Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5V-Turbo
~58
0/8 categoriesGPT-5.1-Codex-Max
81
Winner · 1/8 categoriesGLM-5V-Turbo· GPT-5.1-Codex-Max
Pick GPT-5.1-Codex-Max 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.
GPT-5.1-Codex-Max is clearly ahead on the aggregate, 81 to 58. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.1-Codex-Max's sharpest advantage is in agentic, where it averages 86 against 58. The single biggest benchmark swing on the page is BrowseComp, 51.9% to 85%.
GPT-5.1-Codex-Max is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $1.20 input / $4.00 output per 1M tokens for GLM-5V-Turbo. That is roughly 2.0x on output cost alone. GPT-5.1-Codex-Max 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. GPT-5.1-Codex-Max gives you the larger context window at 400K, 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 | GPT-5.1-Codex-Max |
|---|---|---|
| AgenticGPT-5.1-Codex-Max wins | ||
| BrowseComp | 51.9% | 85% |
| OSWorld-Verified | 62.3% | 82% |
| BrowseComp-VL | 51.9% | — |
| OSWorld | 62.3% | — |
| AndroidWorld | 75.7% | — |
| WebVoyager | 88.5% | — |
| Terminal-Bench 2.0 | — | 90% |
| Coding | ||
| HumanEval | — | 94% |
| SWE-bench Verified | — | 77.9% |
| LiveCodeBench | — | 67% |
| SWE-bench Pro | — | 84% |
| 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 | — | 85% |
| OfficeQA Pro | — | 92% |
| Reasoning | ||
| MuSR | — | 92% |
| BBH | — | 92% |
| LongBench v2 | — | 90% |
| MRCRv2 | — | 93% |
| Knowledge | ||
| MMLU | — | 98% |
| GPQA | — | 96% |
| SuperGPQA | — | 94% |
| MMLU-Pro | — | 82% |
| HLE | — | 27% |
| FrontierScience | — | 84% |
| SimpleQA | — | 94% |
| Instruction Following | ||
| IFEval | — | 91% |
| Multilingual | ||
| MGSM | — | 89% |
| MMLU-ProX | — | 87% |
| Mathematics | ||
| AIME 2023 | — | 99% |
| AIME 2024 | — | 99% |
| AIME 2025 | — | 98% |
| HMMT Feb 2023 | — | 95% |
| HMMT Feb 2024 | — | 97% |
| HMMT Feb 2025 | — | 96% |
| BRUMO 2025 | — | 96% |
| MATH-500 | — | 93% |
GPT-5.1-Codex-Max is ahead overall, 81 to 58. The biggest single separator in this matchup is BrowseComp, where the scores are 51.9% and 85%.
GPT-5.1-Codex-Max has the edge for agentic tasks in this comparison, averaging 86 versus 58. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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