Model comparison
gpt-oss-120b vs Qwen3.5-Flash
Qwen3.5-Flash is the stronger model overall, scoring 42.3 to 36.0 on the Noometry Index. gpt-oss-120b costs 2.5× less per token, which makes it the better buy when Qwen3.5-Flash's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
Alibaba (Qwen)Summary
- They share 27 benchmarks with published results for both. gpt-oss-120b scores higher in 1 category and Qwen3.5-Flash in 7 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in math, where gpt-oss-120b leads 50.6 to 36.3.
- The biggest single-benchmark swing is Mystery Game Puzzles: 2% for gpt-oss-120b and 20% for Qwen3.5-Flash.
- gpt-oss-120b is cheaper at $0.037 / $0.17 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-120b has downloadable open weights; the other is API-only.
Side by side
| gpt-oss-120b | Qwen3.5-Flash | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 36.0 | 42.3 |
| Released | 2025-08-05 | 2026-02-23 |
| Weights | Open | Proprietary |
| Context window | 131K | 1M |
| Max output | 41K | 66K |
| Input $ / M tokens | $0.037 | $0.10 |
| Output $ / M tokens | $0.17 | $0.40 |
| Results tracked | 48 | 32 |
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Category by category
Coding Too close to call
gpt-oss-120b: 33.2 (#251), Qwen3.5-Flash: 33.9 (#234)
| Benchmark | gpt-oss-120b | Qwen3.5-Flash |
|---|---|---|
| LMArena Coding | 1380 | 1412 |
| ALE-Bench | 575.62 | 221.8 |
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| LMArena WebDev | — | 1243 |
| SciCode | 36% | — |
| WeirdML | 48.2% | — |
| AlgoTune | 1.41 | — |
Agentic & Tool Use Not comparable
gpt-oss-120b: 12.1 (#145), Qwen3.5-Flash: —
| Benchmark | gpt-oss-120b | Qwen3.5-Flash |
|---|---|---|
| Vending-Bench 2 | -21.53 | 462.69 |
| Terminal-Bench | 18.7% | — |
| APEX-Agents | 4.4% | — |
| METR Time Horizons | 56.6% | — |
Reasoning Qwen3.5-Flash leads
gpt-oss-120b: 19.5 (#236), Qwen3.5-Flash: 32.9 (#63)
| Benchmark | gpt-oss-120b | Qwen3.5-Flash |
|---|---|---|
| Chess Puzzles | 20% | 21% |
| LMArena Hard Prompts | 1364 | 1404 |
| Mystery Game Puzzles | 2% | 20% |
| DTBench | 76.3% | 82.9% |
| LMCA | 22.1% | 29.1% |
| Epoch Capabilities Index | 139.94 | 143.98 |
| SimpleBench | 22.1% | — |
| Kagi LLM Benchmark | 58.6% | — |
| CritPt | 1.1% | — |
| Surface Evolver Bench | 25% | — |
Math gpt-oss-120b leads
gpt-oss-120b: 50.6 (#43), Qwen3.5-Flash: 36.3 (#117)
| Benchmark | gpt-oss-120b | Qwen3.5-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.9% | 84.4% |
| LMArena Math | 1388 | 1407 |
| FrontierMath (Tiers 1-3) | — | 18.2% |
| Omni-MATH | 68.8% | — |
| FrontierMath (Feb 2025 set) | — | 6.2% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Too close to call
gpt-oss-120b: 42.3 (#89), Qwen3.5-Flash: 43.2 (#84)
| Benchmark | gpt-oss-120b | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 75.8% | 82.3% |
| Vectara Hallucination Rate | 14.2% | 10.5% |
| LMArena Expert | 1356 | 1408 |
| SimpleQA Verified | — | 20.3% |
| MMLU-Pro | 79.5% | — |
| Confabulations | 15.7% | — |
| GPQA (HELM) | 68.4% | — |
Multilingual Qwen3.5-Flash leads
gpt-oss-120b: 48.2 (#142), Qwen3.5-Flash: 50.7 (#116)
| Benchmark | gpt-oss-120b | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1350 | 1385 |
| LMArena Chinese | 1384 | 1446 |
| LMArena French | 1370 | 1412 |
| LMArena German | 1355 | 1389 |
| LMArena Japanese | 1332 | 1369 |
| LMArena Korean | 1283 | 1344 |
| LMArena Russian | 1343 | 1379 |
| LMArena Spanish | 1388 | 1399 |
Instruction Following Qwen3.5-Flash leads
gpt-oss-120b: 69.5 (#168), Qwen3.5-Flash: 72.8 (#133)
| Benchmark | gpt-oss-120b | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1318 | 1374 |
| IFEval | 83.6% | — |
Long Context Qwen3.5-Flash leads
gpt-oss-120b: 31.4 (#273), Qwen3.5-Flash: 42.4 (#119)
| Benchmark | gpt-oss-120b | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1319 | 1392 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference Qwen3.5-Flash leads
gpt-oss-120b: 46.7 (#213), Qwen3.5-Flash: 58.2 (#117)
| Benchmark | gpt-oss-120b | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1366 | 1397 |
| LMArena Creative Writing | 1274 | 1343 |
| LMArena Multi-Turn | 1340 | 1393 |
| Short-Story Creative Writing | 77.1% | — |
| EQ-Bench Creative Writing | 961 | — |
| WildBench | 84.5% | — |
Frequently asked questions
Is gpt-oss-120b better than Qwen3.5-Flash?
Qwen3.5-Flash is the stronger model overall, scoring 42.3 to 36.0 on the Noometry Index. gpt-oss-120b costs 2.5× 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-120b or Qwen3.5-Flash?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; Qwen3.5-Flash lists at $0.10 and $0.40.
Is gpt-oss-120b or Qwen3.5-Flash better for coding?
They score almost the same on coding (33.2 vs 33.9); test both on your own repository before choosing.
Which has the bigger context window?
Qwen3.5-Flash does, with 1M tokens against 131K.
How many benchmarks do gpt-oss-120b and Qwen3.5-Flash share?
27 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and Qwen3.5-Flash has 32.