Model comparison
DeepSeek-V3 vs Qwen3.5-Flash
Qwen3.5-Flash is the stronger model overall, scoring 42.5 to 39.5 on the Noometry Index.
Last verified . 24 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 24 benchmarks with published results for both. DeepSeek-V3 scores higher in 2 categories and Qwen3.5-Flash in 6 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.5-Flash leads 33.7 to 20.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 37.8% for DeepSeek-V3 and 84.4% for Qwen3.5-Flash.
- Qwen3.5-Flash is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.24 / $0.90 for DeepSeek-V3.
- Qwen3.5-Flash accepts more context: 1M tokens versus 164K.
- DeepSeek-V3 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3 | Qwen3.5-Flash | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 39.5 | 42.5 |
| Released | 2024-12-26 | 2026-02-23 |
| Weights | Open | Proprietary |
| Context window | 164K | 1M |
| Max output | 164K | 66K |
| Input $ / M tokens | $0.24 | $0.10 |
| Output $ / M tokens | $0.90 | $0.40 |
| Results tracked | 60 | 32 |
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Category by category
Coding DeepSeek-V3 leads
DeepSeek-V3: 42.3 (#106), Qwen3.5-Flash: 34.2 (#242)
| Benchmark | DeepSeek-V3 | Qwen3.5-Flash |
|---|---|---|
| LMArena Coding | 1368 | 1412 |
| Aider Polyglot | 55.1% | — |
| LMArena WebDev | — | 1244 |
| SciCode | 35.8% | — |
| WeirdML | 36.1% | — |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| BigCodeBench Complete | 62.2% | — |
| ALE-Bench | — | 221.8 |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3: —, Qwen3.5-Flash: —
| Benchmark | DeepSeek-V3 | Qwen3.5-Flash |
|---|---|---|
| METR Time Horizons | 49.6% | — |
| Vending-Bench 2 | — | 462.69 |
Reasoning Qwen3.5-Flash leads
DeepSeek-V3: 20.5 (#236), Qwen3.5-Flash: 33.7 (#72)
| Benchmark | DeepSeek-V3 | Qwen3.5-Flash |
|---|---|---|
| LMArena Hard Prompts | 1365 | 1403 |
| DTBench | 64.8% | 82.9% |
| LMCA | 15.5% | 29.1% |
| Epoch Capabilities Index | 135.94 | 143.98 |
| SimpleBench | 27.2% | — |
| Kagi LLM Benchmark | 52.3% | — |
| CritPt | 0% | — |
| Chess Puzzles | — | 21% |
| LiveBench Reasoning | 65.8% | — |
| Mystery Game Puzzles | — | 20% |
| 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 Qwen3.5-Flash leads
DeepSeek-V3: 32.1 (#219), Qwen3.5-Flash: 37.4 (#158)
| Benchmark | DeepSeek-V3 | Qwen3.5-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | 84.4% |
| LMArena Math | 1373 | 1407 |
| FrontierMath (Feb 2025 set) | 1.7% | 6.2% |
| FrontierMath (Tiers 1-3) | — | 18.2% |
| Omni-MATH | 40.3% | — |
| LiveBench Math | 73.5% | — |
| MATH Level 5 | 75.5% | — |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Qwen3.5-Flash leads
DeepSeek-V3: 37.5 (#155), Qwen3.5-Flash: 43.2 (#93)
| Benchmark | DeepSeek-V3 | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 67.6% | 82.3% |
| Vectara Hallucination Rate | 6.1% | 10.5% |
| LMArena Expert | 1351 | 1407 |
| SimpleQA Verified | — | 20.3% |
| MMLU-Pro | 72.3% | — |
| Confabulations | 26.1% | — |
| GPQA (HELM) | 53.8% | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |
Multilingual Qwen3.5-Flash leads
DeepSeek-V3: 48.5 (#143), Qwen3.5-Flash: 50.5 (#121)
| Benchmark | DeepSeek-V3 | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1358 | 1385 |
| LMArena Chinese | 1391 | 1446 |
| LMArena French | 1385 | 1412 |
| LMArena German | 1374 | 1390 |
| LMArena Japanese | 1333 | 1368 |
| LMArena Korean | 1319 | 1344 |
| LMArena Russian | 1373 | 1379 |
| LMArena Spanish | 1358 | 1400 |
Instruction Following Too close to call
DeepSeek-V3: 72.8 (#130), Qwen3.5-Flash: 72.6 (#139)
| Benchmark | DeepSeek-V3 | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1345 | 1374 |
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |
Long Context Qwen3.5-Flash leads
DeepSeek-V3: 34.0 (#253), Qwen3.5-Flash: 42.4 (#124)
| Benchmark | DeepSeek-V3 | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1352 | 1392 |
| Fiction.LiveBench | 50% | — |
Writing & Preference Too close to call
DeepSeek-V3: 57.4 (#130), Qwen3.5-Flash: 57.9 (#122)
| Benchmark | DeepSeek-V3 | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1375 | 1397 |
| LMArena Creative Writing | 1364 | 1343 |
| LMArena Multi-Turn | 1389 | 1393 |
| Short-Story Creative Writing | 77% | — |
| EQ-Bench Creative Writing | 1472 | — |
| WildBench | 83% | — |
| LiveBench Language | 49.1% | — |
Frequently asked questions
Is DeepSeek-V3 better than Qwen3.5-Flash?
Qwen3.5-Flash is the stronger model overall, scoring 42.5 to 39.5 on the Noometry Index.
Which is cheaper, DeepSeek-V3 or Qwen3.5-Flash?
Qwen3.5-Flash is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; DeepSeek-V3 lists at $0.24 and $0.90.
Is DeepSeek-V3 or Qwen3.5-Flash better for coding?
DeepSeek-V3 scores higher on coding benchmarks: 42.3 versus 34.2 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5-Flash does, with 1M tokens against 164K.
How many benchmarks do DeepSeek-V3 and Qwen3.5-Flash share?
24 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Qwen3.5-Flash has 32.