GLM‑4.7 Review – Open‑Source Coding Powerhouse
Review • Jan 30, 2026
Core Engineering
GLM‑4.7 uses a 30 B dense architecture, eliminating the complexity of Mixture‑of‑Experts models. This yields predictable latency and simplifies deployment on commodity hardware.
- 30 B dense parameters
- 128K token context
- Open‑weight model on Hugging Face (›)
Benchmarks
| Benchmark | GLM‑4.7 Flash | Competitors |
|---|---|---|
| SWE‑Bench Verified | 73.8 % | 68.0 % GPT‑5.0 |
| SWE‑Bench Multilingual | 66.7 % | 53.8 % GPT‑5.0 |
| Terminal Bench 2.0 | 41.0 % | 24.5 % GPT‑5.0 |
| LiveCodeBench | 84 % | 71 % GPT‑5.0 |
| BrowseComp | 52 % | 40 % GPT‑5.0 |
Pricing & Access
- USD $0.02 per 1K tokens – ~10× cheaper than GPT‑4
- Community‑hosted: download weights and run locally; no cloud‑rate limits
Use‑Case Highlights
| Use‑Case | GLM‑4.7 Strength |
|---|---|
| Code Generation | Outperforms GPT‑4 on SWE‑Bench; fewer hallucinations |
| Multi‑Step Reasoning | Thinking‑before‑acting keeps context coherent; ideal for complex tasks |
| Terminal Automation | Understands shell syntax; generates bash scripts that work on first try |
Design Recommendation
We’ll use the default CNET News Digest style because it aligns with the directive’s preference and offers a clean, headline‑heavy format.
Sources: LLM‑Stats Review, Z.AI Blog, Vertu Review, Reddit Discussion.