Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5V-Turbo
~58
Winner · 1/8 categoriesNemotron Ultra 253B
41
0/8 categoriesGLM-5V-Turbo· Nemotron Ultra 253B
Pick GLM-5V-Turbo if you want the stronger benchmark profile. Nemotron Ultra 253B only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
GLM-5V-Turbo is clearly ahead on the aggregate, 58 to 41. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5V-Turbo's sharpest advantage is in agentic, where it averages 58 against 46.7. The single biggest benchmark swing on the page is OSWorld-Verified, 62.3% to 45%.
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 Nemotron Ultra 253B. That is roughly Infinityx on output cost alone. Nemotron Ultra 253B 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. GLM-5V-Turbo gives you the larger context window at 200K, compared with 32K for Nemotron Ultra 253B.
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 | Nemotron Ultra 253B |
|---|---|---|
| AgenticGLM-5V-Turbo wins | ||
| BrowseComp | 51.9% | 50% |
| OSWorld-Verified | 62.3% | 45% |
| BrowseComp-VL | 51.9% | — |
| OSWorld | 62.3% | — |
| AndroidWorld | 75.7% | — |
| WebVoyager | 88.5% | — |
| Terminal-Bench 2.0 | — | 46% |
| Coding | ||
| HumanEval | — | 41% |
| SWE-bench Verified | — | 31% |
| LiveCodeBench | — | 30% |
| SWE-bench Pro | — | 38% |
| 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 | — | 37% |
| OfficeQA Pro | — | 54% |
| Reasoning | ||
| MuSR | — | 45% |
| BBH | — | 77% |
| LongBench v2 | — | 55% |
| MRCRv2 | — | 56% |
| Knowledge | ||
| MMLU | — | 49% |
| GPQA | — | 48% |
| SuperGPQA | — | 46% |
| MMLU-Pro | — | 63% |
| HLE | — | 11% |
| FrontierScience | — | 49% |
| SimpleQA | — | 47% |
| Instruction Following | ||
| IFEval | — | 78% |
| Multilingual | ||
| MGSM | — | 74% |
| MMLU-ProX | — | 68% |
| Mathematics | ||
| AIME 2023 | — | 49% |
| AIME 2024 | — | 51% |
| AIME 2025 | — | 50% |
| HMMT Feb 2023 | — | 45% |
| HMMT Feb 2024 | — | 47% |
| HMMT Feb 2025 | — | 46% |
| BRUMO 2025 | — | 48% |
| MATH-500 | — | 74% |
GLM-5V-Turbo is ahead overall, 58 to 41. The biggest single separator in this matchup is OSWorld-Verified, where the scores are 62.3% and 45%.
GLM-5V-Turbo has the edge for agentic tasks in this comparison, averaging 58 versus 46.7. Inside this category, OSWorld-Verified is the benchmark that creates the most daylight between them.
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