Speciale | MiMo-V2.5 |
|---|---|---|
| ProofBench | — | 16% |
| LMArena Math | — | 1436 |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, MiMo-V2.5: 40.8 (#115)
| Benchmark | DeepSeek-V3.2-Speciale | MiMo-V2.5 |
|---|---|---|
| LMArena Expert | — | 1460 |
Multimodal Not comparable
DeepSeek-V3.2-Speciale: —, MiMo-V2.5: 39.8 (#54)
| Benchmark | DeepSeek-V3.2-Speciale | MiMo-V2.5 |
|---|---|---|
| LMArena Vision | — | 1247 |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, MiMo-V2.5: 51.9 (#99)
| Benchmark | DeepSeek-V3.2-Speciale | 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 Not comparable
DeepSeek-V3.2-Speciale: —, MiMo-V2.5: 75.5 (#60)
| Benchmark | DeepSeek-V3.2-Speciale | MiMo-V2.5 |
|---|---|---|
| LMArena Instruction Following | — | 1434 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, MiMo-V2.5: 44.2 (#73)
| Benchmark | DeepSeek-V3.2-Speciale | MiMo-V2.5 |
|---|---|---|
| LMArena Longer Query | — | 1445 |
Writing & Preference MiMo-V2.5 leads
DeepSeek-V3.2-Speciale: 46.0 (#222), MiMo-V2.5: 61.6 (#86)
| Benchmark | DeepSeek-V3.2-Speciale | MiMo-V2.5 |
|---|---|---|
| LMArena Text | — | 1428 |
| LMArena Creative Writing | — | 1393 |
| EQ-Bench Creative Writing | 1276 | — |
| LMArena Multi-Turn | — | 1445 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than MiMo-V2.5?
MiMo-V2.5 is the stronger model overall, scoring 43.4 to 39.7 on the Noometry Index.
Which is cheaper, DeepSeek-V3.2-Speciale or MiMo-V2.5?
MiMo-V2.5 is cheaper. It lists at $0.14 per million input tokens and $0.28 per million output tokens; DeepSeek-V3.2-Speciale lists at $0.58 and $1.68.
Is DeepSeek-V3.2-Speciale or MiMo-V2.5 better for coding?
MiMo-V2.5 scores higher on coding benchmarks: 43.9 versus 40.4 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2.5 does, with 1.05M tokens against 128K.
How many benchmarks do DeepSeek-V3.2-Speciale and MiMo-V2.5 share?
0 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and MiMo-V2.5 has 23.