Model comparison
DeepSeek-V3.2-Exp vs Qwen1.5-14B
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 32.1 on the Noometry Index.
Last verified . 16 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 16 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 8 categories and Qwen1.5-14B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.2-Exp leads 62.5 to 33.7.
Side by side
| DeepSeek-V3.2-Exp | Qwen1.5-14B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 44.9 | 32.1 |
| Released | 2025-09-29 | 2024-02-04 |
| Weights | Open | Open |
| Context window | 164K | — |
| Max output | 66K | — |
| Input $ / M tokens | $0.26 | — |
| Output $ / M tokens | $0.38 | — |
| Results tracked | 48 | 17 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.4 (#61), Qwen1.5-14B: 32.5 (#259)
| Benchmark | DeepSeek-V3.2-Exp | Qwen1.5-14B |
|---|---|---|
| LMArena Coding | 1454 | 1138 |
| 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), Qwen1.5-14B: —
| Benchmark | DeepSeek-V3.2-Exp | Qwen1.5-14B |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |
Reasoning DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 22.0 (#185), Qwen1.5-14B: 20.5 (#217)
| Benchmark | DeepSeek-V3.2-Exp | Qwen1.5-14B |
|---|---|---|
| LMArena Hard Prompts | 1433 | 1113 |
| 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), Qwen1.5-14B: 29.9 (#214)
| Benchmark | DeepSeek-V3.2-Exp | Qwen1.5-14B |
|---|---|---|
| LMArena Math | 1435 | 1125 |
| 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), Qwen1.5-14B: 29.3 (#230)
| Benchmark | DeepSeek-V3.2-Exp | Qwen1.5-14B |
|---|---|---|
| LMArena Expert | 1436 | 1094 |
| GPQA Diamond | 83.4% | — |
| Vectara Hallucination Rate | 5.3% | — |
| MMLU | — | 68.6% |
Multilingual DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 52.4 (#85), Qwen1.5-14B: 30.8 (#257)
| Benchmark | DeepSeek-V3.2-Exp | Qwen1.5-14B |
|---|---|---|
| LMArena Non-English | 1409 | 1095 |
| LMArena Chinese | 1461 | 1147 |
| LMArena French | 1435 | 1115 |
| LMArena German | 1440 | 1043 |
| LMArena Japanese | 1377 | 1019 |
| LMArena Russian | 1423 | 1046 |
| LMArena Spanish | 1441 | 1085 |
| LMArena Korean | 1372 | — |
Instruction Following DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 74.7 (#89), Qwen1.5-14B: 57.1 (#266)
| Benchmark | DeepSeek-V3.2-Exp | Qwen1.5-14B |
|---|---|---|
| LMArena Instruction Following | 1413 | 1102 |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#15), Qwen1.5-14B: 33.7 (#252)
| Benchmark | DeepSeek-V3.2-Exp | Qwen1.5-14B |
|---|---|---|
| LMArena Longer Query | 1428 | 1113 |
| 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), Qwen1.5-14B: 33.7 (#270)
| Benchmark | DeepSeek-V3.2-Exp | Qwen1.5-14B |
|---|---|---|
| LMArena Text | 1425 | 1128 |
| LMArena Creative Writing | 1403 | 1091 |
| LMArena Multi-Turn | 1427 | 1110 |
| EQ-Bench Creative Writing | 1515 | — |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than Qwen1.5-14B?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 32.1 on the Noometry Index.
Is DeepSeek-V3.2-Exp or Qwen1.5-14B better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.4 versus 32.5 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.2-Exp and Qwen1.5-14B share?
16 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and Qwen1.5-14B has 17.