Model comparison
DeepSeek-V3.1-Terminus vs Qwen2.5 32B Instruct
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 30.1 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- The widest gap is in math, where DeepSeek-V3.1-Terminus leads 38.5 to 16.2.
- DeepSeek-V3.1-Terminus is cheaper at $0.27 / $1 per million input/output tokens, against $0.70 / $2.80 for Qwen2.5 32B Instruct.
- DeepSeek-V3.1-Terminus accepts more context: 164K tokens versus 131K.
Side by side
| DeepSeek-V3.1-Terminus | Qwen2.5 32B Instruct | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 43.1 | 30.1 |
| Released | 2025-09-22 | 2024-09 |
| Weights | Open | Open |
| Context window | 164K | 131K |
| Max output | 147K | 8K |
| Input $ / M tokens | $0.27 | $0.70 |
| Output $ / M tokens | $1 | $2.80 |
| Results tracked | 16 | 7 |
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Category by category
Coding DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 42.0 (#113), Qwen2.5 32B Instruct: 38.7 (#169)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen2.5 32B Instruct |
|---|---|---|
| SciCode | 40.6% | — |
| BigCodeBench Instruct | — | 45% |
| LMArena Coding | 1426 | — |
| BigCodeBench Complete | — | 52.3% |
| ALE-Bench | 745.17 | — |
Reasoning DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 26.4 (#133), Qwen2.5 32B Instruct: 19.2 (#266)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen2.5 32B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 57.4% | — |
| CritPt | 1.7% | — |
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | 1426 | — |
| DTBench | 81.3% | — |
| LMCA | 28.6% | — |
| Epoch Capabilities Index | — | 128.52 |
Math DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 38.5 (#137), Qwen2.5 32B Instruct: 16.2 (#296)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen2.5 32B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 7.4% |
| LMArena Math | 1402 | — |
| MATH Level 5 | — | 56.1% |
Knowledge Not comparable
DeepSeek-V3.1-Terminus: —, Qwen2.5 32B Instruct: 24.9 (#266)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen2.5 32B Instruct |
|---|---|---|
| GPQA Diamond | — | 46.1% |
Multilingual Not comparable
DeepSeek-V3.1-Terminus: 52.1 (#92), Qwen2.5 32B Instruct: —
| Benchmark | DeepSeek-V3.1-Terminus | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Non-English | 1407 | — |
| LMArena Russian | 1436 | — |
Instruction Following Not comparable
DeepSeek-V3.1-Terminus: 74.0 (#106), Qwen2.5 32B Instruct: —
| Benchmark | DeepSeek-V3.1-Terminus | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Instruction Following | 1404 | — |
Long Context Not comparable
DeepSeek-V3.1-Terminus: 43.4 (#97), Qwen2.5 32B Instruct: —
| Benchmark | DeepSeek-V3.1-Terminus | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Longer Query | 1421 | — |
Writing & Preference Not comparable
DeepSeek-V3.1-Terminus: 61.0 (#92), Qwen2.5 32B Instruct: —
| Benchmark | DeepSeek-V3.1-Terminus | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Text | 1419 | — |
| LMArena Creative Writing | 1403 | — |
| LMArena Multi-Turn | 1411 | — |
Frequently asked questions
Is DeepSeek-V3.1-Terminus better than Qwen2.5 32B Instruct?
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 30.1 on the Noometry Index.
Which is cheaper, DeepSeek-V3.1-Terminus or Qwen2.5 32B Instruct?
DeepSeek-V3.1-Terminus is cheaper. It lists at $0.27 per million input tokens and $1 per million output tokens; Qwen2.5 32B Instruct lists at $0.70 and $2.80.
Is DeepSeek-V3.1-Terminus or Qwen2.5 32B Instruct better for coding?
DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 42.0 versus 38.7 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3.1-Terminus does, with 164K tokens against 131K.
How many benchmarks do DeepSeek-V3.1-Terminus and Qwen2.5 32B Instruct share?
0 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Qwen2.5 32B Instruct has 7.