Model comparison
DeepSeek-V3.2-Exp vs Qwen2.5 Plus 1127
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 38.1 on the Noometry Index.
Last verified . 14 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 14 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 7 categories and Qwen2.5 Plus 1127 in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.6 to 34.9.
- DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.2-Exp | Qwen2.5 Plus 1127 | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 44.9 | 38.1 |
| Released | 2025-09-29 | — |
| Weights | Open | Proprietary |
| Context window | 164K | — |
| Max output | 66K | — |
| Input $ / M tokens | $0.26 | — |
| Output $ / M tokens | $0.38 | — |
| Results tracked | 48 | 14 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.4 (#61), Qwen2.5 Plus 1127: 37.9 (#171)
| Benchmark | DeepSeek-V3.2-Exp | Qwen2.5 Plus 1127 |
|---|---|---|
| LMArena Coding | 1454 | 1314 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| WeirdML | 39.5% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Exp: 37.9 (#31), Qwen2.5 Plus 1127: —
| Benchmark | DeepSeek-V3.2-Exp | Qwen2.5 Plus 1127 |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |
Reasoning Qwen2.5 Plus 1127 leads
DeepSeek-V3.2-Exp: 22.0 (#185), Qwen2.5 Plus 1127: 24.8 (#139)
| Benchmark | DeepSeek-V3.2-Exp | Qwen2.5 Plus 1127 |
|---|---|---|
| LMArena Hard Prompts | 1433 | 1299 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| CritPt | 2.9% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| DTBench | 87.7% | — |
| LMCA | 29.1% | — |
| Epoch Capabilities Index | 146.28 | — |
Math DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 40.8 (#75), Qwen2.5 Plus 1127: 33.5 (#175)
| Benchmark | DeepSeek-V3.2-Exp | Qwen2.5 Plus 1127 |
|---|---|---|
| LMArena Math | 1435 | 1298 |
| MathArena Final-Answer Competitions | 57.7% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 8% | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 51.6 (#61), Qwen2.5 Plus 1127: 34.9 (#179)
| Benchmark | DeepSeek-V3.2-Exp | Qwen2.5 Plus 1127 |
|---|---|---|
| LMArena Expert | 1436 | 1289 |
| GPQA Diamond | 83.4% | — |
| Vectara Hallucination Rate | 5.3% | — |
Multilingual DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 52.4 (#85), Qwen2.5 Plus 1127: 42.1 (#196)
| Benchmark | DeepSeek-V3.2-Exp | Qwen2.5 Plus 1127 |
|---|---|---|
| LMArena Non-English | 1409 | 1265 |
| LMArena Chinese | 1461 | 1314 |
| LMArena German | 1440 | 1231 |
| LMArena Japanese | 1377 | 1207 |
| LMArena Russian | 1423 | 1271 |
| LMArena French | 1435 | — |
| LMArena Korean | 1372 | — |
| LMArena Spanish | 1441 | — |
Instruction Following DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 74.7 (#89), Qwen2.5 Plus 1127: 67.4 (#194)
| Benchmark | DeepSeek-V3.2-Exp | Qwen2.5 Plus 1127 |
|---|---|---|
| LMArena Instruction Following | 1413 | 1275 |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#15), Qwen2.5 Plus 1127: 39.2 (#180)
| Benchmark | DeepSeek-V3.2-Exp | Qwen2.5 Plus 1127 |
|---|---|---|
| LMArena Longer Query | 1428 | 1292 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 62.5 (#73), Qwen2.5 Plus 1127: 49.5 (#186)
| Benchmark | DeepSeek-V3.2-Exp | Qwen2.5 Plus 1127 |
|---|---|---|
| LMArena Text | 1425 | 1299 |
| LMArena Creative Writing | 1403 | 1262 |
| LMArena Multi-Turn | 1427 | 1299 |
| EQ-Bench Creative Writing | 1515 | — |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than Qwen2.5 Plus 1127?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 38.1 on the Noometry Index.
Is DeepSeek-V3.2-Exp or Qwen2.5 Plus 1127 better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.4 versus 37.9 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.2-Exp and Qwen2.5 Plus 1127 share?
14 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and Qwen2.5 Plus 1127 has 14.