Model comparison
DeepSeek-V3 vs Qwen2.5 72B Instruct
DeepSeek-V3 is the stronger model overall, scoring 39.5 to 31.9 on the Noometry Index.
Last verified . 41 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 41 benchmarks with published results for both. DeepSeek-V3 scores higher in 6 categories and Qwen2.5 72B Instruct in 2 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-V3 leads 32.1 to 19.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 37.8% for DeepSeek-V3 and 8.1% for Qwen2.5 72B Instruct.
- DeepSeek-V3 is cheaper at $0.24 / $0.90 per million input/output tokens, against $1.40 / $5.60 for Qwen2.5 72B Instruct.
- DeepSeek-V3 accepts more context: 164K tokens versus 131K.
Side by side
| DeepSeek-V3 | Qwen2.5 72B Instruct | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 39.5 | 31.9 |
| Released | 2024-12-26 | 2024-09 |
| Weights | Open | Open |
| Context window | 164K | 131K |
| Max output | 164K | 8K |
| Input $ / M tokens | $0.24 | $1.40 |
| Output $ / M tokens | $0.90 | $5.60 |
| Results tracked | 60 | 43 |
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Category by category
Coding DeepSeek-V3 leads
DeepSeek-V3: 42.3 (#106), Qwen2.5 72B Instruct: 33.2 (#260)
| Benchmark | DeepSeek-V3 | Qwen2.5 72B Instruct |
|---|---|---|
| WeirdML | 36.1% | 16% |
| BigCodeBench Instruct | 50% | 45.8% |
| LMArena Coding | 1368 | 1292 |
| BigCodeBench Complete | 62.2% | 55.9% |
| Aider Polyglot | 55.1% | — |
| SciCode | 35.8% | — |
| LiveBench Coding | 70.9% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3: —, Qwen2.5 72B Instruct: 22.1 (#133)
| Benchmark | DeepSeek-V3 | Qwen2.5 72B Instruct |
|---|---|---|
| METR Time Horizons | 49.6% | 35.8% |
| TheAgentCompany | — | 5.7% |
| BALROG | — | 16.2% |
Reasoning Qwen2.5 72B Instruct leads
DeepSeek-V3: 20.5 (#236), Qwen2.5 72B Instruct: 22.3 (#199)
| Benchmark | DeepSeek-V3 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1365 | 1271 |
| DTBench | 64.8% | 62.9% |
| LMCA | 15.5% | 13.4% |
| BIG-Bench Hard | 87.5% | 79.8% |
| Epoch Capabilities Index | 135.94 | 129 |
| ForecastBench | 59.1 | 57.5 |
| HellaSwag | 88.9% | 84.8% |
| PIQA | 84.7% | 82.6% |
| WinoGrande | 85.2% | 82.3% |
| SimpleBench | 27.2% | — |
| Kagi LLM Benchmark | 52.3% | — |
| CritPt | 0% | — |
| LiveBench Reasoning | 65.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| LiveBench | 66.9% | — |
Math DeepSeek-V3 leads
DeepSeek-V3: 32.1 (#219), Qwen2.5 72B Instruct: 19.3 (#287)
| Benchmark | DeepSeek-V3 | Qwen2.5 72B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | 8.1% |
| Omni-MATH | 40.3% | 33% |
| LMArena Math | 1373 | 1283 |
| MATH Level 5 | 75.5% | 63.2% |
| LiveBench Math | 73.5% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |
Knowledge DeepSeek-V3 leads
DeepSeek-V3: 37.5 (#155), Qwen2.5 72B Instruct: 27.0 (#253)
| Benchmark | DeepSeek-V3 | Qwen2.5 72B Instruct |
|---|---|---|
| GPQA Diamond | 67.6% | 49.1% |
| MMLU-Pro | 72.3% | 63.1% |
| Confabulations | 26.1% | 19.1% |
| GPQA (HELM) | 53.8% | 42.6% |
| LMArena Expert | 1351 | 1245 |
| ARC (AI2) Challenge | 95.3% | 94.5% |
| MMLU | 87.2% | 85.3% |
| TriviaQA | 82.9% | 71.9% |
| Vectara Hallucination Rate | 6.1% | — |
Multilingual DeepSeek-V3 leads
DeepSeek-V3: 48.5 (#143), Qwen2.5 72B Instruct: 41.0 (#213)
| Benchmark | DeepSeek-V3 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Non-English | 1358 | 1252 |
| LMArena Chinese | 1391 | 1272 |
| LMArena French | 1385 | 1280 |
| LMArena German | 1374 | 1234 |
| LMArena Japanese | 1333 | 1180 |
| LMArena Korean | 1319 | 1188 |
| LMArena Russian | 1373 | 1264 |
| LMArena Spanish | 1358 | 1256 |
Instruction Following DeepSeek-V3 leads
DeepSeek-V3: 72.8 (#130), Qwen2.5 72B Instruct: 65.5 (#221)
| Benchmark | DeepSeek-V3 | Qwen2.5 72B Instruct |
|---|---|---|
| IFEval | 83.2% | 80.6% |
| LMArena Instruction Following | 1345 | 1254 |
| LiveBench Instruction Following | 81.5% | — |
Long Context Qwen2.5 72B Instruct leads
DeepSeek-V3: 34.0 (#253), Qwen2.5 72B Instruct: 38.9 (#188)
| Benchmark | DeepSeek-V3 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Longer Query | 1352 | 1282 |
| Fiction.LiveBench | 50% | — |
Writing & Preference DeepSeek-V3 leads
DeepSeek-V3: 57.4 (#130), Qwen2.5 72B Instruct: 46.7 (#215)
| Benchmark | DeepSeek-V3 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Text | 1375 | 1269 |
| LMArena Creative Writing | 1364 | 1221 |
| WildBench | 83% | 80.2% |
| LMArena Multi-Turn | 1389 | 1272 |
| Short-Story Creative Writing | 77% | — |
| EQ-Bench Creative Writing | 1472 | — |
| LiveBench Language | 49.1% | — |
Frequently asked questions
Is DeepSeek-V3 better than Qwen2.5 72B Instruct?
DeepSeek-V3 is the stronger model overall, scoring 39.5 to 31.9 on the Noometry Index.
Which is cheaper, DeepSeek-V3 or Qwen2.5 72B Instruct?
DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; Qwen2.5 72B Instruct lists at $1.40 and $5.60.
Is DeepSeek-V3 or Qwen2.5 72B Instruct better for coding?
DeepSeek-V3 scores higher on coding benchmarks: 42.3 versus 33.2 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3 does, with 164K tokens against 131K.
How many benchmarks do DeepSeek-V3 and Qwen2.5 72B Instruct share?
41 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Qwen2.5 72B Instruct has 43.