GLM 5.2 beats Claude in our benchmarks

Original Article ↗ Hacker News Discussion ↗

Model performance & capabilities

  • Many commenters report GLM‑5.2 is a strong “workhorse” for coding and security work, often comparable to Claude Opus 4.6 and sometimes close to 4.8 in practice.
  • Some say it never refuses “risky” commands and is particularly good at finding and fixing vulnerabilities; others find it unstable or “spiraling into nonsense” on certain tasks.
  • Several claim Chinese models (GLM, DeepSeek, Kimi, MiMo) are now competitive with or better than US frontier models for coding and cyber, though not necessarily overall.

Benchmarks, methodology & harness effects

  • The Semgrep benchmark is narrow: IDOR vulnerabilities in specific open‑source repos, single task, single run. Many view the “beats Claude” headline as over‑stated and click‑driven.
  • Multiple people stress that harness/agent design (Claude Code, OpenCode, Pi, custom Pydantic harnesses, etc.) often matters more than raw model choice. Same model can look weak or strong depending on scaffolding.
  • Some external benchmarks and personal tests put GLM‑5.2 just below Opus 4.6 on multi‑agent coding, with better price/performance. Others find DeepSeek V4 Pro or Gemma 4 31B stronger for bug‑hunting in their pipelines.
  • There is suspicion that some models, especially from Chinese labs, may be “benchmaxxed” (overfitted to public benchmarks), but this is disputed and unproven in the thread.

Cost, access, and local vs cloud

  • Strong interest in GLM‑5.2 via OpenRouter, Neuralwatt, z.ai coding plans, OpenCode, etc. Some find cloud pricing extremely favorable (hundreds of millions of tokens for under $20); others see z.ai and Ollama-hosted GLM as overpriced relative to Claude/Codex subscriptions.
  • Running full GLM‑5.2 locally is seen as impractical for most: needs 8–16 high‑end GPUs or extreme quantization with very slow throughput and huge disk streaming. Quantized variants can be demoed but not used comfortably for heavy work.
  • Debate over whether local inference will catch up on consumer hardware; some are optimistic (small models already beat older GPT‑4), others think RAM and GPU constraints plus cloud economics will keep big models remote.

Geopolitics, regulation & open vs closed

  • Long subthreads speculate about US export/import controls on powerful foreign models, payment‑network pressure on hosts, and parallels to past encryption controls. Legal feasibility is contested.
  • Some argue Chinese open‑weight releases are a strategic “dumping” move to undermine US AI business models; others see it as normal competition.
  • Many expect frontier closed models to become restricted or “defense‑only,” making strong open‑weight models like GLM‑5.2 increasingly important for ordinary developers and defenders.