Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5V-Turbo
~58
Winner · 1/8 categoriesQwen3 235B 2507 (Reasoning)
55
0/8 categoriesGLM-5V-Turbo· Qwen3 235B 2507 (Reasoning)
Pick GLM-5V-Turbo if you want the stronger benchmark profile. Qwen3 235B 2507 (Reasoning) only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
GLM-5V-Turbo has the cleaner overall profile here, landing at 58 versus 55. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GLM-5V-Turbo's sharpest advantage is in agentic, where it averages 58 against 45.9. The single biggest benchmark swing on the page is OSWorld-Verified, 62.3% to 43%.
GLM-5V-Turbo is also the more expensive model on tokens at $1.20 input / $4.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3 235B 2507 (Reasoning). That is roughly Infinityx on output cost alone. Qwen3 235B 2507 (Reasoning) is the reasoning model in the pair, while GLM-5V-Turbo is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GLM-5V-Turbo gives you the larger context window at 200K, compared with 128K for Qwen3 235B 2507 (Reasoning).
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 | GLM-5V-Turbo | Qwen3 235B 2507 (Reasoning) |
|---|---|---|
| AgenticGLM-5V-Turbo wins | ||
| BrowseComp | 51.9% | 48% |
| OSWorld-Verified | 62.3% | 43% |
| BrowseComp-VL | 51.9% | — |
| OSWorld | 62.3% | — |
| AndroidWorld | 75.7% | — |
| WebVoyager | 88.5% | — |
| Terminal-Bench 2.0 | — | 47% |
| Coding | ||
| HumanEval | — | 32% |
| SWE-bench Verified | — | 16% |
| LiveCodeBench | — | 74.1% |
| SWE-bench Pro | — | 29% |
| Multimodal & Grounded | ||
| Design2Code | 94.8% | — |
| Flame-VLM-Code | 93.8% | — |
| Vision2Web | 31.0% | — |
| ImageMining | 30.7% | — |
| MMSearch | 72.9% | — |
| MMSearch-Plus | 30.0% | — |
| SimpleVQA | 78.2% | — |
| Facts-VLM | 58.6% | — |
| V* | 89.0% | — |
| MMMU-Pro | — | 38% |
| OfficeQA Pro | — | 47% |
| Reasoning | ||
| MuSR | — | 36% |
| BBH | — | 63% |
| LongBench v2 | — | 58% |
| MRCRv2 | — | 58% |
| Knowledge | ||
| MMLU | — | 40% |
| GPQA | — | 81.1% |
| SuperGPQA | — | 64.9% |
| MMLU-Pro | — | 84.4% |
| HLE | — | 6% |
| FrontierScience | — | 42% |
| SimpleQA | — | 38% |
| Instruction Following | ||
| IFEval | — | 87.8% |
| Multilingual | ||
| MGSM | — | 62% |
| MMLU-ProX | — | 81% |
| Mathematics | ||
| AIME 2023 | — | 40% |
| AIME 2024 | — | 42% |
| AIME 2025 | — | 92.3% |
| HMMT Feb 2023 | — | 36% |
| HMMT Feb 2024 | — | 38% |
| HMMT Feb 2025 | — | 37% |
| BRUMO 2025 | — | 39% |
| MATH-500 | — | 60% |
GLM-5V-Turbo is ahead overall, 58 to 55. The biggest single separator in this matchup is OSWorld-Verified, where the scores are 62.3% and 43%.
GLM-5V-Turbo has the edge for agentic tasks in this comparison, averaging 58 versus 45.9. Inside this category, OSWorld-Verified is the benchmark that creates the most daylight between them.
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