Model comparison
DeepSeek-R1 vs Qwen3.6 27B
DeepSeek-R1 and Qwen3.6 27B score almost the same on the Noometry Index (42.3 vs 42.2), so choose on price, context window or the category you care about most.
Last verified . 5 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 5 benchmarks with published results for both. DeepSeek-R1 scores higher in 2 categories and Qwen3.6 27B in 3 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-R1 leads 61.4 to 50.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 66.4% for DeepSeek-R1 and 91.1% for Qwen3.6 27B.
- DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $0.60 / $3.60 for Qwen3.6 27B.
- Qwen3.6 27B accepts more context: 262K tokens versus 164K.
- Qwen3.6 27B has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-R1 | Qwen3.6 27B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 42.3 | 42.2 |
| Released | 2025-01-20 | 2026-04-22 |
| Weights | Proprietary | Open |
| Context window | 164K | 262K |
| Max output | 64K | 66K |
| Input $ / M tokens | $0.50 | $0.60 |
| Output $ / M tokens | $2.15 | $3.60 |
| Results tracked | 52 | 11 |
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Category by category
Coding DeepSeek-R1 leads
DeepSeek-R1: 46.3 (#68), Qwen3.6 27B: 39.1 (#163)
| Benchmark | DeepSeek-R1 | Qwen3.6 27B |
|---|---|---|
| SciCode | 35.7% | 37.3% |
| Aider Polyglot | 71.4% | — |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| LMArena Coding | 1427 | — |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use Not comparable
DeepSeek-R1: 30.7 (#75), Qwen3.6 27B: —
| Benchmark | DeepSeek-R1 | Qwen3.6 27B |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
Reasoning Qwen3.6 27B leads
DeepSeek-R1: 18.6 (#278), Qwen3.6 27B: 25.0 (#153)
| Benchmark | DeepSeek-R1 | Qwen3.6 27B |
|---|---|---|
| CritPt | 1.1% | 0.9% |
| Epoch Capabilities Index | 141.29 | 146.5 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| Kagi LLM Benchmark | 69.4% | — |
| ARC-AGI-1 | 21.2% | — |
| Chess Puzzles | — | 22% |
| LiveBench Reasoning | 83.2% | — |
| LMArena Hard Prompts | 1416 | — |
| Mystery Game Puzzles | — | 7% |
| DTBench | — | 78.1% |
| LiveBench Data Analysis | 69.8% | — |
| LMCA | — | 34.5% |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math Qwen3.6 27B leads
DeepSeek-R1: 43.8 (#79), Qwen3.6 27B: 48.5 (#62)
| Benchmark | DeepSeek-R1 | Qwen3.6 27B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 91.1% |
| FrontierMath (Tiers 1-3) | — | 35.1% |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| LMArena Math | 1400 | — |
| MATH Level 5 | 96.6% | — |
Knowledge Qwen3.6 27B leads
DeepSeek-R1: 44.5 (#87), Qwen3.6 27B: 52.4 (#63)
| Benchmark | DeepSeek-R1 | Qwen3.6 27B |
|---|---|---|
| GPQA Diamond | 76.3% | 85.9% |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |
| LMArena Expert | 1394 | — |
Multilingual Not comparable
DeepSeek-R1: 52.4 (#85), Qwen3.6 27B: —
| Benchmark | DeepSeek-R1 | Qwen3.6 27B |
|---|---|---|
| LMArena Non-English | 1412 | — |
| LMArena Chinese | 1442 | — |
| LMArena French | 1417 | — |
| LMArena German | 1404 | — |
| LMArena Japanese | 1391 | — |
| LMArena Korean | 1360 | — |
| LMArena Russian | 1423 | — |
| LMArena Spanish | 1411 | — |
Instruction Following Not comparable
DeepSeek-R1: 72.0 (#143), Qwen3.6 27B: —
| Benchmark | DeepSeek-R1 | Qwen3.6 27B |
|---|---|---|
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
| LMArena Instruction Following | 1382 | — |
Long Context Not comparable
DeepSeek-R1: 45.4 (#36), Qwen3.6 27B: —
| Benchmark | DeepSeek-R1 | Qwen3.6 27B |
|---|---|---|
| Fiction.LiveBench | 75% | — |
| LMArena Longer Query | 1391 | — |
Writing & Preference DeepSeek-R1 leads
DeepSeek-R1: 61.4 (#88), Qwen3.6 27B: 50.3 (#181)
| Benchmark | DeepSeek-R1 | Qwen3.6 27B |
|---|---|---|
| LMArena Text | 1428 | — |
| LMArena Creative Writing | 1405 | — |
| Short-Story Creative Writing | 83% | — |
| EQ-Bench Creative Writing | 1500 | — |
| WildBench | 82.8% | — |
| EQ-Bench 4 | — | 1026 |
| LMArena Multi-Turn | 1405 | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than Qwen3.6 27B?
DeepSeek-R1 and Qwen3.6 27B score almost the same on the Noometry Index (42.3 vs 42.2), so choose on price, context window or the category you care about most.
Which is cheaper, DeepSeek-R1 or Qwen3.6 27B?
DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; Qwen3.6 27B lists at $0.60 and $3.60.
Is DeepSeek-R1 or Qwen3.6 27B better for coding?
DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 39.1 in the Noometry coding category.
Which has the bigger context window?
Qwen3.6 27B does, with 262K tokens against 164K.
How many benchmarks do DeepSeek-R1 and Qwen3.6 27B share?
5 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Qwen3.6 27B has 11.