Model comparison
DeepSeek-V3.1 vs Qwen3-4B
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 31.9 on the Noometry Index.
Last verified . 1 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 1 benchmark with published results for both. DeepSeek-V3.1 scores higher in 3 categories and Qwen3-4B in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.1 leads 43.7 to 33.0.
Side by side
| DeepSeek-V3.1 | Qwen3-4B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 42.8 | 31.9 |
| Released | 2025-08-21 | 2025-04-29 |
| Weights | Open | Open |
| Context window | 164K | — |
| Max output | 8K | — |
| Input $ / M tokens | $0.25 | — |
| Output $ / M tokens | $0.95 | — |
| Results tracked | 27 | 6 |
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Category by category
Coding Not comparable
DeepSeek-V3.1: 40.3 (#144), Qwen3-4B: —
| Benchmark | DeepSeek-V3.1 | Qwen3-4B |
|---|---|---|
| WeirdML | 38.4% | — |
| LMArena Coding | 1417 | — |
Agentic & Tool Use Not comparable
DeepSeek-V3.1: —, Qwen3-4B: 27.6 (#100)
| Benchmark | DeepSeek-V3.1 | Qwen3-4B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 35.7% |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.9 (#110), Qwen3-4B: 19.2 (#268)
| Benchmark | DeepSeek-V3.1 | Qwen3-4B |
|---|---|---|
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| Chess Puzzles | — | 4% |
| LMArena Hard Prompts | 1417 | — |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| Epoch Capabilities Index | 139.92 | — |
| ForecastBench | 58 | — |
Math DeepSeek-V3.1 leads
DeepSeek-V3.1: 38.9 (#122), Qwen3-4B: 29.7 (#240)
| Benchmark | DeepSeek-V3.1 | Qwen3-4B |
|---|---|---|
| MathArena Final-Answer Competitions | — | 38.5% |
| OTIS Mock AIME 2024-2025 | — | 52.2% |
| LMArena Math | 1420 | — |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.7 (#90), Qwen3-4B: 33.0 (#208)
| Benchmark | DeepSeek-V3.1 | Qwen3-4B |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | 5.7% |
| GPQA Diamond | — | 52.3% |
| LMArena Expert | 1405 | — |
Multilingual Not comparable
DeepSeek-V3.1: 51.6 (#106), Qwen3-4B: —
| Benchmark | DeepSeek-V3.1 | Qwen3-4B |
|---|---|---|
| LMArena Non-English | 1400 | — |
| LMArena Chinese | 1469 | — |
| LMArena French | 1447 | — |
| LMArena German | 1411 | — |
| LMArena Japanese | 1378 | — |
| LMArena Korean | 1337 | — |
| LMArena Russian | 1405 | — |
| LMArena Spanish | 1431 | — |
Instruction Following Not comparable
DeepSeek-V3.1: 73.9 (#110), Qwen3-4B: —
| Benchmark | DeepSeek-V3.1 | Qwen3-4B |
|---|---|---|
| LMArena Instruction Following | 1400 | — |
Long Context Not comparable
DeepSeek-V3.1: 36.3 (#232), Qwen3-4B: —
| Benchmark | DeepSeek-V3.1 | Qwen3-4B |
|---|---|---|
| Fiction.LiveBench | 52.8% | — |
| LMArena Longer Query | 1422 | — |
Writing & Preference Not comparable
DeepSeek-V3.1: 60.3 (#98), Qwen3-4B: —
| Benchmark | DeepSeek-V3.1 | Qwen3-4B |
|---|---|---|
| LMArena Text | 1420 | — |
| LMArena Creative Writing | 1401 | — |
| EQ-Bench Creative Writing | 1436 | — |
| LMArena Multi-Turn | 1408 | — |
Frequently asked questions
Is DeepSeek-V3.1 better than Qwen3-4B?
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 31.9 on the Noometry Index.
How many benchmarks do DeepSeek-V3.1 and Qwen3-4B share?
1 benchmark has published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Qwen3-4B has 6.