Model comparison
DeepSeek V4 Pro vs Qwen3-30B-A3B
DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 38.9 on the Noometry Index. Qwen3-30B-A3B costs 4.6× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.
Last verified . 28 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 28 benchmarks with published results for both. DeepSeek V4 Pro scores higher in 9 categories and Qwen3-30B-A3B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Pro leads 56.5 to 22.2.
- The biggest single-benchmark swing is Chess Puzzles: 47% for DeepSeek V4 Pro and 8% for Qwen3-30B-A3B.
- Qwen3-30B-A3B is cheaper at $0.12 / $0.50 per million input/output tokens, against $0.66 / $1.98 for DeepSeek V4 Pro.
- DeepSeek V4 Pro accepts more context: 1M tokens versus 41K.
Side by side
| DeepSeek V4 Pro | Qwen3-30B-A3B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 54.3 | 38.9 |
| Released | 2026-04-24 | 2025-04-28 |
| Weights | Open | Open |
| Context window | 1M | 41K |
| Max output | 393K | 16K |
| Input $ / M tokens | $0.66 | $0.12 |
| Output $ / M tokens | $1.98 | $0.50 |
| Results tracked | 48 | 32 |
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Category by category
Coding DeepSeek V4 Pro leads
DeepSeek V4 Pro: 52.4 (#34), Qwen3-30B-A3B: 37.5 (#194)
| Benchmark | DeepSeek V4 Pro | Qwen3-30B-A3B |
|---|---|---|
| SciCode | 51% | 33.3% |
| WeirdML | 66.2% | 29.8% |
| LMArena Coding | 1470 | 1416 |
| SWE-bench Verified | 77.6% | — |
| FrontierCode | 28.6% | — |
| LMArena WebDev | 1582 | — |
| ALE-Bench | 1,403 | — |
Agentic & Tool Use DeepSeek V4 Pro leads
DeepSeek V4 Pro: 32.8 (#58), Qwen3-30B-A3B: 29.8 (#82)
| Benchmark | DeepSeek V4 Pro | Qwen3-30B-A3B |
|---|---|---|
| APEX-Agents | 47.3% | — |
| Berkeley Function Calling Leaderboard | — | 41.4% |
| Vending-Bench 2 | 3,285 | — |
Reasoning DeepSeek V4 Pro leads
DeepSeek V4 Pro: 56.5 (#24), Qwen3-30B-A3B: 22.2 (#204)
| Benchmark | DeepSeek V4 Pro | Qwen3-30B-A3B |
|---|---|---|
| Kagi LLM Benchmark | 53.5% | 54.9% |
| CritPt | 18% | 0.3% |
| Chess Puzzles | 47% | 8% |
| LMArena Hard Prompts | 1461 | 1398 |
| DTBench | 93.9% | 69.3% |
| LMCA | 45.5% | 22.4% |
| Epoch Capabilities Index | 155.31 | 139.63 |
| ARC-AGI-2 | 61.3% | — |
| NYT Connections (extended) | 91.3% | — |
| ARC-AGI-1 | 90.5% | — |
| Mystery Game Puzzles | 43% | — |
| Surface Evolver Bench | 40% | — |
| ForecastBench | 56.1 | — |
Math DeepSeek V4 Pro leads
DeepSeek V4 Pro: 64.8 (#30), Qwen3-30B-A3B: 37.4 (#157)
| Benchmark | DeepSeek V4 Pro | Qwen3-30B-A3B |
|---|---|---|
| MathArena Final-Answer Competitions | 76.6% | 47.8% |
| OTIS Mock AIME 2024-2025 | 98.6% | 70.3% |
| LMArena Math | 1455 | 1394 |
| FrontierMath (Tiers 1-3) | 64.6% | — |
| FrontierMath Tier 4 | 26.8% | — |
| ProofBench | 50% | — |
Knowledge DeepSeek V4 Pro leads
DeepSeek V4 Pro: 59.5 (#31), Qwen3-30B-A3B: 41.8 (#105)
| Benchmark | DeepSeek V4 Pro | Qwen3-30B-A3B |
|---|---|---|
| GPQA Diamond | 91.7% | 70.1% |
| LMArena Expert | 1464 | 1396 |
| SimpleQA Verified | 52.9% | — |
| Confabulations | — | 12.3% |
| Vectara Hallucination Rate | 8.6% | — |
Multilingual DeepSeek V4 Pro leads
DeepSeek V4 Pro: 54.4 (#45), Qwen3-30B-A3B: 49.5 (#132)
| Benchmark | DeepSeek V4 Pro | Qwen3-30B-A3B |
|---|---|---|
| LMArena Non-English | 1439 | 1372 |
| LMArena Chinese | 1486 | 1433 |
| LMArena French | 1472 | 1418 |
| LMArena German | 1458 | 1380 |
| LMArena Japanese | 1445 | 1337 |
| LMArena Korean | 1447 | 1331 |
| LMArena Russian | 1453 | 1370 |
| LMArena Spanish | 1458 | 1404 |
Instruction Following DeepSeek V4 Pro leads
DeepSeek V4 Pro: 76.1 (#47), Qwen3-30B-A3B: 72.0 (#142)
| Benchmark | DeepSeek V4 Pro | Qwen3-30B-A3B |
|---|---|---|
| LMArena Instruction Following | 1448 | 1363 |
Long Context DeepSeek V4 Pro leads
DeepSeek V4 Pro: 45.0 (#51), Qwen3-30B-A3B: 31.0 (#283)
| Benchmark | DeepSeek V4 Pro | Qwen3-30B-A3B |
|---|---|---|
| LMArena Longer Query | 1458 | 1379 |
| Fiction.LiveBench | — | 40.6% |
| CL-bench Life | 13.5% | — |
Writing & Preference DeepSeek V4 Pro leads
DeepSeek V4 Pro: 65.5 (#46), Qwen3-30B-A3B: 55.6 (#143)
| Benchmark | DeepSeek V4 Pro | Qwen3-30B-A3B |
|---|---|---|
| LMArena Text | 1451 | 1384 |
| LMArena Creative Writing | 1446 | 1317 |
| LMArena Multi-Turn | 1467 | 1378 |
| Short-Story Creative Writing | — | 75.3% |
| EQ-Bench Creative Writing | 1553 | — |
| EQ-Bench 4 | 1166 | — |
Frequently asked questions
Is DeepSeek V4 Pro better than Qwen3-30B-A3B?
DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 38.9 on the Noometry Index. Qwen3-30B-A3B costs 4.6× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.
Which is cheaper, DeepSeek V4 Pro 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 V4 Pro lists at $0.66 and $1.98.
Is DeepSeek V4 Pro or Qwen3-30B-A3B better for coding?
DeepSeek V4 Pro scores higher on coding benchmarks: 52.4 versus 37.5 in the Noometry coding category.
Which has the bigger context window?
DeepSeek V4 Pro does, with 1M tokens against 41K.
How many benchmarks do DeepSeek V4 Pro and Qwen3-30B-A3B share?
28 benchmarks have published results for both models. DeepSeek V4 Pro has 48 scored results on Noometry and Qwen3-30B-A3B has 32.