Model comparison
gpt-oss-20b vs Qwen3.5-Flash
Qwen3.5-Flash is the stronger model overall, scoring 42.3 to 32.1 on the Noometry Index. gpt-oss-20b costs 4.9× less per token, which makes it the better buy when Qwen3.5-Flash's lead doesn't matter for your workload.
Last verified . 23 shared benchmarks.
Alibaba (Qwen)Summary
- They share 23 benchmarks with published results for both. gpt-oss-20b scores higher in 2 categories and Qwen3.5-Flash in 6 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Qwen3.5-Flash leads 58.2 to 35.8.
- The biggest single-benchmark swing is GPQA Diamond: 60.8% for gpt-oss-20b and 82.3% for Qwen3.5-Flash.
- gpt-oss-20b is cheaper at $0.018 / $0.09 per million input/output tokens, against $0.10 / $0.40 for Qwen3.5-Flash.
- Qwen3.5-Flash accepts more context: 1M tokens versus 131K.
- gpt-oss-20b has downloadable open weights; the other is API-only.
Side by side
| gpt-oss-20b | Qwen3.5-Flash | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 32.1 | 42.3 |
| Released | 2025-08-05 | 2026-02-23 |
| Weights | Open | Proprietary |
| Context window | 131K | 1M |
| Max output | 16K | 66K |
| Input $ / M tokens | $0.018 | $0.10 |
| Output $ / M tokens | $0.09 | $0.40 |
| Results tracked | 34 | 32 |
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Category by category
Coding gpt-oss-20b leads
gpt-oss-20b: 37.1 (#185), Qwen3.5-Flash: 33.9 (#234)
| Benchmark | gpt-oss-20b | Qwen3.5-Flash |
|---|---|---|
| LMArena Coding | 1307 | 1412 |
| ALE-Bench | 566.05 | 221.8 |
| LMArena WebDev | — | 1243 |
| SciCode | 34.4% | — |
| WeirdML | 40.9% | — |
Agentic & Tool Use Not comparable
gpt-oss-20b: 9.2 (#146), Qwen3.5-Flash: —
| Benchmark | gpt-oss-20b | Qwen3.5-Flash |
|---|---|---|
| Terminal-Bench | 3.4% | — |
| Vending-Bench 2 | — | 462.69 |
Reasoning Qwen3.5-Flash leads
gpt-oss-20b: 18.8 (#251), Qwen3.5-Flash: 32.9 (#63)
| Benchmark | gpt-oss-20b | Qwen3.5-Flash |
|---|---|---|
| Chess Puzzles | 4% | 21% |
| LMArena Hard Prompts | 1274 | 1404 |
| DTBench | 68% | 82.9% |
| LMCA | 14.5% | 29.1% |
| Epoch Capabilities Index | 137.81 | 143.98 |
| Kagi LLM Benchmark | 53.2% | — |
| CritPt | 1.4% | — |
| Mystery Game Puzzles | — | 20% |
Math gpt-oss-20b leads
gpt-oss-20b: 37.6 (#93), Qwen3.5-Flash: 36.3 (#117)
| Benchmark | gpt-oss-20b | Qwen3.5-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 65.3% | 84.4% |
| LMArena Math | 1317 | 1407 |
| FrontierMath (Tiers 1-3) | — | 18.2% |
| Omni-MATH | 56.5% | — |
| FrontierMath (Feb 2025 set) | — | 6.2% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Qwen3.5-Flash leads
gpt-oss-20b: 34.6 (#186), Qwen3.5-Flash: 43.2 (#84)
| Benchmark | gpt-oss-20b | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 60.8% | 82.3% |
| LMArena Expert | 1258 | 1408 |
| SimpleQA Verified | — | 20.3% |
| MMLU-Pro | 74% | — |
| Vectara Hallucination Rate | — | 10.5% |
| GPQA (HELM) | 59.4% | — |
Multilingual Qwen3.5-Flash leads
gpt-oss-20b: 42.4 (#192), Qwen3.5-Flash: 50.7 (#116)
| Benchmark | gpt-oss-20b | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1269 | 1385 |
| LMArena Chinese | 1311 | 1446 |
| LMArena German | 1249 | 1389 |
| LMArena Japanese | 1245 | 1369 |
| LMArena Korean | 1239 | 1344 |
| LMArena Russian | 1279 | 1379 |
| LMArena Spanish | 1267 | 1399 |
| LMArena French | — | 1412 |
Instruction Following Qwen3.5-Flash leads
gpt-oss-20b: 62.0 (#235), Qwen3.5-Flash: 72.8 (#133)
| Benchmark | gpt-oss-20b | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1236 | 1374 |
| IFEval | 73.2% | — |
Long Context Qwen3.5-Flash leads
gpt-oss-20b: 37.9 (#203), Qwen3.5-Flash: 42.4 (#119)
| Benchmark | gpt-oss-20b | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1251 | 1392 |
Writing & Preference Qwen3.5-Flash leads
gpt-oss-20b: 35.8 (#260), Qwen3.5-Flash: 58.2 (#117)
| Benchmark | gpt-oss-20b | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1288 | 1397 |
| LMArena Creative Writing | 1201 | 1343 |
| LMArena Multi-Turn | 1268 | 1393 |
| EQ-Bench Creative Writing | 666 | — |
| WildBench | 73.7% | — |
Frequently asked questions
Is gpt-oss-20b better than Qwen3.5-Flash?
Qwen3.5-Flash is the stronger model overall, scoring 42.3 to 32.1 on the Noometry Index. gpt-oss-20b costs 4.9× less per token, which makes it the better buy when Qwen3.5-Flash's lead doesn't matter for your workload.
Which is cheaper, gpt-oss-20b or Qwen3.5-Flash?
gpt-oss-20b is cheaper. It lists at $0.018 per million input tokens and $0.09 per million output tokens; Qwen3.5-Flash lists at $0.10 and $0.40.
Is gpt-oss-20b or Qwen3.5-Flash better for coding?
gpt-oss-20b scores higher on coding benchmarks: 37.1 versus 33.9 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5-Flash does, with 1M tokens against 131K.
How many benchmarks do gpt-oss-20b and Qwen3.5-Flash share?
23 benchmarks have published results for both models. gpt-oss-20b has 34 scored results on Noometry and Qwen3.5-Flash has 32.