Model comparison
DeepSeek-V3 vs Qwen3 Max
Qwen3 Max is the stronger model overall, scoring 43.5 to 39.2 on the Noometry Index. DeepSeek-V3 costs 5.9× less per token, which makes it the better buy when Qwen3 Max's lead doesn't matter for your workload.
Last verified . 25 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 25 benchmarks with published results for both. DeepSeek-V3 scores higher in 0 categories and Qwen3 Max in 8 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3 Max leads 48.1 to 37.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 37.8% for DeepSeek-V3 and 73.3% for Qwen3 Max.
- DeepSeek-V3 is cheaper at $0.24 / $0.90 per million input/output tokens, against $1.20 / $6 for Qwen3 Max.
- Qwen3 Max accepts more context: 262K tokens versus 164K.
- DeepSeek-V3 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3 | Qwen3 Max | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 39.2 | 43.5 |
| Released | 2024-12-26 | 2025-09-23 |
| Weights | Open | Proprietary |
| Context window | 164K | 262K |
| Max output | 164K | 66K |
| Input $ / M tokens | $0.24 | $1.20 |
| Output $ / M tokens | $0.90 | $6 |
| Results tracked | 60 | 33 |
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Category by category
Coding Too close to call
DeepSeek-V3: 41.9 (#99), Qwen3 Max: 42.4 (#93)
| Benchmark | DeepSeek-V3 | Qwen3 Max |
|---|---|---|
| LMArena Coding | 1368 | 1456 |
| Aider Polyglot | 55.1% | — |
| SciCode | 35.8% | — |
| WeirdML | 36.1% | — |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| BigCodeBench Complete | 62.2% | — |
| ALE-Bench | — | 370.45 |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3: —, Qwen3 Max: —
| Benchmark | DeepSeek-V3 | Qwen3 Max |
|---|---|---|
| METR Time Horizons | 49.6% | — |
| Vending-Bench 2 | — | 71.56 |
Reasoning Qwen3 Max leads
DeepSeek-V3: 20.0 (#226), Qwen3 Max: 22.0 (#186)
| Benchmark | DeepSeek-V3 | Qwen3 Max |
|---|---|---|
| Kagi LLM Benchmark | 52.3% | 72.5% |
| LMArena Hard Prompts | 1365 | 1448 |
| DTBench | 64.8% | 82.1% |
| LMCA | 15.5% | 28.3% |
| Epoch Capabilities Index | 135.95 | 142.41 |
| SimpleBench | 27.2% | — |
| NYT Connections (extended) | — | 30.1% |
| CritPt | 0% | — |
| Chess Puzzles | — | 4% |
| LiveBench Reasoning | 65.8% | — |
| Mystery Game Puzzles | — | 5% |
| LiveBench Data Analysis | 60.9% | — |
| BIG-Bench Hard | 87.5% | — |
| ForecastBench | 59.1 | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |
Math Qwen3 Max leads
DeepSeek-V3: 30.4 (#208), Qwen3 Max: 37.6 (#94)
| Benchmark | DeepSeek-V3 | Qwen3 Max |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | 73.3% |
| LMArena Math | 1373 | 1447 |
| MATH Level 5 | 75.5% | 97.1% |
| FrontierMath (Tiers 1-3) | — | 18.9% |
| Omni-MATH | 40.3% | — |
| LiveBench Math | 73.5% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |
Knowledge Qwen3 Max leads
DeepSeek-V3: 37.5 (#147), Qwen3 Max: 48.1 (#72)
| Benchmark | DeepSeek-V3 | Qwen3 Max |
|---|---|---|
| GPQA Diamond | 67.6% | 72.6% |
| LMArena Expert | 1352 | 1457 |
| SimpleQA Verified | — | 48.7% |
| MMLU-Pro | 72.3% | — |
| Confabulations | 26.1% | — |
| Vectara Hallucination Rate | 6.1% | — |
| GPQA (HELM) | 53.8% | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |
Multilingual Qwen3 Max leads
DeepSeek-V3: 48.8 (#138), Qwen3 Max: 53.9 (#58)
| Benchmark | DeepSeek-V3 | Qwen3 Max |
|---|---|---|
| LMArena Non-English | 1358 | 1429 |
| LMArena Chinese | 1390 | 1479 |
| LMArena French | 1386 | 1450 |
| LMArena German | 1374 | 1463 |
| LMArena Japanese | 1333 | 1399 |
| LMArena Korean | 1321 | 1397 |
| LMArena Russian | 1372 | 1428 |
| LMArena Spanish | 1358 | 1463 |
Instruction Following Qwen3 Max leads
DeepSeek-V3: 73.0 (#125), Qwen3 Max: 75.0 (#81)
| Benchmark | DeepSeek-V3 | Qwen3 Max |
|---|---|---|
| LMArena Instruction Following | 1345 | 1420 |
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |
Long Context Qwen3 Max leads
DeepSeek-V3: 34.0 (#248), Qwen3 Max: 41.6 (#129)
| Benchmark | DeepSeek-V3 | Qwen3 Max |
|---|---|---|
| Fiction.LiveBench | 50% | 66.7% |
| LMArena Longer Query | 1353 | 1439 |
| CL-bench | — | 14.5% |
Writing & Preference Qwen3 Max leads
DeepSeek-V3: 57.6 (#124), Qwen3 Max: 62.6 (#72)
| Benchmark | DeepSeek-V3 | Qwen3 Max |
|---|---|---|
| LMArena Text | 1375 | 1439 |
| LMArena Creative Writing | 1364 | 1401 |
| LMArena Multi-Turn | 1389 | 1446 |
| Short-Story Creative Writing | 77% | — |
| EQ-Bench Creative Writing | 1472 | — |
| WildBench | 83% | — |
| LiveBench Language | 49.1% | — |
Frequently asked questions
Is DeepSeek-V3 better than Qwen3 Max?
Qwen3 Max is the stronger model overall, scoring 43.5 to 39.2 on the Noometry Index. DeepSeek-V3 costs 5.9× less per token, which makes it the better buy when Qwen3 Max's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3 or Qwen3 Max?
DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; Qwen3 Max lists at $1.20 and $6.
Is DeepSeek-V3 or Qwen3 Max better for coding?
They score almost the same on coding (41.9 vs 42.4); test both on your own repository before choosing.
Which has the bigger context window?
Qwen3 Max does, with 262K tokens against 164K.
How many benchmarks do DeepSeek-V3 and Qwen3 Max share?
25 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Qwen3 Max has 33.