Model comparison
DeepSeek-V3 vs Qwen3-30B-A3B
DeepSeek-V3 and Qwen3-30B-A3B score almost the same on the Noometry Index (39.2 vs 38.5), so choose on price, context window or the category you care about most.
Last verified . 29 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 29 benchmarks with published results for both. DeepSeek-V3 scores higher in 4 categories and Qwen3-30B-A3B in 4 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3-30B-A3B leads 36.0 to 30.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 37.8% for DeepSeek-V3 and 70.3% for Qwen3-30B-A3B.
- Qwen3-30B-A3B is cheaper at $0.12 / $0.50 per million input/output tokens, against $0.24 / $0.90 for DeepSeek-V3.
- DeepSeek-V3 accepts more context: 164K tokens versus 41K.
Side by side
| DeepSeek-V3 | Qwen3-30B-A3B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 39.2 | 38.5 |
| Released | 2024-12-26 | 2025-04-28 |
| Weights | Open | Open |
| Context window | 164K | 41K |
| Max output | 164K | 16K |
| Input $ / M tokens | $0.24 | $0.12 |
| Output $ / M tokens | $0.90 | $0.50 |
| Results tracked | 60 | 32 |
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Category by category
Coding DeepSeek-V3 leads
DeepSeek-V3: 41.9 (#99), Qwen3-30B-A3B: 37.0 (#188)
| Benchmark | DeepSeek-V3 | Qwen3-30B-A3B |
|---|---|---|
| SciCode | 35.8% | 33.3% |
| WeirdML | 36.1% | 29.8% |
| LMArena Coding | 1368 | 1416 |
| Aider Polyglot | 55.1% | — |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| BigCodeBench Complete | 62.2% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3: —, Qwen3-30B-A3B: 29.5 (#75)
| Benchmark | DeepSeek-V3 | Qwen3-30B-A3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41.4% |
| METR Time Horizons | 49.6% | — |
Reasoning Qwen3-30B-A3B leads
DeepSeek-V3: 20.0 (#226), Qwen3-30B-A3B: 21.6 (#193)
| Benchmark | DeepSeek-V3 | Qwen3-30B-A3B |
|---|---|---|
| Kagi LLM Benchmark | 52.3% | 54.9% |
| CritPt | 0% | 0.3% |
| LMArena Hard Prompts | 1365 | 1398 |
| DTBench | 64.8% | 69.3% |
| LMCA | 15.5% | 22.4% |
| Epoch Capabilities Index | 135.95 | 139.63 |
| SimpleBench | 27.2% | — |
| Chess Puzzles | — | 8% |
| LiveBench Reasoning | 65.8% | — |
| 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 Qwen3-30B-A3B leads
DeepSeek-V3: 30.4 (#208), Qwen3-30B-A3B: 36.0 (#133)
| Benchmark | DeepSeek-V3 | Qwen3-30B-A3B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | 70.3% |
| LMArena Math | 1373 | 1394 |
| MathArena Final-Answer Competitions | — | 47.8% |
| Omni-MATH | 40.3% | — |
| LiveBench Math | 73.5% | — |
| MATH Level 5 | 75.5% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |
Knowledge Qwen3-30B-A3B leads
DeepSeek-V3: 37.5 (#147), Qwen3-30B-A3B: 41.7 (#96)
| Benchmark | DeepSeek-V3 | Qwen3-30B-A3B |
|---|---|---|
| GPQA Diamond | 67.6% | 70.1% |
| Confabulations | 26.1% | 12.3% |
| LMArena Expert | 1352 | 1395 |
| MMLU-Pro | 72.3% | — |
| Vectara Hallucination Rate | 6.1% | — |
| GPQA (HELM) | 53.8% | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |
Multilingual Too close to call
DeepSeek-V3: 48.8 (#138), Qwen3-30B-A3B: 49.8 (#126)
| Benchmark | DeepSeek-V3 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Non-English | 1358 | 1372 |
| LMArena Chinese | 1390 | 1434 |
| LMArena French | 1386 | 1419 |
| LMArena German | 1374 | 1379 |
| LMArena Japanese | 1333 | 1338 |
| LMArena Korean | 1321 | 1333 |
| LMArena Russian | 1372 | 1369 |
| LMArena Spanish | 1358 | 1403 |
Instruction Following Too close to call
DeepSeek-V3: 73.0 (#125), Qwen3-30B-A3B: 72.2 (#137)
| Benchmark | DeepSeek-V3 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Instruction Following | 1345 | 1364 |
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |
Long Context DeepSeek-V3 leads
DeepSeek-V3: 34.0 (#248), Qwen3-30B-A3B: 31.0 (#278)
| Benchmark | DeepSeek-V3 | Qwen3-30B-A3B |
|---|---|---|
| Fiction.LiveBench | 50% | 40.6% |
| LMArena Longer Query | 1353 | 1379 |
Writing & Preference DeepSeek-V3 leads
DeepSeek-V3: 57.6 (#124), Qwen3-30B-A3B: 55.9 (#138)
| Benchmark | DeepSeek-V3 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Text | 1375 | 1384 |
| LMArena Creative Writing | 1364 | 1318 |
| Short-Story Creative Writing | 77% | 75.3% |
| LMArena Multi-Turn | 1389 | 1378 |
| EQ-Bench Creative Writing | 1472 | — |
| WildBench | 83% | — |
| LiveBench Language | 49.1% | — |
Frequently asked questions
Is DeepSeek-V3 better than Qwen3-30B-A3B?
DeepSeek-V3 and Qwen3-30B-A3B score almost the same on the Noometry Index (39.2 vs 38.5), so choose on price, context window or the category you care about most.
Which is cheaper, DeepSeek-V3 or Qwen3-30B-A3B?
Qwen3-30B-A3B is cheaper. It lists at $0.12 per million input tokens and $0.50 per million output tokens; DeepSeek-V3 lists at $0.24 and $0.90.
Is DeepSeek-V3 or Qwen3-30B-A3B better for coding?
DeepSeek-V3 scores higher on coding benchmarks: 41.9 versus 37.0 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3 does, with 164K tokens against 41K.
How many benchmarks do DeepSeek-V3 and Qwen3-30B-A3B share?
29 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Qwen3-30B-A3B has 32.