# DeepSeek-V3.1-Terminus vs MiMo-V2.5-Pro

> MiMo-V2.5-Pro is the stronger model overall, scoring 44.8 to 42.6 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-terminus-vs-mimo-v2-5-pro
- Last updated: 2026-10-10
- Shared benchmarks: 15

## Summary

- They share 15 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 0 categories and MiMo-V2.5-Pro in 7 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in coding, where MiMo-V2.5-Pro leads 47.0 to 41.5.
- The biggest single-benchmark swing is SciCode: 40.6% for DeepSeek-V3.1-Terminus and 50.2% for MiMo-V2.5-Pro.
- DeepSeek-V3.1-Terminus is cheaper at $0.30 / $1 per million input/output tokens, against $0.43 / $0.87 for MiMo-V2.5-Pro.
- MiMo-V2.5-Pro accepts more context: 1.05M tokens versus 164K.

## Snapshot

| | DeepSeek-V3.1-Terminus | MiMo-V2.5-Pro |
|---|---|---|
| Provider | DeepSeek | Xiaomi |
| Noometry Index | 42.6 | 44.8 |
| Rank | 96 | 70 |
| Context | 164K | 1.05M |
| Input $/M | $0.30 | $0.43 |
| Output $/M | $1 | $0.87 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1-Terminus: 41.5 (#114)
- MiMo-V2.5-Pro: 47.0 (#58)

| Benchmark | DeepSeek-V3.1-Terminus | MiMo-V2.5-Pro |
|---|---|---|
| SciCode | 40.6% | 50.2% |
| LMArena Coding | 1426 | 1502 |
| ALE-Bench | 745.17 | 899.8 |
| LMArena WebDev | — | 1477 |

## Reasoning

- DeepSeek-V3.1-Terminus: 25.7 (#126)
- MiMo-V2.5-Pro: 25.9 (#124)

| Benchmark | DeepSeek-V3.1-Terminus | MiMo-V2.5-Pro |
|---|---|---|
| CritPt | 1.7% | 4% |
| LMArena Hard Prompts | 1427 | 1487 |
| DTBench | 81.3% | 84.5% |
| LMCA | 28.6% | 29.5% |
| Kagi LLM Benchmark | 57.4% | — |
| NYT Connections (extended) | — | 34.4% |

## Math

- DeepSeek-V3.1-Terminus: 35.7 (#138)
- MiMo-V2.5-Pro: 38.6 (#84)

| Benchmark | DeepSeek-V3.1-Terminus | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Math | 1402 | 1480 |
| ProofBench | — | 22% |

## Knowledge

- DeepSeek-V3.1-Terminus: —
- MiMo-V2.5-Pro: 41.5 (#100)

| Benchmark | DeepSeek-V3.1-Terminus | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Expert | — | 1501 |

## Multilingual

- DeepSeek-V3.1-Terminus: 52.3 (#87)
- MiMo-V2.5-Pro: 55.3 (#33)

| Benchmark | DeepSeek-V3.1-Terminus | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Non-English | 1407 | 1449 |
| LMArena Russian | 1434 | 1450 |
| LMArena Chinese | — | 1507 |
| LMArena French | — | 1489 |
| LMArena German | — | 1455 |
| LMArena Japanese | — | 1418 |
| LMArena Korean | — | 1436 |
| LMArena Spanish | — | 1470 |

## Instruction Following

- DeepSeek-V3.1-Terminus: 74.4 (#96)
- MiMo-V2.5-Pro: 77.6 (#21)

| Benchmark | DeepSeek-V3.1-Terminus | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Instruction Following | 1406 | 1477 |

## Long Context

- DeepSeek-V3.1-Terminus: 43.5 (#88)
- MiMo-V2.5-Pro: 45.4 (#38)

| Benchmark | DeepSeek-V3.1-Terminus | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Longer Query | 1426 | 1482 |

## Writing & Preference

- DeepSeek-V3.1-Terminus: 61.3 (#87)
- MiMo-V2.5-Pro: 65.5 (#47)

| Benchmark | DeepSeek-V3.1-Terminus | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Text | 1420 | 1465 |
| LMArena Creative Writing | 1404 | 1440 |
| LMArena Multi-Turn | 1411 | 1477 |
| EQ-Bench Creative Writing | — | 1493 |
| EQ-Bench 4 | — | 1208 |

## FAQ

### Is DeepSeek-V3.1-Terminus better than MiMo-V2.5-Pro?

MiMo-V2.5-Pro is the stronger model overall, scoring 44.8 to 42.6 on the Noometry Index.

### Which is cheaper, DeepSeek-V3.1-Terminus or MiMo-V2.5-Pro?

DeepSeek-V3.1-Terminus is cheaper. It lists at $0.30 per million input tokens and $1 per million output tokens; MiMo-V2.5-Pro lists at $0.43 and $0.87.

### Is DeepSeek-V3.1-Terminus or MiMo-V2.5-Pro better for coding?

MiMo-V2.5-Pro scores higher on coding benchmarks: 47.0 versus 41.5 in the Noometry coding category.

### Which has the bigger context window?

MiMo-V2.5-Pro does, with 1.05M tokens against 164K.

### How many benchmarks do DeepSeek-V3.1-Terminus and MiMo-V2.5-Pro share?

15 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and MiMo-V2.5-Pro has 27.