Qwen3.8-27BvsGLM-5.2
Qwen3.8-27B | GLM-5.2 | |
|---|---|---|
| Specifications | ||
Parameters | 27B | 744B |
Context window | 262k | 1M |
| 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. | — | 31% |
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% | 62.1%Best |
Agentic coding CursorBench v3.2Cursor's own test of harder, real-world coding tasks inside a code editor, on the refreshed v3.2 task set. Scores aren't comparable with v3.1. Higher is better. | — | 55% |
Agentic coding CursorBench v3.1Cursor's own test of harder, real-world coding tasks inside a code editor. Higher is better. | — | 54.6% |
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% | 44%Best |
Next.js coding Next.js EvalsVercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | — | 88% |
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 computer work Frontier-Bench v0.1A hard, ever-evolving set of real computer tasks — coding, system administration, data work, and more — that an AI agent has to complete on its own. Run by the Harbor / Laude Institute team as the successor to Terminal-Bench (v0.1 is the first release of the task set). The score is the share of tasks solved. Higher is better. | — | 5.1% |
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% | 81%Best |
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% | 40.5%Best |
Multidisciplinary reasoning Humanity's Last Exam · with toolsHumanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 54.7% |
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% | 91.2%Best |
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% | — |
Knowledge work GDPval-AA v2economically valuable knowledge work (v2, re-based Elo) | — | 1514 |
Community preference (code) Arena Elo (Code)Like the text arena, but people vote on which AI writes better code. The votes become a chess-style Elo rating on arena.ai. Higher is better. | — | 1587 |
| Overview | ||
| Company | Qwen | Z.ai |
| Release date | Aug 14 2026 | Jun 16 2026 |
| Access | Open Weight | Open Weight |
Which is better: Qwen3.8-27B or GLM-5.2?
GLM-5.2 leads Qwen3.8-27B on 5 of the 5 benchmarks they both report. GLM-5.2 shipped 59 days before Qwen3.8-27B, so benchmark comparisons should account for the intervening progress.
Qwen3.8-27B has 27B parameters, while GLM-5.2 has 744B. Context windows are 262k (Qwen3.8-27B) vs 1M (GLM-5.2).
On SWE-Bench Pro, GLM-5.2 leads at 62.1% vs Qwen3.8-27B at 61.7%. On DeepSWE 1.1, GLM-5.2 leads at 44% vs Qwen3.8-27B at 42.2%. On Terminal-Bench 2.1, GLM-5.2 leads at 81% vs Qwen3.8-27B at 73%. On Humanity's Last Exam · no tools, GLM-5.2 leads at 40.5% vs Qwen3.8-27B at 30.8%. On GPQA Diamond, GLM-5.2 leads at 91.2% vs Qwen3.8-27B at 89.2%.
Frequently asked questions
Qwen3.8-27B was released by Qwen on Aug 14 2026.
GLM-5.2 was released by Z.ai on Jun 16 2026.
GLM-5.2 leads on SWE-Bench Pro — Qwen3.8-27B 61.7% vs GLM-5.2 62.1%.
GLM-5.2 leads on Humanity's Last Exam · no tools — Qwen3.8-27B 30.8% vs GLM-5.2 40.5%.
Qwen3.8-27B has a 262k context window; GLM-5.2 has 1M.