Qwen3.8-27BvsGLM-4.5
Qwen3.8-27B | GLM-4.5 | |
|---|---|---|
| Specifications | ||
Parameters | 27B | 355B |
Context window | 262k | 128k |
| Benchmarks | ||
Nonsense detection BullshitBench v2Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | — | 8% |
Agentic coding SWE-Bench ProCan the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | 61.7% | — |
Coding SWE-Bench VerifiedReal coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | — | 64.2% |
Agentic coding DeepSWE 1.1Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | 42.2% | — |
Repo-level code generation NL2Repo-BenchTests whether the AI can turn a natural-language requirement into working code across an entire repository, not just produce a single function or patch. Higher is better. | 42.3% | — |
Software engineering QwenSWEBenchQwen's in-house coding benchmark for evaluating a model's ability to complete software-engineering work. Higher is better. | 79% | — |
Competitive coding LiveCodeBenchCoding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | 90.3% | — |
Agentic terminal coding Terminal-Bench 2.1Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | 73% | — |
Professional tool use JobBenchTests the AI on professional workplace tasks that require using real work tools — the kind of multi-step jobs an office worker handles. Higher is better. | 33.4% | — |
Long-horizon office work CoWorkBenchTests long-running office tasks across fields including computer science, finance, law, medicine, and other productivity work. Higher is better. | 70.7% | — |
Multidisciplinary reasoning Humanity's Last Exam · no toolsHumanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | 30.8% | — |
Science GPQA DiamondGraduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 89.2%Best | 79.1% |
Instruction following IFBenchTests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | 79.5% | — |
Agentic computer use Agent's Last Exam · pass@1A hard set of desktop and operating-system tasks an AI agent has to finish by looking at the screen and working the machine itself. The score is the share it passes outright — partial credit does not count. Higher is better. | 20.4% | — |
Agentic computer use Agent's Last Exam · scoreThe graded score on the same desktop and operating-system tasks in Agent's Last Exam, giving partial credit for progress beyond the strict pass-or-fail result. Higher is better. | 42.9% | — |
| Overview | ||
| Company | Qwen | Z.ai |
| Release date | Aug 14 2026 | Jul 28 2025 |
| Access | Open Weight | Open Weight |
Which is better: Qwen3.8-27B or GLM-4.5?
Qwen3.8-27B leads GLM-4.5 on 1 of the 1 benchmark they both report (GPQA Diamond). GLM-4.5 shipped 382 days before Qwen3.8-27B, so benchmark comparisons should account for the intervening progress.
Qwen3.8-27B has 27B parameters, while GLM-4.5 has 355B. Context windows are 262k (Qwen3.8-27B) vs 128k (GLM-4.5).
On GPQA Diamond, Qwen3.8-27B leads at 89.2% vs GLM-4.5 at 79.1%.
Frequently asked questions
Qwen3.8-27B was released by Qwen on Aug 14 2026.
GLM-4.5 was released by Z.ai on Jul 28 2025.
Qwen3.8-27B leads on GPQA Diamond — Qwen3.8-27B 89.2% vs GLM-4.5 79.1%.
Qwen3.8-27B has a 262k context window; GLM-4.5 has 128k.