Model comparison
gpt-oss-120b vs Qwen3.6 Max Preview
Qwen3.6 Max Preview is the stronger model overall, scoring 51.1 to 36.0 on the Noometry Index. gpt-oss-120b costs 42× less per token, which makes it the better buy when Qwen3.6 Max Preview'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-120b scores higher in 0 categories and Qwen3.6 Max Preview in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.6 Max Preview leads 40.8 to 19.5.
- The biggest single-benchmark swing is SimpleBench: 22.1% for gpt-oss-120b and 63% for Qwen3.6 Max Preview.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $1.30 / $7.80 for Qwen3.6 Max Preview.
- Qwen3.6 Max Preview accepts more context: 262K tokens versus 131K.
- gpt-oss-120b has downloadable open weights; the other is API-only.
Side by side
| gpt-oss-120b | Qwen3.6 Max Preview | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 36.0 | 51.1 |
| Released | 2025-08-05 | 2026-04-20 |
| Weights | Open | Proprietary |
| Context window | 131K | 262K |
| Max output | 41K | 66K |
| Input $ / M tokens | $0.037 | $1.30 |
| Output $ / M tokens | $0.17 | $7.80 |
| Results tracked | 48 | 29 |
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Category by category
Coding Qwen3.6 Max Preview leads
gpt-oss-120b: 33.2 (#251), Qwen3.6 Max Preview: 48.6 (#50)
| Benchmark | gpt-oss-120b | Qwen3.6 Max Preview |
|---|---|---|
| LMArena Coding | 1380 | 1472 |
| SWE-bench Verified | — | 76.7% |
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| LMArena WebDev | — | 1482 |
| SciCode | 36% | — |
| WeirdML | 48.2% | — |
| ALE-Bench | 575.62 | — |
| AlgoTune | 1.41 | — |
Agentic & Tool Use Not comparable
gpt-oss-120b: 12.1 (#145), Qwen3.6 Max Preview: —
| Benchmark | gpt-oss-120b | Qwen3.6 Max Preview |
|---|---|---|
| Vending-Bench 2 | -21.53 | 4,254 |
| Terminal-Bench | 18.7% | — |
| APEX-Agents | 4.4% | — |
| METR Time Horizons | 56.6% | — |
Reasoning Qwen3.6 Max Preview leads
gpt-oss-120b: 19.5 (#236), Qwen3.6 Max Preview: 40.8 (#47)
| Benchmark | gpt-oss-120b | Qwen3.6 Max Preview |
|---|---|---|
| SimpleBench | 22.1% | 63% |
| Chess Puzzles | 20% | 20% |
| LMArena Hard Prompts | 1364 | 1457 |
| Mystery Game Puzzles | 2% | 19% |
| DTBench | 76.3% | 87.2% |
| LMCA | 22.1% | 42.5% |
| Epoch Capabilities Index | 139.94 | 149.27 |
| Kagi LLM Benchmark | 58.6% | — |
| NYT Connections (extended) | — | 74.1% |
| CritPt | 1.1% | — |
| Surface Evolver Bench | 25% | — |
Math Qwen3.6 Max Preview leads
gpt-oss-120b: 50.6 (#43), Qwen3.6 Max Preview: 52.2 (#40)
| Benchmark | gpt-oss-120b | Qwen3.6 Max Preview |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.9% | 91.1% |
| LMArena Math | 1388 | 1465 |
| Omni-MATH | 68.8% | — |
| FrontierMath (Feb 2025 set) | — | 23.1% |
| FrontierMath Tier 4 (v1) | — | 4.2% |
Knowledge Qwen3.6 Max Preview leads
gpt-oss-120b: 42.3 (#89), Qwen3.6 Max Preview: 57.6 (#35)
| Benchmark | gpt-oss-120b | Qwen3.6 Max Preview |
|---|---|---|
| GPQA Diamond | 75.8% | 87.4% |
| LMArena Expert | 1356 | 1476 |
| SimpleQA Verified | — | 52% |
| MMLU-Pro | 79.5% | — |
| Confabulations | 15.7% | — |
| Vectara Hallucination Rate | 14.2% | — |
| GPQA (HELM) | 68.4% | — |
Multilingual Qwen3.6 Max Preview leads
gpt-oss-120b: 48.2 (#142), Qwen3.6 Max Preview: 54.3 (#46)
| Benchmark | gpt-oss-120b | Qwen3.6 Max Preview |
|---|---|---|
| LMArena Non-English | 1350 | 1436 |
| LMArena Chinese | 1384 | 1487 |
| LMArena French | 1370 | 1449 |
| LMArena Russian | 1343 | 1444 |
| LMArena Spanish | 1388 | 1452 |
| LMArena German | 1355 | — |
| LMArena Japanese | 1332 | — |
| LMArena Korean | 1283 | — |
Instruction Following Qwen3.6 Max Preview leads
gpt-oss-120b: 69.5 (#168), Qwen3.6 Max Preview: 75.8 (#51)
| Benchmark | gpt-oss-120b | Qwen3.6 Max Preview |
|---|---|---|
| LMArena Instruction Following | 1318 | 1437 |
| IFEval | 83.6% | — |
Long Context Qwen3.6 Max Preview leads
gpt-oss-120b: 31.4 (#273), Qwen3.6 Max Preview: 44.6 (#59)
| Benchmark | gpt-oss-120b | Qwen3.6 Max Preview |
|---|---|---|
| LMArena Longer Query | 1319 | 1457 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference Qwen3.6 Max Preview leads
gpt-oss-120b: 46.7 (#213), Qwen3.6 Max Preview: 64.0 (#58)
| Benchmark | gpt-oss-120b | Qwen3.6 Max Preview |
|---|---|---|
| LMArena Text | 1366 | 1447 |
| LMArena Creative Writing | 1274 | 1435 |
| LMArena Multi-Turn | 1340 | 1456 |
| Short-Story Creative Writing | 77.1% | — |
| EQ-Bench Creative Writing | 961 | — |
| WildBench | 84.5% | — |
Frequently asked questions
Is gpt-oss-120b better than Qwen3.6 Max Preview?
Qwen3.6 Max Preview is the stronger model overall, scoring 51.1 to 36.0 on the Noometry Index. gpt-oss-120b costs 42× less per token, which makes it the better buy when Qwen3.6 Max Preview's lead doesn't matter for your workload.
Which is cheaper, gpt-oss-120b or Qwen3.6 Max Preview?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; Qwen3.6 Max Preview lists at $1.30 and $7.80.
Is gpt-oss-120b or Qwen3.6 Max Preview better for coding?
Qwen3.6 Max Preview scores higher on coding benchmarks: 48.6 versus 33.2 in the Noometry coding category.
Which has the bigger context window?
Qwen3.6 Max Preview does, with 262K tokens against 131K.
How many benchmarks do gpt-oss-120b and Qwen3.6 Max Preview share?
23 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and Qwen3.6 Max Preview has 29.