Model comparison
DeepSeek-R1 vs Qwen3.5-Flash
DeepSeek-R1 and Qwen3.5-Flash score almost the same on the Noometry Index (42.0 vs 42.3), so choose on price, context window or the category you care about most.
Last verified . 22 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 22 benchmarks with published results for both. DeepSeek-R1 scores higher in 6 categories and Qwen3.5-Flash in 2 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.5-Flash leads 32.9 to 18.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 66.4% for DeepSeek-R1 and 84.4% for Qwen3.5-Flash.
- Qwen3.5-Flash is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
- Qwen3.5-Flash accepts more context: 1M tokens versus 164K.
Side by side
| DeepSeek-R1 | Qwen3.5-Flash | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 42.0 | 42.3 |
| Released | 2025-01-20 | 2026-02-23 |
| Weights | Proprietary | Proprietary |
| Context window | 164K | 1M |
| Max output | 64K | 66K |
| Input $ / M tokens | $0.50 | $0.10 |
| Output $ / M tokens | $2.15 | $0.40 |
| Results tracked | 52 | 32 |
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Category by category
Coding DeepSeek-R1 leads
DeepSeek-R1: 45.9 (#63), Qwen3.5-Flash: 33.9 (#234)
| Benchmark | DeepSeek-R1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Coding | 1427 | 1412 |
| ALE-Bench | 804.12 | 221.8 |
| Aider Polyglot | 71.4% | — |
| LMArena WebDev | — | 1243 |
| SciCode | 35.7% | — |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use Not comparable
DeepSeek-R1: 30.4 (#68), Qwen3.5-Flash: —
| Benchmark | DeepSeek-R1 | Qwen3.5-Flash |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
| Vending-Bench 2 | — | 462.69 |
Reasoning Qwen3.5-Flash leads
DeepSeek-R1: 18.7 (#253), Qwen3.5-Flash: 32.9 (#63)
| Benchmark | DeepSeek-R1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Hard Prompts | 1415 | 1404 |
| Epoch Capabilities Index | 141.3 | 143.98 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| Kagi LLM Benchmark | 69.4% | — |
| ARC-AGI-1 | 21.2% | — |
| CritPt | 1.1% | — |
| Chess Puzzles | — | 21% |
| LiveBench Reasoning | 83.2% | — |
| Mystery Game Puzzles | — | 20% |
| DTBench | — | 82.9% |
| LiveBench Data Analysis | 69.8% | — |
| LMCA | — | 29.1% |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math DeepSeek-R1 leads
DeepSeek-R1: 42.0 (#70), Qwen3.5-Flash: 36.3 (#117)
| Benchmark | DeepSeek-R1 | Qwen3.5-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 84.4% |
| LMArena Math | 1401 | 1407 |
| FrontierMath (Tiers 1-3) | — | 18.2% |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |
| FrontierMath (Feb 2025 set) | — | 6.2% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge DeepSeek-R1 leads
DeepSeek-R1: 44.3 (#81), Qwen3.5-Flash: 43.2 (#84)
| Benchmark | DeepSeek-R1 | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 76.3% | 82.3% |
| Vectara Hallucination Rate | 11.3% | 10.5% |
| LMArena Expert | 1394 | 1408 |
| SimpleQA Verified | — | 20.3% |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| GPQA (HELM) | 66.6% | — |
Multilingual DeepSeek-R1 leads
DeepSeek-R1: 52.6 (#80), Qwen3.5-Flash: 50.7 (#116)
| Benchmark | DeepSeek-R1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1412 | 1385 |
| LMArena Chinese | 1443 | 1446 |
| LMArena French | 1415 | 1412 |
| LMArena German | 1405 | 1389 |
| LMArena Japanese | 1394 | 1369 |
| LMArena Korean | 1359 | 1344 |
| LMArena Russian | 1423 | 1379 |
| LMArena Spanish | 1410 | 1399 |
Instruction Following Too close to call
DeepSeek-R1: 72.2 (#138), Qwen3.5-Flash: 72.8 (#133)
| Benchmark | DeepSeek-R1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1382 | 1374 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
Long Context DeepSeek-R1 leads
DeepSeek-R1: 45.4 (#36), Qwen3.5-Flash: 42.4 (#119)
| Benchmark | DeepSeek-R1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1391 | 1392 |
| Fiction.LiveBench | 75% | — |
Writing & Preference DeepSeek-R1 leads
DeepSeek-R1: 61.5 (#84), Qwen3.5-Flash: 58.2 (#117)
| Benchmark | DeepSeek-R1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1428 | 1397 |
| LMArena Creative Writing | 1406 | 1343 |
| LMArena Multi-Turn | 1404 | 1393 |
| 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-Flash?
DeepSeek-R1 and Qwen3.5-Flash score almost the same on the Noometry Index (42.0 vs 42.3), so choose on price, context window or the category you care about most.
Which is cheaper, DeepSeek-R1 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-R1 lists at $0.50 and $2.15.
Is DeepSeek-R1 or Qwen3.5-Flash better for coding?
DeepSeek-R1 scores higher on coding benchmarks: 45.9 versus 33.9 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-R1 and Qwen3.5-Flash share?
22 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Qwen3.5-Flash has 32.