Model comparison
DeepSeek-V3 vs Qwen3.5 27B
Qwen3.5 27B is the stronger model overall, scoring 41.4 to 39.2 on the Noometry Index. DeepSeek-V3 costs 2.0× less per token, which makes it the better buy when Qwen3.5 27B's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 21 benchmarks with published results for both. DeepSeek-V3 scores higher in 2 categories and Qwen3.5 27B in 6 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in long context, where Qwen3.5 27B leads 43.1 to 34.0.
- The biggest single-benchmark swing is LMCA: 15.5% for DeepSeek-V3 and 34% for Qwen3.5 27B.
- DeepSeek-V3 is cheaper at $0.24 / $0.90 per million input/output tokens, against $0.30 / $2.40 for Qwen3.5 27B.
- Qwen3.5 27B accepts more context: 262K tokens versus 164K.
Side by side
| DeepSeek-V3 | Qwen3.5 27B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 39.2 | 41.4 |
| Released | 2024-12-26 | 2026-02-23 |
| Weights | Open | Open |
| Context window | 164K | 262K |
| Max output | 164K | 66K |
| Input $ / M tokens | $0.24 | $0.30 |
| Output $ / M tokens | $0.90 | $2.40 |
| Results tracked | 60 | 28 |
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Category by category
Coding DeepSeek-V3 leads
DeepSeek-V3: 41.9 (#99), Qwen3.5 27B: 38.6 (#159)
| Benchmark | DeepSeek-V3 | Qwen3.5 27B |
|---|---|---|
| WeirdML | 36.1% | 39.5% |
| LMArena Coding | 1368 | 1427 |
| Aider Polyglot | 55.1% | — |
| LMArena WebDev | — | 1357 |
| SciCode | 35.8% | — |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| BigCodeBench Complete | 62.2% | — |
| ALE-Bench | — | 349.45 |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3: —, Qwen3.5 27B: —
| Benchmark | DeepSeek-V3 | Qwen3.5 27B |
|---|---|---|
| METR Time Horizons | 49.6% | — |
| Vending-Bench 2 | — | 201.98 |
Reasoning Qwen3.5 27B leads
DeepSeek-V3: 20.0 (#226), Qwen3.5 27B: 26.6 (#116)
| Benchmark | DeepSeek-V3 | Qwen3.5 27B |
|---|---|---|
| LMArena Hard Prompts | 1365 | 1414 |
| DTBench | 64.8% | 82.4% |
| LMCA | 15.5% | 34% |
| SimpleBench | 27.2% | — |
| Kagi LLM Benchmark | 52.3% | — |
| NYT Connections (extended) | — | 47.9% |
| CritPt | 0% | — |
| Thematic Generalization | — | 45.5% |
| LiveBench Reasoning | 65.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| BIG-Bench Hard | 87.5% | — |
| Epoch Capabilities Index | 135.95 | — |
| ForecastBench | 59.1 | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |
Math Qwen3.5 27B leads
DeepSeek-V3: 30.4 (#208), Qwen3.5 27B: 36.8 (#103)
| Benchmark | DeepSeek-V3 | Qwen3.5 27B |
|---|---|---|
| LMArena Math | 1373 | 1428 |
| MathArena Final-Answer Competitions | — | 56.7% |
| OTIS Mock AIME 2024-2025 | 37.8% | — |
| Omni-MATH | 40.3% | — |
| LiveBench Math | 73.5% | — |
| MATH Level 5 | 75.5% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |
Knowledge Too close to call
DeepSeek-V3: 37.5 (#147), Qwen3.5 27B: 37.4 (#149)
| Benchmark | DeepSeek-V3 | Qwen3.5 27B |
|---|---|---|
| Vectara Hallucination Rate | 6.1% | 12.1% |
| LMArena Expert | 1352 | 1428 |
| GPQA Diamond | 67.6% | — |
| MMLU-Pro | 72.3% | — |
| Confabulations | 26.1% | — |
| GPQA (HELM) | 53.8% | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |
Multimodal Not comparable
DeepSeek-V3: —, Qwen3.5 27B: 38.7 (#56)
| Benchmark | DeepSeek-V3 | Qwen3.5 27B |
|---|---|---|
| LMArena Vision | — | 1240 |
Multilingual Qwen3.5 27B leads
DeepSeek-V3: 48.8 (#138), Qwen3.5 27B: 51.0 (#112)
| Benchmark | DeepSeek-V3 | Qwen3.5 27B |
|---|---|---|
| LMArena Non-English | 1358 | 1390 |
| LMArena Chinese | 1390 | 1478 |
| LMArena French | 1386 | 1409 |
| LMArena German | 1374 | 1393 |
| LMArena Japanese | 1333 | 1345 |
| LMArena Korean | 1321 | 1359 |
| LMArena Russian | 1372 | 1389 |
| LMArena Spanish | 1358 | 1408 |
Instruction Following Too close to call
DeepSeek-V3: 73.0 (#125), Qwen3.5 27B: 73.7 (#114)
| Benchmark | DeepSeek-V3 | Qwen3.5 27B |
|---|---|---|
| LMArena Instruction Following | 1345 | 1393 |
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |
Long Context Qwen3.5 27B leads
DeepSeek-V3: 34.0 (#248), Qwen3.5 27B: 43.1 (#103)
| Benchmark | DeepSeek-V3 | Qwen3.5 27B |
|---|---|---|
| LMArena Longer Query | 1353 | 1413 |
| Fiction.LiveBench | 50% | — |
Writing & Preference Qwen3.5 27B leads
DeepSeek-V3: 57.6 (#124), Qwen3.5 27B: 59.5 (#106)
| Benchmark | DeepSeek-V3 | Qwen3.5 27B |
|---|---|---|
| LMArena Text | 1375 | 1409 |
| LMArena Creative Writing | 1364 | 1362 |
| LMArena Multi-Turn | 1389 | 1410 |
| 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 27B?
Qwen3.5 27B is the stronger model overall, scoring 41.4 to 39.2 on the Noometry Index. DeepSeek-V3 costs 2.0× less per token, which makes it the better buy when Qwen3.5 27B's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3 or Qwen3.5 27B?
DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; Qwen3.5 27B lists at $0.30 and $2.40.
Is DeepSeek-V3 or Qwen3.5 27B better for coding?
DeepSeek-V3 scores higher on coding benchmarks: 41.9 versus 38.6 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5 27B does, with 262K tokens against 164K.
How many benchmarks do DeepSeek-V3 and Qwen3.5 27B share?
21 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Qwen3.5 27B has 28.