Model comparison
DeepSeek-V3.1 vs Qwen3.5-Flash
DeepSeek-V3.1 and Qwen3.5-Flash score almost the same on the Noometry Index (42.2 vs 42.3), so choose on price, context window or the category you care about most.
Last verified . 21 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 21 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 4 categories and Qwen3.5-Flash in 4 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in long context, where Qwen3.5-Flash leads 42.4 to 36.3.
- The biggest single-benchmark swing is Vectara Hallucination Rate: 5.5% for DeepSeek-V3.1 and 10.5% for Qwen3.5-Flash.
- Qwen3.5-Flash is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
- Qwen3.5-Flash accepts more context: 1M tokens versus 164K.
- DeepSeek-V3.1 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.1 | Qwen3.5-Flash | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 42.2 | 42.3 |
| Released | 2025-08-21 | 2026-02-23 |
| Weights | Open | Proprietary |
| Context window | 164K | 1M |
| Max output | 8K | 66K |
| Input $ / M tokens | $0.25 | $0.10 |
| Output $ / M tokens | $0.95 | $0.40 |
| Results tracked | 27 | 32 |
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Category by category
Coding DeepSeek-V3.1 leads
DeepSeek-V3.1: 39.8 (#140), Qwen3.5-Flash: 33.9 (#234)
| Benchmark | DeepSeek-V3.1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Coding | 1417 | 1412 |
| LMArena WebDev | — | 1243 |
| WeirdML | 38.4% | — |
| ALE-Bench | — | 221.8 |
Agentic & Tool Use Not comparable
DeepSeek-V3.1: —, Qwen3.5-Flash: —
| Benchmark | DeepSeek-V3.1 | Qwen3.5-Flash |
|---|---|---|
| Vending-Bench 2 | — | 462.69 |
Reasoning Qwen3.5-Flash leads
DeepSeek-V3.1: 27.3 (#109), Qwen3.5-Flash: 32.9 (#63)
| Benchmark | DeepSeek-V3.1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1404 |
| DTBench | 82.7% | 82.9% |
| LMCA | 24.3% | 29.1% |
| Epoch Capabilities Index | 139.92 | 143.98 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| Chess Puzzles | — | 21% |
| Mystery Game Puzzles | — | 20% |
| ForecastBench | 58 | — |
Math Too close to call
DeepSeek-V3.1: 36.1 (#125), Qwen3.5-Flash: 36.3 (#117)
| Benchmark | DeepSeek-V3.1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Math | 1420 | 1407 |
| FrontierMath (Tiers 1-3) | — | 18.2% |
| OTIS Mock AIME 2024-2025 | — | 84.4% |
| FrontierMath (Feb 2025 set) | — | 6.2% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Too close to call
DeepSeek-V3.1: 43.0 (#86), Qwen3.5-Flash: 43.2 (#84)
| Benchmark | DeepSeek-V3.1 | Qwen3.5-Flash |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | 10.5% |
| LMArena Expert | 1403 | 1408 |
| GPQA Diamond | — | 82.3% |
| SimpleQA Verified | — | 20.3% |
Multilingual DeepSeek-V3.1 leads
DeepSeek-V3.1: 51.8 (#100), Qwen3.5-Flash: 50.7 (#116)
| Benchmark | DeepSeek-V3.1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1400 | 1385 |
| LMArena Chinese | 1470 | 1446 |
| LMArena French | 1447 | 1412 |
| LMArena German | 1412 | 1389 |
| LMArena Japanese | 1374 | 1369 |
| LMArena Korean | 1337 | 1344 |
| LMArena Russian | 1405 | 1379 |
| LMArena Spanish | 1429 | 1399 |
Instruction Following DeepSeek-V3.1 leads
DeepSeek-V3.1: 74.1 (#105), Qwen3.5-Flash: 72.8 (#133)
| Benchmark | DeepSeek-V3.1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1400 | 1374 |
Long Context Qwen3.5-Flash leads
DeepSeek-V3.1: 36.3 (#227), Qwen3.5-Flash: 42.4 (#119)
| Benchmark | DeepSeek-V3.1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1422 | 1392 |
| Fiction.LiveBench | 52.8% | — |
Writing & Preference DeepSeek-V3.1 leads
DeepSeek-V3.1: 60.5 (#93), Qwen3.5-Flash: 58.2 (#117)
| Benchmark | DeepSeek-V3.1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1420 | 1397 |
| LMArena Creative Writing | 1401 | 1343 |
| LMArena Multi-Turn | 1408 | 1393 |
| EQ-Bench Creative Writing | 1436 | — |
Frequently asked questions
Is DeepSeek-V3.1 better than Qwen3.5-Flash?
DeepSeek-V3.1 and Qwen3.5-Flash score almost the same on the Noometry Index (42.2 vs 42.3), so choose on price, context window or the category you care about most.
Which is cheaper, DeepSeek-V3.1 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-V3.1 lists at $0.25 and $0.95.
Is DeepSeek-V3.1 or Qwen3.5-Flash better for coding?
DeepSeek-V3.1 scores higher on coding benchmarks: 39.8 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-V3.1 and Qwen3.5-Flash share?
21 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Qwen3.5-Flash has 32.