# DeepSeek-V3.1-Terminus vs Qwen2.5-Coder-32B

> DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 33.4 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-terminus-vs-qwen2-5-coder-32b
- Last updated: 2026-10-12
- Shared benchmarks: 10

## Summary

- They share 10 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 7 categories and Qwen2.5-Coder-32B in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in coding, where DeepSeek-V3.1-Terminus leads 42.0 to 22.6.
- DeepSeek-V3.1-Terminus is cheaper at $0.27 / $1 per million input/output tokens, against $0.66 / $1 for Qwen2.5-Coder-32B.
- DeepSeek-V3.1-Terminus accepts more context: 164K tokens versus 33K.

## Snapshot

| | DeepSeek-V3.1-Terminus | Qwen2.5-Coder-32B |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 43.1 | 33.4 |
| Rank | 97 | 245 |
| Context | 164K | 33K |
| Input $/M | $0.27 | $0.66 |
| Output $/M | $1 | $1 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1-Terminus: 42.0 (#113)
- Qwen2.5-Coder-32B: 22.6 (#333)

| Benchmark | DeepSeek-V3.1-Terminus | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Coding | 1426 | 1276 |
| SWE-bench Verified (bash only) | — | 9% |
| Aider Polyglot | — | 16.4% |
| SciCode | 40.6% | — |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| BigCodeBench Complete | — | 58% |
| ALE-Bench | 745.17 | — |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |

## Reasoning

- DeepSeek-V3.1-Terminus: 26.4 (#133)
- Qwen2.5-Coder-32B: 21.2 (#225)

| Benchmark | DeepSeek-V3.1-Terminus | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Hard Prompts | 1426 | 1251 |
| Kagi LLM Benchmark | 57.4% | — |
| CritPt | 1.7% | — |
| LiveBench Reasoning | — | 42.1% |
| DTBench | 81.3% | — |
| LiveBench Data Analysis | — | 49.9% |
| LMCA | 28.6% | — |
| Epoch Capabilities Index | — | 119.49 |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |

## Math

- DeepSeek-V3.1-Terminus: 38.5 (#137)
- Qwen2.5-Coder-32B: 33.3 (#204)

| Benchmark | DeepSeek-V3.1-Terminus | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Math | 1402 | 1251 |
| LiveBench Math | — | 46.6% |
| GSM8K | — | 93% |

## Knowledge

- DeepSeek-V3.1-Terminus: —
- Qwen2.5-Coder-32B: 33.4 (#203)

| Benchmark | DeepSeek-V3.1-Terminus | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | — | 1221 |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |

## Multilingual

- DeepSeek-V3.1-Terminus: 52.1 (#92)
- Qwen2.5-Coder-32B: 37.8 (#235)

| Benchmark | DeepSeek-V3.1-Terminus | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1407 | 1205 |
| LMArena Russian | 1436 | 1228 |
| LMArena Chinese | — | 1222 |

## Instruction Following

- DeepSeek-V3.1-Terminus: 74.0 (#106)
- Qwen2.5-Coder-32B: 61.4 (#245)

| Benchmark | DeepSeek-V3.1-Terminus | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Instruction Following | 1404 | 1223 |
| LiveBench Instruction Following | — | 58.7% |

## Long Context

- DeepSeek-V3.1-Terminus: 43.4 (#97)
- Qwen2.5-Coder-32B: 38.0 (#208)

| Benchmark | DeepSeek-V3.1-Terminus | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1421 | 1251 |

## Writing & Preference

- DeepSeek-V3.1-Terminus: 61.0 (#92)
- Qwen2.5-Coder-32B: 41.6 (#240)

| Benchmark | DeepSeek-V3.1-Terminus | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1419 | 1230 |
| LMArena Creative Writing | 1403 | 1174 |
| LMArena Multi-Turn | 1411 | 1222 |
| LiveBench Language | — | 23.3% |

## FAQ

### Is DeepSeek-V3.1-Terminus better than Qwen2.5-Coder-32B?

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 33.4 on the Noometry Index.

### Which is cheaper, DeepSeek-V3.1-Terminus or Qwen2.5-Coder-32B?

DeepSeek-V3.1-Terminus is cheaper. It lists at $0.27 per million input tokens and $1 per million output tokens; Qwen2.5-Coder-32B lists at $0.66 and $1.

### Is DeepSeek-V3.1-Terminus or Qwen2.5-Coder-32B better for coding?

DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 42.0 versus 22.6 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek-V3.1-Terminus does, with 164K tokens against 33K.

### How many benchmarks do DeepSeek-V3.1-Terminus and Qwen2.5-Coder-32B share?

10 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Qwen2.5-Coder-32B has 31.