GLM-5.2
Open-weight coding flagship at a fraction of frontier cost.
- Context window
- 1.0Mtokens
- Max output
- 131Ktokens
- Input price
- $1.40/ 1M tokens
- Output price
- $4.40/ 1M tokens
- Cached input
- $0.26/ 1M tokens
- Modalities
- Text
- Knowledge cutoff
- Mar 2026
- Released
- Jun 2026
- Parameters
- 753B
- License
- MIT
- Approx. speed
- —
What this model is
Zhipu's GLM-5.2 is a ~753B-parameter open-weight MoE (~40B active, MIT) that leads open models on Artificial Analysis' Intelligence Index (51 — the top open-weights score) while its Z.ai API list price ($1.40 in / $4.40 out) runs roughly one-sixth the closed frontier, and open-weight hosts price it lower still. It adds selectable High and Max thinking-effort reasoning modes for long-horizon coding. Its vendor-aggregate SWE-bench Pro (62.1) edges GPT-5.5's, though independent contamination-resistant runs of the GLM family land far lower, so its coding lead is softer than the headline suggests. Its 1M-token context holds an entire mid-sized codebase, and it ships as a drop-in Anthropic-compatible backend for Claude Code workflows — a value challenger rather than a frontier-reliable autonomous agent.
Strengths
- Best-in-class open-weight coding benchmarks
- 1M-token context for whole-repo work
- Roughly one-sixth the cost of frontier closed models
- Selectable High / Max thinking-effort reasoning modes
- Permissive MIT weights, self-hostable
- Strong math and reasoning (AIME ~99%)
Trade-offs
- Long agentic sessions less reliable than Claude/GPT
- Text-only flagship (vision is the separate GLM-5V)
- Benchmark scores overstate real-world autonomy
- Large model — throughput and latency costs
Best for
- Cost-efficient coding assistants
- Whole-repo refactors within a supervised loop
- Self-hosted / on-prem deployments
- Claude Code-compatible budget backend
- Math and reasoning tasks
Not ideal for
- Unsupervised long-horizon autonomous coding
- Vision-heavy multimodal tasks
Capability profile
Normalized 0–100 scores, comparable across the whole catalog.
- Reasoning
- 86
- Coding
- 77
- Math
- 88
- Writing
- 82
- Knowledge
- 85
- Speed
- 58
- Agentic
- 75
- Vision
- 10
- Multilingual
- 84
- Long Context
- 84
How it scores
Public benchmark results, with independent third-party results where available. Bars normalize percentages to 100 and Elo ratings to a 1500 ceiling.