Model comparison
DeepSeek-R1 vs Qwen3.5 27B
DeepSeek-R1 and Qwen3.5 27B score almost the same on the Noometry Index (42.3 vs 41.9), so choose on price, context window or the category you care about most.
Last verified . 20 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 20 benchmarks with published results for both. DeepSeek-R1 scores higher in 6 categories and Qwen3.5 27B in 2 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.5 27B leads 27.5 to 18.6.
- Qwen3.5 27B is cheaper at $0.30 / $2.40 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
- Qwen3.5 27B accepts more context: 262K tokens versus 164K.
- Qwen3.5 27B has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-R1 | Qwen3.5 27B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 42.3 | 41.9 |
| Released | 2025-01-20 | 2026-02-23 |
| Weights | Proprietary | Open |
| Context window | 164K | 262K |
| Max output | 64K | 66K |
| Input $ / M tokens | $0.50 | $0.30 |
| Output $ / M tokens | $2.15 | $2.40 |
| Results tracked | 52 | 28 |
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Category by category
Coding DeepSeek-R1 leads
DeepSeek-R1: 46.3 (#68), Qwen3.5 27B: 38.9 (#168)
| Benchmark | DeepSeek-R1 | Qwen3.5 27B |
|---|---|---|
| WeirdML | 41.6% | 39.5% |
| LMArena Coding | 1427 | 1427 |
| ALE-Bench | 804.12 | 349.45 |
| Aider Polyglot | 71.4% | — |
| LMArena WebDev | — | 1358 |
| SciCode | 35.7% | — |
| LiveBench Coding | 66.7% | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use Not comparable
DeepSeek-R1: 30.7 (#75), Qwen3.5 27B: —
| Benchmark | DeepSeek-R1 | Qwen3.5 27B |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
| Vending-Bench 2 | — | 201.98 |
Reasoning Qwen3.5 27B leads
DeepSeek-R1: 18.6 (#278), Qwen3.5 27B: 27.5 (#117)
| Benchmark | DeepSeek-R1 | Qwen3.5 27B |
|---|---|---|
| LMArena Hard Prompts | 1416 | 1414 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| Kagi LLM Benchmark | 69.4% | — |
| NYT Connections (extended) | — | 47.9% |
| ARC-AGI-1 | 21.2% | — |
| CritPt | 1.1% | — |
| Thematic Generalization | — | 45.5% |
| LiveBench Reasoning | 83.2% | — |
| DTBench | — | 82.4% |
| LiveBench Data Analysis | 69.8% | — |
| LMCA | — | 34% |
| Epoch Capabilities Index | 141.29 | — |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math DeepSeek-R1 leads
DeepSeek-R1: 43.8 (#79), Qwen3.5 27B: 38.8 (#127)
| Benchmark | DeepSeek-R1 | Qwen3.5 27B |
|---|---|---|
| LMArena Math | 1400 | 1429 |
| MathArena Final-Answer Competitions | — | 56.7% |
| OTIS Mock AIME 2024-2025 | 66.4% | — |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |
Knowledge DeepSeek-R1 leads
DeepSeek-R1: 44.5 (#87), Qwen3.5 27B: 38.0 (#150)
| Benchmark | DeepSeek-R1 | Qwen3.5 27B |
|---|---|---|
| Vectara Hallucination Rate | 11.3% | 12.1% |
| LMArena Expert | 1394 | 1428 |
| GPQA Diamond | 76.3% | — |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| GPQA (HELM) | 66.6% | — |
Multimodal Not comparable
DeepSeek-R1: —, Qwen3.5 27B: 39.4 (#59)
| Benchmark | DeepSeek-R1 | Qwen3.5 27B |
|---|---|---|
| LMArena Vision | — | 1241 |
Multilingual DeepSeek-R1 leads
DeepSeek-R1: 52.4 (#85), Qwen3.5 27B: 50.8 (#115)
| Benchmark | DeepSeek-R1 | Qwen3.5 27B |
|---|---|---|
| LMArena Non-English | 1412 | 1390 |
| LMArena Chinese | 1442 | 1478 |
| LMArena French | 1417 | 1410 |
| LMArena German | 1404 | 1393 |
| LMArena Japanese | 1391 | 1345 |
| LMArena Korean | 1360 | 1358 |
| LMArena Russian | 1423 | 1390 |
| LMArena Spanish | 1411 | 1407 |
Instruction Following Qwen3.5 27B leads
DeepSeek-R1: 72.0 (#143), Qwen3.5 27B: 73.5 (#119)
| Benchmark | DeepSeek-R1 | Qwen3.5 27B |
|---|---|---|
| LMArena Instruction Following | 1382 | 1393 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
Long Context DeepSeek-R1 leads
DeepSeek-R1: 45.4 (#36), Qwen3.5 27B: 43.1 (#106)
| Benchmark | DeepSeek-R1 | Qwen3.5 27B |
|---|---|---|
| LMArena Longer Query | 1391 | 1413 |
| Fiction.LiveBench | 75% | — |
Writing & Preference DeepSeek-R1 leads
DeepSeek-R1: 61.4 (#88), Qwen3.5 27B: 59.3 (#111)
| Benchmark | DeepSeek-R1 | Qwen3.5 27B |
|---|---|---|
| LMArena Text | 1428 | 1409 |
| LMArena Creative Writing | 1405 | 1362 |
| LMArena Multi-Turn | 1405 | 1410 |
| Short-Story Creative Writing | 83% | — |
| EQ-Bench Creative Writing | 1500 | — |
| WildBench | 82.8% | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than Qwen3.5 27B?
DeepSeek-R1 and Qwen3.5 27B score almost the same on the Noometry Index (42.3 vs 41.9), so choose on price, context window or the category you care about most.
Which is cheaper, DeepSeek-R1 or Qwen3.5 27B?
Qwen3.5 27B is cheaper. It lists at $0.30 per million input tokens and $2.40 per million output tokens; DeepSeek-R1 lists at $0.50 and $2.15.
Is DeepSeek-R1 or Qwen3.5 27B better for coding?
DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 38.9 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-R1 and Qwen3.5 27B share?
20 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Qwen3.5 27B has 28.