Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5V-Turbo
~58
0/8 categorieso4-mini (high)
58
1/8 categoriesGLM-5V-Turbo· o4-mini (high)
Treat this as a split decision. GLM-5V-Turbo makes more sense if you would rather avoid the extra latency and token burn of a reasoning model; o4-mini (high) is the better fit if agentic is the priority or you want the stronger reasoning-first profile.
GLM-5V-Turbo and o4-mini (high) finish on the same overall score, so this is less about a single winner and more about where the edge shows up. The headline says tie; the benchmark table is where the real choice happens.
o4-mini (high) 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 | o4-mini (high) |
|---|---|---|
| Agentico4-mini (high) wins | ||
| BrowseComp | 51.9% | 64% |
| OSWorld-Verified | 62.3% | 55% |
| BrowseComp-VL | 51.9% | — |
| OSWorld | 62.3% | — |
| AndroidWorld | 75.7% | — |
| WebVoyager | 88.5% | — |
| Terminal-Bench 2.0 | — | 58% |
| Coding | ||
| HumanEval | — | 74% |
| SWE-bench Verified | — | 68.1% |
| LiveCodeBench | — | 34% |
| SWE-bench Pro | — | 42% |
| 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 | — | 66% |
| OfficeQA Pro | — | 71% |
| Reasoning | ||
| MuSR | — | 78% |
| BBH | — | 83% |
| LongBench v2 | — | 75% |
| MRCRv2 | — | 74% |
| ARC-AGI-2 | — | 2.4% |
| Knowledge | ||
| MMLU | — | 82% |
| GPQA | — | 82% |
| SuperGPQA | — | 80% |
| MMLU-Pro | — | 76% |
| HLE | — | 13% |
| FrontierScience | — | 73% |
| SimpleQA | — | 80% |
| Instruction Following | ||
| IFEval | — | 83% |
| Multilingual | ||
| MGSM | — | 83% |
| MMLU-ProX | — | 81% |
| Mathematics | ||
| AIME 2023 | — | 83% |
| AIME 2024 | — | 93.4% |
| AIME 2025 | — | 92.7% |
| HMMT Feb 2023 | — | 79% |
| HMMT Feb 2024 | — | 81% |
| HMMT Feb 2025 | — | 80% |
| BRUMO 2025 | — | 82% |
| MATH-500 | — | 84% |
GLM-5V-Turbo and o4-mini (high) are tied on overall score, so the right pick depends on which category matters most for your use case.
o4-mini (high) has the edge for agentic tasks in this comparison, averaging 58.5 versus 58. Inside this category, BrowseComp 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.