Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5V-Turbo
~58
0/8 categorieso3
65
Winner · 1/8 categoriesGLM-5V-Turbo· o3
Pick o3 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 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's sharpest advantage is in agentic, where it averages 69.9 against 58. The single biggest benchmark swing on the page is BrowseComp, 51.9% to 75%.
o3 is also the more expensive model on tokens at $10.00 input / $40.00 output per 1M tokens, versus $1.20 input / $4.00 output per 1M tokens for GLM-5V-Turbo. That is roughly 10.0x on output cost alone. o3 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 |
|---|---|---|
| Agentico3 wins | ||
| BrowseComp | 51.9% | 75% |
| OSWorld-Verified | 62.3% | 65% |
| BrowseComp-VL | 51.9% | — |
| OSWorld | 62.3% | — |
| AndroidWorld | 75.7% | — |
| WebVoyager | 88.5% | — |
| Terminal-Bench 2.0 | — | 71% |
| Coding | ||
| HumanEval | — | 78% |
| SWE-bench Verified | — | 71.7% |
| LiveCodeBench | — | 40% |
| SWE-bench Pro | — | 58% |
| 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 | — | 70% |
| OfficeQA Pro | — | 75% |
| Reasoning | ||
| MuSR | — | 82% |
| BBH | — | 86% |
| LongBench v2 | — | 82% |
| MRCRv2 | — | 81% |
| ARC-AGI-2 | — | 3% |
| Knowledge | ||
| MMLU | — | 86% |
| GPQA | — | 87% |
| SuperGPQA | — | 85% |
| MMLU-Pro | — | 75% |
| HLE | — | 24% |
| FrontierScience | — | 77% |
| SimpleQA | — | 84% |
| Instruction Following | ||
| IFEval | — | 85% |
| Multilingual | ||
| MGSM | — | 83% |
| MMLU-ProX | — | 80% |
| Mathematics | ||
| AIME 2023 | — | 88% |
| AIME 2024 | — | 90% |
| AIME 2025 | — | 89% |
| HMMT Feb 2023 | — | 84% |
| HMMT Feb 2024 | — | 86% |
| HMMT Feb 2025 | — | 85% |
| BRUMO 2025 | — | 87% |
| MATH-500 | — | 88% |
o3 is ahead overall, 65 to 58. The biggest single separator in this matchup is BrowseComp, where the scores are 51.9% and 75%.
o3 has the edge for agentic tasks in this comparison, averaging 69.9 versus 58. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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