Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5 (high)
82
Winner · 4/8 categoriesQwen3.5-122B-A10B
71
3/8 categoriesGPT-5 (high)· Qwen3.5-122B-A10B
Pick GPT-5 (high) if you want the stronger benchmark profile. Qwen3.5-122B-A10B only becomes the better choice if coding is the priority or you need the larger 262K context window.
GPT-5 (high) is clearly ahead on the aggregate, 82 to 71. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5 (high)'s sharpest advantage is in reasoning, where it averages 83.1 against 60.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 78% to 49.4%. Qwen3.5-122B-A10B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Qwen3.5-122B-A10B gives you the larger context window at 262K, compared with 128K for GPT-5 (high).
BenchLM keeps the benchmark table and the operator tradeoffs on the same page so a better score does not hide a materially slower, pricier, or smaller-context model.
Runtime metrics show N/A when BenchLM does not have a sourced snapshot for that exact model. The scoring rules and freshness policy are documented on the methodology page.
| Benchmark | GPT-5 (high) | Qwen3.5-122B-A10B |
|---|---|---|
| AgenticGPT-5 (high) wins | ||
| Terminal-Bench 2.0 | 78% | 49.4% |
| BrowseComp | 75% | 63.8% |
| OSWorld-Verified | 72% | 58% |
| tau2-bench | — | 79.5% |
| CodingQwen3.5-122B-A10B wins | ||
| HumanEval | 85% | — |
| SWE-bench Verified | 67% | 72% |
| LiveCodeBench | 62% | 78.9% |
| SWE-bench Pro | 70% | — |
| Multimodal & GroundedGPT-5 (high) wins | ||
| MMMU-Pro | 93% | 76.9% |
| OfficeQA Pro | 85% | — |
| ReasoningGPT-5 (high) wins | ||
| MuSR | 87% | — |
| BBH | 94% | — |
| LongBench v2 | 83% | 60.2% |
| MRCRv2 | 80% | — |
| KnowledgeQwen3.5-122B-A10B wins | ||
| MMLU | 93% | — |
| GPQA | 91% | 86.6% |
| SuperGPQA | 89% | 67.1% |
| MMLU-Pro | 83% | 86.7% |
| HLE | 27% | — |
| FrontierScience | 83% | — |
| SimpleQA | 89% | — |
| Instruction FollowingQwen3.5-122B-A10B wins | ||
| IFEval | 91% | 93.4% |
| MultilingualGPT-5 (high) wins | ||
| MGSM | 89% | — |
| MMLU-ProX | 85% | 82.2% |
| Mathematics | ||
| AIME 2023 | 95% | — |
| AIME 2024 | 97% | — |
| AIME 2025 | 96% | — |
| HMMT Feb 2023 | 91% | — |
| HMMT Feb 2024 | 93% | — |
| HMMT Feb 2025 | 92% | — |
| BRUMO 2025 | 94% | — |
| MATH-500 | 94% | — |
GPT-5 (high) is ahead overall, 82 to 71. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 78% and 49.4%.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 72.6. Inside this category, SuperGPQA is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for coding in this comparison, averaging 76.3 versus 66.2. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
GPT-5 (high) has the edge for reasoning in this comparison, averaging 83.1 versus 60.2. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
GPT-5 (high) has the edge for agentic tasks in this comparison, averaging 75.2 versus 56. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5 (high) has the edge for multimodal and grounded tasks in this comparison, averaging 89.4 versus 76.9. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for instruction following in this comparison, averaging 93.4 versus 91. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5 (high) has the edge for multilingual tasks in this comparison, averaging 86.4 versus 82.2. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.