Model comparison
DeepSeek-R1 vs Qwen3.5 122B-A10B
DeepSeek-R1 and Qwen3.5 122B-A10B score almost the same on the Noometry Index (42.3 vs 42.1), 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 122B-A10B in 2 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.5 122B-A10B leads 27.2 to 18.6.
- DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $0.40 / $3.20 for Qwen3.5 122B-A10B.
- Qwen3.5 122B-A10B accepts more context: 262K tokens versus 164K.
- Qwen3.5 122B-A10B has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-R1 | Qwen3.5 122B-A10B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 42.3 | 42.1 |
| Released | 2025-01-20 | 2026-02-23 |
| Weights | Proprietary | Open |
| Context window | 164K | 262K |
| Max output | 64K | 66K |
| Input $ / M tokens | $0.50 | $0.40 |
| Output $ / M tokens | $2.15 | $3.20 |
| Results tracked | 52 | 27 |
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Category by category
Coding DeepSeek-R1 leads
DeepSeek-R1: 46.3 (#68), Qwen3.5 122B-A10B: 39.1 (#162)
| Benchmark | DeepSeek-R1 | Qwen3.5 122B-A10B |
|---|---|---|
| SciCode | 35.7% | 35.6% |
| LMArena Coding | 1427 | 1436 |
| Aider Polyglot | 71.4% | — |
| LMArena WebDev | — | 1360 |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use Not comparable
DeepSeek-R1: 30.7 (#75), Qwen3.5 122B-A10B: —
| Benchmark | DeepSeek-R1 | Qwen3.5 122B-A10B |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
Reasoning Qwen3.5 122B-A10B leads
DeepSeek-R1: 18.6 (#278), Qwen3.5 122B-A10B: 27.2 (#123)
| Benchmark | DeepSeek-R1 | Qwen3.5 122B-A10B |
|---|---|---|
| CritPt | 1.1% | 0.9% |
| LMArena Hard Prompts | 1416 | 1421 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| Kagi LLM Benchmark | 69.4% | — |
| NYT Connections (extended) | — | 51.7% |
| ARC-AGI-1 | 21.2% | — |
| Thematic Generalization | — | 51.2% |
| LiveBench Reasoning | 83.2% | — |
| Mystery Game Puzzles | — | 17% |
| DTBench | — | 84.3% |
| LiveBench Data Analysis | 69.8% | — |
| LMCA | — | 32.2% |
| Epoch Capabilities Index | 141.29 | — |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math DeepSeek-R1 leads
DeepSeek-R1: 43.8 (#79), Qwen3.5 122B-A10B: 39.1 (#112)
| Benchmark | DeepSeek-R1 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Math | 1400 | 1432 |
| 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 122B-A10B: 38.8 (#142)
| Benchmark | DeepSeek-R1 | Qwen3.5 122B-A10B |
|---|---|---|
| Vectara Hallucination Rate | 11.3% | 11.2% |
| LMArena Expert | 1394 | 1432 |
| GPQA Diamond | 76.3% | — |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| GPQA (HELM) | 66.6% | — |
Multimodal Not comparable
DeepSeek-R1: —, Qwen3.5 122B-A10B: 39.6 (#57)
| Benchmark | DeepSeek-R1 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Vision | — | 1245 |
Multilingual Too close to call
DeepSeek-R1: 52.4 (#85), Qwen3.5 122B-A10B: 51.6 (#107)
| Benchmark | DeepSeek-R1 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Non-English | 1412 | 1400 |
| LMArena Chinese | 1442 | 1462 |
| LMArena French | 1417 | 1442 |
| LMArena German | 1404 | 1426 |
| LMArena Japanese | 1391 | 1367 |
| LMArena Korean | 1360 | 1352 |
| LMArena Russian | 1423 | 1400 |
| LMArena Spanish | 1411 | 1424 |
Instruction Following Qwen3.5 122B-A10B leads
DeepSeek-R1: 72.0 (#143), Qwen3.5 122B-A10B: 73.8 (#115)
| Benchmark | DeepSeek-R1 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Instruction Following | 1382 | 1399 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
Long Context DeepSeek-R1 leads
DeepSeek-R1: 45.4 (#36), Qwen3.5 122B-A10B: 43.0 (#109)
| Benchmark | DeepSeek-R1 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Longer Query | 1391 | 1410 |
| Fiction.LiveBench | 75% | — |
Writing & Preference DeepSeek-R1 leads
DeepSeek-R1: 61.4 (#88), Qwen3.5 122B-A10B: 60.0 (#105)
| Benchmark | DeepSeek-R1 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Text | 1428 | 1417 |
| LMArena Creative Writing | 1405 | 1368 |
| LMArena Multi-Turn | 1405 | 1416 |
| 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 122B-A10B?
DeepSeek-R1 and Qwen3.5 122B-A10B score almost the same on the Noometry Index (42.3 vs 42.1), so choose on price, context window or the category you care about most.
Which is cheaper, DeepSeek-R1 or Qwen3.5 122B-A10B?
DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; Qwen3.5 122B-A10B lists at $0.40 and $3.20.
Is DeepSeek-R1 or Qwen3.5 122B-A10B 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?
Qwen3.5 122B-A10B does, with 262K tokens against 164K.
How many benchmarks do DeepSeek-R1 and Qwen3.5 122B-A10B share?
20 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Qwen3.5 122B-A10B has 27.