Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5V-Turbo
~58
0/8 categorieso3-mini
65
Winner · 1/8 categoriesGLM-5V-Turbo· o3-mini
Pick o3-mini if you want the stronger benchmark profile. GLM-5V-Turbo only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
o3-mini is clearly ahead on the aggregate, 65 to 58. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o3-mini's sharpest advantage is in agentic, where it averages 66.6 against 58. The single biggest benchmark swing on the page is BrowseComp, 51.9% to 74%.
o3-mini is also the more expensive model on tokens at $1.10 input / $4.40 output per 1M tokens, versus $1.20 input / $4.00 output per 1M tokens for GLM-5V-Turbo. o3-mini 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.
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 | o3-mini |
|---|---|---|
| Agentico3-mini wins | ||
| BrowseComp | 51.9% | 74% |
| OSWorld-Verified | 62.3% | 61% |
| BrowseComp-VL | 51.9% | — |
| OSWorld | 62.3% | — |
| AndroidWorld | 75.7% | — |
| WebVoyager | 88.5% | — |
| Terminal-Bench 2.0 | — | 67% |
| Coding | ||
| SWE-bench Verified | — | 49.3% |
| SWE-bench Pro | — | 57% |
| 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 | — | 73% |
| OfficeQA Pro | — | 76% |
| Reasoning | ||
| LongBench v2 | — | 82% |
| MRCRv2 | — | 80% |
| Knowledge | ||
| MMLU | — | 86.9% |
| GPQA | — | 77.2% |
| FrontierScience | — | 66% |
| Instruction Following | ||
| IFEval | — | 93.9% |
| Multilingual | ||
| MMLU-ProX | — | 73% |
| Mathematics | ||
| AIME 2024 | — | 87.3% |
o3-mini is ahead overall, 65 to 58. The biggest single separator in this matchup is BrowseComp, where the scores are 51.9% and 74%.
o3-mini has the edge for agentic tasks in this comparison, averaging 66.6 versus 58. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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