Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5V-Turbo
~58
1/8 categoriesQwen3.5-27B
70
Winner · 0/8 categoriesGLM-5V-Turbo· Qwen3.5-27B
Pick Qwen3.5-27B if you want the stronger benchmark profile. GLM-5V-Turbo only becomes the better choice if agentic is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.5-27B is clearly ahead on the aggregate, 70 to 58. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
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 Qwen3.5-27B. That is roughly Infinityx on output cost alone. Qwen3.5-27B 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. Qwen3.5-27B gives you the larger context window at 262K, compared with 200K for GLM-5V-Turbo.
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 | Qwen3.5-27B |
|---|---|---|
| AgenticGLM-5V-Turbo wins | ||
| BrowseComp | 51.9% | 61% |
| OSWorld-Verified | 62.3% | 56.2% |
| BrowseComp-VL | 51.9% | — |
| OSWorld | 62.3% | — |
| AndroidWorld | 75.7% | — |
| WebVoyager | 88.5% | — |
| Terminal-Bench 2.0 | — | 41.6% |
| Tau2-Telecom | — | 79% |
| Coding | ||
| SWE-bench Verified | — | 72.4% |
| LiveCodeBench | — | 80.7% |
| 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 | — | 75% |
| Reasoning | ||
| LongBench v2 | — | 60.6% |
| Knowledge | ||
| MMLU-Pro | — | 86.1% |
| SuperGPQA | — | 65.6% |
| GPQA | — | 85.5% |
| Instruction Following | ||
| IFEval | — | 95% |
| Multilingual | ||
| MMLU-ProX | — | 82.2% |
| Mathematics | ||
| Coming soon | ||
Qwen3.5-27B is ahead overall, 70 to 58. The biggest single separator in this matchup is BrowseComp, where the scores are 51.9% and 61%.
GLM-5V-Turbo has the edge for agentic tasks in this comparison, averaging 58 versus 51.6. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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