Model comparison
DeepSeek-R1-Distill-Qwen-1.5B vs Qwen3.5-Flash
Qwen3.5-Flash is the stronger model overall, scoring 42.5 to 26.1 on the Noometry Index.
Last verified . 3 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 3 benchmarks with published results for both. DeepSeek-R1-Distill-Qwen-1.5B scores higher in 0 categories and Qwen3.5-Flash in 4 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3.5-Flash leads 43.2 to 16.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 21.4% for DeepSeek-R1-Distill-Qwen-1.5B and 84.4% for Qwen3.5-Flash.
- DeepSeek-R1-Distill-Qwen-1.5B has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-R1-Distill-Qwen-1.5B | Qwen3.5-Flash | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 26.1 | 42.5 |
| Released | 2025-01-20 | 2026-02-23 |
| Weights | Open | Proprietary |
| Context window | — | 1M |
| Max output | — | 66K |
| Input $ / M tokens | — | $0.10 |
| Output $ / M tokens | — | $0.40 |
| Results tracked | 5 | 32 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Qwen3.5-Flash leads
DeepSeek-R1-Distill-Qwen-1.5B: 21.8 (#336), Qwen3.5-Flash: 34.2 (#242)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Qwen3.5-Flash |
|---|---|---|
| LMArena WebDev | — | 1244 |
| BigCodeBench Instruct | 7% | — |
| LMArena Coding | — | 1412 |
| BigCodeBench Complete | 7.9% | — |
| ALE-Bench | — | 221.8 |
Agentic & Tool Use Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, Qwen3.5-Flash: —
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Qwen3.5-Flash |
|---|---|---|
| Vending-Bench 2 | — | 462.69 |
Reasoning Qwen3.5-Flash leads
DeepSeek-R1-Distill-Qwen-1.5B: 19.2 (#262), Qwen3.5-Flash: 33.7 (#72)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Qwen3.5-Flash |
|---|---|---|
| Chess Puzzles | 0% | 21% |
| LMArena Hard Prompts | — | 1403 |
| Mystery Game Puzzles | — | 20% |
| DTBench | — | 82.9% |
| LMCA | — | 29.1% |
| Epoch Capabilities Index | — | 143.98 |
Math Qwen3.5-Flash leads
DeepSeek-R1-Distill-Qwen-1.5B: 23.0 (#274), Qwen3.5-Flash: 37.4 (#158)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Qwen3.5-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 21.4% | 84.4% |
| FrontierMath (Tiers 1-3) | — | 18.2% |
| LMArena Math | — | 1407 |
| FrontierMath (Feb 2025 set) | — | 6.2% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Qwen3.5-Flash leads
DeepSeek-R1-Distill-Qwen-1.5B: 16.0 (#290), Qwen3.5-Flash: 43.2 (#93)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 33.6% | 82.3% |
| SimpleQA Verified | — | 20.3% |
| Vectara Hallucination Rate | — | 10.5% |
| LMArena Expert | — | 1407 |
Multilingual Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, Qwen3.5-Flash: 50.5 (#121)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | — | 1385 |
| LMArena Chinese | — | 1446 |
| LMArena French | — | 1412 |
| LMArena German | — | 1390 |
| LMArena Japanese | — | 1368 |
| LMArena Korean | — | 1344 |
| LMArena Russian | — | 1379 |
| LMArena Spanish | — | 1400 |
Instruction Following Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, Qwen3.5-Flash: 72.6 (#139)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | — | 1374 |
Long Context Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, Qwen3.5-Flash: 42.4 (#124)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | — | 1392 |
Writing & Preference Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, Qwen3.5-Flash: 57.9 (#122)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | — | 1397 |
| LMArena Creative Writing | — | 1343 |
| LMArena Multi-Turn | — | 1393 |
Frequently asked questions
Is DeepSeek-R1-Distill-Qwen-1.5B better than Qwen3.5-Flash?
Qwen3.5-Flash is the stronger model overall, scoring 42.5 to 26.1 on the Noometry Index.
Is DeepSeek-R1-Distill-Qwen-1.5B or Qwen3.5-Flash better for coding?
Qwen3.5-Flash scores higher on coding benchmarks: 34.2 versus 21.8 in the Noometry coding category.
How many benchmarks do DeepSeek-R1-Distill-Qwen-1.5B and Qwen3.5-Flash share?
3 benchmarks have published results for both models. DeepSeek-R1-Distill-Qwen-1.5B has 5 scored results on Noometry and Qwen3.5-Flash has 32.