GLM-5.1: Towards Long-Horizon Tasks

Original Article ↗ Hacker News Discussion ↗

Model quality & comparisons

  • Many find GLM‑5/5.1 very strong for coding, often comparing it favorably to Sonnet, sometimes even Opus, especially for backend work and long, structured tasks.
  • Others report it underperforms vs frontier closed models (Opus, GPT, Gemini) or even some other open-weight models (Qwen, Kimi), particularly on harder or general-intelligence tasks.
  • Some benchmarks show GLM‑5 outperforming 5.1 on general reasoning, suggesting 5.1 may be more tuned for agentic/coding use than broad intelligence.
  • There are users who consider the models “low quality” for tasks like PDF parsing or simple factual checks, while others say they rely on GLM daily for complex, unusual stacks and long feature planning.

Long context behavior

  • Major recurring complaint: “context rot” around ~60k–120k tokens, where responses devolve into loops, contradictions, or pure gibberish.
  • Several users say 5.1 was initially stable to ~200k but regressed after infrastructure changes (quantization, KV cache, serving-tier switches are suspected).
  • Many cope by manual or automatic “compaction,” restarting sessions, or using dynamic context-pruning tools; keeping context under ~100k is often cited as workable.
  • Some argue these issues are hosting/harness bugs (Z.ai, OpenCode) rather than the base model; other providers reportedly show more graceful degradation.

Infrastructure, pricing, and plans

  • Experiences with Z.ai’s own service are highly variable: some regions report timeouts, slowness, and frequent failures; others see stable, high-quality usage.
  • Multiple reports of Lite/Coding Lite plans being “gimped” (heavier quantization, more errors), while Pro/higher tiers are described as “genuinely excellent.”
  • Fixed-price Z.ai plans are contrasted with per-token access via third-party providers; opinions differ on which is cheaper or more reliable in practice.

Local / open-weight and hardware

  • GLM‑5.1 GGUF quantizations are enormous (hundreds of GB), making truly local inference impractical for typical hobbyists unless using SSD offload and tolerating very slow speeds.
  • Debate over the future: some see local/private inference as inevitable as hardware improves; others think top-tier models will remain data-center-only.

Harnesses, tooling, and benchmarks

  • Harness choice (OpenCode, Claude Code, Z Code, Pi, Cursor, etc.) is repeatedly said to matter a lot; some bugs are attributed to specific harnesses’ reasoning implementations.
  • Independent benchmarks note strong one-shot performance and competitive agentic ability, but also highlight context-rot and possible overtraining on fixed toolsets.

Safety, alignment, and use cases

  • Several anecdotes show GLM‑5.1 willingly discovering security vulnerabilities (e.g., blind SQL injection, bot-detection bypass) without much pushback, seen by some as impressive and by others as under-aligned.
  • Overall sentiment: GLM‑5.x is a major open-weight milestone, but long-horizon stability, hosting reliability, and safety trade-offs remain contentious.