Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5V-Turbo
~58
1/8 categoriesMiniMax M2.7
~66
Winner · 0/8 categoriesGLM-5V-Turbo· MiniMax M2.7
Pick MiniMax M2.7 if you want the stronger benchmark profile. GLM-5V-Turbo only becomes the better choice if agentic is the priority.
MiniMax M2.7 is clearly ahead on the aggregate, 66 to 58. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5V-Turbo is also the more expensive model on tokens at $1.20 input / $4.00 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M2.7. That is roughly 3.3x on output cost alone.
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 | MiniMax M2.7 |
|---|---|---|
| AgenticGLM-5V-Turbo wins | ||
| BrowseComp | 51.9% | — |
| OSWorld-Verified | 62.3% | — |
| BrowseComp-VL | 51.9% | — |
| OSWorld | 62.3% | — |
| AndroidWorld | 75.7% | — |
| WebVoyager | 88.5% | — |
| Terminal-Bench 2.0 | — | 57% |
| Tau2-Airline | — | 80.0% |
| Tau2-Telecom | — | 84.8% |
| PinchBench | — | 89.8% |
| BFCL v4 | — | 70.6% |
| Toolathlon | — | 46.3% |
| MLE-Bench Lite | — | 66.6% |
| MM-ClawBench | — | 62.7% |
| Coding | ||
| SWE-bench Verified* | — | 75.4% |
| SWE-bench Pro | — | 56.2% |
| SWE Multilingual | — | 76.5% |
| Multi-SWE Bench | — | 52.7% |
| VIBE-Pro | — | 55.6% |
| NL2Repo | — | 39.8% |
| 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% | — |
| GDPval-AA | — | 1495 |
| Reasoning | ||
| Coming soon | ||
| Knowledge | ||
| GPQA-D | — | 86.2% |
| MMLU-Pro (Arcee) | — | 80.8% |
| Instruction Following | ||
| IFBench | — | 75.7% |
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| AIME25 (Arcee) | — | 80.0% |
MiniMax M2.7 is ahead overall, 66 to 58.
GLM-5V-Turbo has the edge for agentic tasks in this comparison, averaging 58 versus 57. MiniMax M2.7 stays close enough that the answer can still flip depending on your workload.
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