# gpt-oss-20b vs Qwen3.5-Flash

> Qwen3.5-Flash is the stronger model overall, scoring 42.5 to 32.5 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.

- Canonical page: https://noometry.com/compare/gpt-oss-20b-vs-qwen3-5-flash
- Last updated: 2026-10-12
- Shared benchmarks: 23

## 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 57.9 to 35.5.
- 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.

## Snapshot

| | gpt-oss-20b | Qwen3.5-Flash |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 32.5 | 42.5 |
| Rank | 255 | 112 |
| Context | 131K | 1M |
| Input $/M | $0.018 | $0.10 |
| Output $/M | $0.09 | $0.40 |
| Weights | Open | Proprietary |

## Coding

- gpt-oss-20b: 37.6 (#192)
- Qwen3.5-Flash: 34.2 (#242)

| Benchmark | gpt-oss-20b | Qwen3.5-Flash |
|---|---|---|
| LMArena Coding | 1306 | 1412 |
| ALE-Bench | 566.05 | 221.8 |
| LMArena WebDev | — | 1244 |
| SciCode | 34.4% | — |
| WeirdML | 40.9% | — |

## Agentic & Tool Use

- gpt-oss-20b: 9.3 (#154)
- Qwen3.5-Flash: —

| Benchmark | gpt-oss-20b | Qwen3.5-Flash |
|---|---|---|
| Terminal-Bench | 3.4% | — |
| Vending-Bench 2 | — | 462.69 |

## Reasoning

- gpt-oss-20b: 19.3 (#261)
- Qwen3.5-Flash: 33.7 (#72)

| Benchmark | gpt-oss-20b | Qwen3.5-Flash |
|---|---|---|
| Chess Puzzles | 4% | 21% |
| LMArena Hard Prompts | 1274 | 1403 |
| DTBench | 68% | 82.9% |
| LMCA | 14.5% | 29.1% |
| Epoch Capabilities Index | 137.82 | 143.98 |
| Kagi LLM Benchmark | 53.2% | — |
| CritPt | 1.4% | — |
| Mystery Game Puzzles | — | 20% |

## Math

- gpt-oss-20b: 39.4 (#103)
- Qwen3.5-Flash: 37.4 (#158)

| 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

- gpt-oss-20b: 34.6 (#195)
- Qwen3.5-Flash: 43.2 (#93)

| Benchmark | gpt-oss-20b | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 60.8% | 82.3% |
| LMArena Expert | 1258 | 1407 |
| SimpleQA Verified | — | 20.3% |
| MMLU-Pro | 74% | — |
| Vectara Hallucination Rate | — | 10.5% |
| GPQA (HELM) | 59.4% | — |

## Multilingual

- gpt-oss-20b: 42.2 (#197)
- Qwen3.5-Flash: 50.5 (#121)

| Benchmark | gpt-oss-20b | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1268 | 1385 |
| LMArena Chinese | 1314 | 1446 |
| LMArena German | 1255 | 1390 |
| LMArena Japanese | 1244 | 1368 |
| LMArena Korean | 1236 | 1344 |
| LMArena Russian | 1278 | 1379 |
| LMArena Spanish | 1267 | 1400 |
| LMArena French | — | 1412 |

## Instruction Following

- gpt-oss-20b: 61.8 (#240)
- Qwen3.5-Flash: 72.6 (#139)

| Benchmark | gpt-oss-20b | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1236 | 1374 |
| IFEval | 73.2% | — |

## Long Context

- gpt-oss-20b: 37.9 (#209)
- Qwen3.5-Flash: 42.4 (#124)

| Benchmark | gpt-oss-20b | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1250 | 1392 |

## Writing & Preference

- gpt-oss-20b: 35.5 (#265)
- Qwen3.5-Flash: 57.9 (#122)

| Benchmark | gpt-oss-20b | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1287 | 1397 |
| LMArena Creative Writing | 1201 | 1343 |
| LMArena Multi-Turn | 1268 | 1393 |
| EQ-Bench Creative Writing | 666 | — |
| WildBench | 73.7% | — |

## FAQ

### Is gpt-oss-20b better than Qwen3.5-Flash?

Qwen3.5-Flash is the stronger model overall, scoring 42.5 to 32.5 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: 37.6 versus 34.2 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.