Model comparison
DeepSeek-V3 vs Qwen1.5-14B
DeepSeek-V3 is the stronger model overall, scoring 39.2 to 32.1 on the Noometry Index.
Last verified . 17 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 17 benchmarks with published results for both. DeepSeek-V3 scores higher in 7 categories and Qwen1.5-14B in 1 category; 5 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3 leads 57.6 to 33.7.
Side by side
| DeepSeek-V3 | Qwen1.5-14B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 39.2 | 32.1 |
| Released | 2024-12-26 | 2024-02-04 |
| Weights | Open | Open |
| Context window | 164K | — |
| Max output | 164K | — |
| Input $ / M tokens | $0.24 | — |
| Output $ / M tokens | $0.90 | — |
| Results tracked | 60 | 17 |
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Category by category
Coding DeepSeek-V3 leads
DeepSeek-V3: 41.9 (#99), Qwen1.5-14B: 32.5 (#259)
| Benchmark | DeepSeek-V3 | Qwen1.5-14B |
|---|---|---|
| LMArena Coding | 1368 | 1138 |
| Aider Polyglot | 55.1% | — |
| SciCode | 35.8% | — |
| WeirdML | 36.1% | — |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| BigCodeBench Complete | 62.2% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3: —, Qwen1.5-14B: —
| Benchmark | DeepSeek-V3 | Qwen1.5-14B |
|---|---|---|
| METR Time Horizons | 49.6% | — |
Reasoning Too close to call
DeepSeek-V3: 20.0 (#226), Qwen1.5-14B: 20.5 (#217)
| Benchmark | DeepSeek-V3 | Qwen1.5-14B |
|---|---|---|
| LMArena Hard Prompts | 1365 | 1113 |
| SimpleBench | 27.2% | — |
| Kagi LLM Benchmark | 52.3% | — |
| CritPt | 0% | — |
| LiveBench Reasoning | 65.8% | — |
| DTBench | 64.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| LMCA | 15.5% | — |
| BIG-Bench Hard | 87.5% | — |
| Epoch Capabilities Index | 135.95 | — |
| ForecastBench | 59.1 | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |
Math Too close to call
DeepSeek-V3: 30.4 (#208), Qwen1.5-14B: 29.9 (#214)
| Benchmark | DeepSeek-V3 | Qwen1.5-14B |
|---|---|---|
| LMArena Math | 1373 | 1125 |
| OTIS Mock AIME 2024-2025 | 37.8% | — |
| Omni-MATH | 40.3% | — |
| LiveBench Math | 73.5% | — |
| MATH Level 5 | 75.5% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |
Knowledge DeepSeek-V3 leads
DeepSeek-V3: 37.5 (#147), Qwen1.5-14B: 29.3 (#230)
| Benchmark | DeepSeek-V3 | Qwen1.5-14B |
|---|---|---|
| LMArena Expert | 1352 | 1094 |
| MMLU | 87.2% | 68.6% |
| GPQA Diamond | 67.6% | — |
| MMLU-Pro | 72.3% | — |
| Confabulations | 26.1% | — |
| Vectara Hallucination Rate | 6.1% | — |
| GPQA (HELM) | 53.8% | — |
| ARC (AI2) Challenge | 95.3% | — |
| TriviaQA | 82.9% | — |
Multilingual DeepSeek-V3 leads
DeepSeek-V3: 48.8 (#138), Qwen1.5-14B: 30.8 (#257)
| Benchmark | DeepSeek-V3 | Qwen1.5-14B |
|---|---|---|
| LMArena Non-English | 1358 | 1095 |
| LMArena Chinese | 1390 | 1147 |
| LMArena French | 1386 | 1115 |
| LMArena German | 1374 | 1043 |
| LMArena Japanese | 1333 | 1019 |
| LMArena Russian | 1372 | 1046 |
| LMArena Spanish | 1358 | 1085 |
| LMArena Korean | 1321 | — |
Instruction Following DeepSeek-V3 leads
DeepSeek-V3: 73.0 (#125), Qwen1.5-14B: 57.1 (#266)
| Benchmark | DeepSeek-V3 | Qwen1.5-14B |
|---|---|---|
| LMArena Instruction Following | 1345 | 1102 |
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |
Long Context Too close to call
DeepSeek-V3: 34.0 (#248), Qwen1.5-14B: 33.7 (#252)
| Benchmark | DeepSeek-V3 | Qwen1.5-14B |
|---|---|---|
| LMArena Longer Query | 1353 | 1113 |
| Fiction.LiveBench | 50% | — |
Writing & Preference DeepSeek-V3 leads
DeepSeek-V3: 57.6 (#124), Qwen1.5-14B: 33.7 (#270)
| Benchmark | DeepSeek-V3 | Qwen1.5-14B |
|---|---|---|
| LMArena Text | 1375 | 1128 |
| LMArena Creative Writing | 1364 | 1091 |
| LMArena Multi-Turn | 1389 | 1110 |
| Short-Story Creative Writing | 77% | — |
| EQ-Bench Creative Writing | 1472 | — |
| WildBench | 83% | — |
| LiveBench Language | 49.1% | — |
Frequently asked questions
Is DeepSeek-V3 better than Qwen1.5-14B?
DeepSeek-V3 is the stronger model overall, scoring 39.2 to 32.1 on the Noometry Index.
Is DeepSeek-V3 or Qwen1.5-14B better for coding?
DeepSeek-V3 scores higher on coding benchmarks: 41.9 versus 32.5 in the Noometry coding category.
How many benchmarks do DeepSeek-V3 and Qwen1.5-14B share?
17 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Qwen1.5-14B has 17.