LLaMA 3.2vsGLM-5.3
LLaMA 3.2 | GLM-5.3 | |
|---|---|---|
| Specifications | ||
Parameters | 1B/3B | 743B |
| Benchmarks | ||
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. | — | 66.9% |
Agentic terminal coding Terminal-Bench 3.0command-line task completion (v3.0, much harder task set) | — | 28.3% |
Cybersecurity CyberGymTests the AI on cybersecurity challenges — finding and exploiting software weaknesses inside a safe sandbox. Higher is better. | — | 84.5% |
Cybersecurity ExploitBenchA 'capability ladder' for security research, built by CMU researchers: the AI is given known bugs in Chrome's V8 engine and scored on how far it gets toward a working exploit inside a research sandbox — from understanding the patch to triggering a crash. Higher is better. | — | 54.4% |
Cybersecurity ExploitGym · 6-hour budgetCan an AI agent turn a known software vulnerability into a working attack in a controlled lab? Built by MPI-SP researchers, the score is how many of 898 real cases (userspace programs, the V8 engine, the Linux kernel) it cracks — here with a 6-hour compute budget per case. Higher is better. | — | 130 |
Cybersecurity ExploitGym · 2-hour budgetCan an AI agent turn a known software vulnerability into a working attack in a controlled lab? Built by MPI-SP researchers, the score is how many of 898 real cases (userspace programs, the V8 engine, the Linux kernel) it cracks — here with a 2-hour compute budget per case. Higher is better. | — | 105 |
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. | — | 62.5% |
Agentic computer use Agent's Last ExamA 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. | — | 28.5% |
Business workflows AutomationBenchTests whether the AI can run real multi-step business workflows — the kind of end-to-end office processes companies want to automate — from start to finish. Higher is better. | — | 48.2% |
Knowledge work GDPval-AA v2economically valuable knowledge work (v2, re-based Elo) | — | 1769 |
| Overview | ||
| Company | Meta | Z.ai |
| Release date | Sep 25 2024 | Aug 14 2026 |
| Access | Open Weight | Proprietary |
Which is better: LLaMA 3.2 or GLM-5.3?
LLaMA 3.2 and GLM-5.3 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 3.2 shipped 688 days before GLM-5.3, so benchmark comparisons should account for the intervening progress.
LLaMA 3.2 has 1B/3B parameters, while GLM-5.3 has 743B. LLaMA 3.2 is open weight, while GLM-5.3 is proprietary.
Direct benchmark comparisons are unavailable — LLaMA 3.2 and GLM-5.3 don't publish scores on any of the same benchmarks.
Frequently asked questions
LLaMA 3.2 was released by Meta on Sep 25 2024.
GLM-5.3 was released by Z.ai on Aug 14 2026.
LLaMA 3.2 is an open weight model released by Meta. GLM-5.3 is a proprietary model released by Z.ai.