Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5V-Turbo
~58
0/8 categoriesQwen2.5-1M
62
Winner · 1/8 categoriesGLM-5V-Turbo· Qwen2.5-1M
Pick Qwen2.5-1M if you want the stronger benchmark profile. GLM-5V-Turbo only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Qwen2.5-1M is clearly ahead on the aggregate, 62 to 58. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen2.5-1M's sharpest advantage is in agentic, where it averages 64.7 against 58. The single biggest benchmark swing on the page is BrowseComp, 51.9% to 72%.
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 Qwen2.5-1M. That is roughly Infinityx on output cost alone. Qwen2.5-1M gives you the larger context window at 1M, 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 | Qwen2.5-1M |
|---|---|---|
| AgenticQwen2.5-1M wins | ||
| BrowseComp | 51.9% | 72% |
| OSWorld-Verified | 62.3% | 59% |
| BrowseComp-VL | 51.9% | — |
| OSWorld | 62.3% | — |
| AndroidWorld | 75.7% | — |
| WebVoyager | 88.5% | — |
| Terminal-Bench 2.0 | — | 65% |
| Coding | ||
| HumanEval | — | 76% |
| SWE-bench Verified | — | 47% |
| LiveCodeBench | — | 40% |
| SWE-bench Pro | — | 49% |
| 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 | — | 63% |
| OfficeQA Pro | — | 75% |
| Reasoning | ||
| MuSR | — | 79% |
| BBH | — | 82% |
| LongBench v2 | — | 82% |
| MRCRv2 | — | 81% |
| Knowledge | ||
| MMLU | — | 84% |
| GPQA | — | 83% |
| SuperGPQA | — | 81% |
| MMLU-Pro | — | 74% |
| HLE | — | 10% |
| FrontierScience | — | 74% |
| SimpleQA | — | 81% |
| Instruction Following | ||
| IFEval | — | 84% |
| Multilingual | ||
| MGSM | — | 81% |
| MMLU-ProX | — | 80% |
| Mathematics | ||
| AIME 2023 | — | 85% |
| AIME 2024 | — | 87% |
| AIME 2025 | — | 86% |
| HMMT Feb 2023 | — | 81% |
| HMMT Feb 2024 | — | 83% |
| HMMT Feb 2025 | — | 82% |
| BRUMO 2025 | — | 84% |
| MATH-500 | — | 83% |
Qwen2.5-1M is ahead overall, 62 to 58. The biggest single separator in this matchup is BrowseComp, where the scores are 51.9% and 72%.
Qwen2.5-1M has the edge for agentic tasks in this comparison, averaging 64.7 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.