GLM / Z.ai (Zhipu AI) vs Google Antigravity
Which one should you pick? Here's the full breakdown.
GLM / Z.ai (Zhipu AI)
Zhipu AI's open-weights family -- GLM-4.6 text flagship and GLM-4.6V multimodal, true MIT licensed
Google Antigravity
Google's agent-first AI IDE -- deploys up to 5 autonomous coding agents in parallel on a VS Code fork
Powered by Gemini 3.1 Pro / Claude Opus 4.6 / GPT-OSS 120B (multi-model)
| Category | GLM / Z.ai (Zhipu AI) | Google Antigravity |
|---|---|---|
| Ease of Use | 6.5 | 8.0 |
| Output Quality | 8.5 | 8.5 |
| Value | 9.0 | 6.0 |
| Features | 8.0 | 9.5 |
| Overall | 8.0 | 8.0 |
Pricing Comparison
| Feature | GLM / Z.ai (Zhipu AI) | Google Antigravity |
|---|---|---|
| Free Tier | Yes | Yes |
| Starting Price | $0 | $0 |
Benchmark Head-to-Head
GLM-4.6 benchmarks — Google Antigravity has no published benchmarks
| Benchmark | Description | Score |
|---|---|---|
| MMLU-Pro | Harder multi-subject reasoning | 81.2% |
| GPQA Diamond | Graduate-level science questions | 74.5% |
| HumanEval | Python code generation | 89.1% |
| SWE-Bench Verified | 64.2% | |
| BFCL (function calling) | 88% |
Which Should You Pick?
Pick GLM / Z.ai (Zhipu AI) if...
- ✓Better value for money (9/10)
Teams that need genuine MIT-licensed frontier open weights with no commercial strings. Especially strong for agentic workflows and vision (GLM-4.6V).
Visit GLM / Z.ai (Zhipu AI)Pick Google Antigravity if...
- ✓Easier to use (8 vs 6.5)
- ✓More features (9.5 vs 8)
Developers working on large, multi-file projects who want to parallelize their workflow. If you regularly work on 3-5 tasks simultaneously (fix a bug, add a feature, write tests, refactor), Antigravity's multi-agent architecture is unmatched.
Visit Google AntigravityOur Verdict
GLM / Z.ai (Zhipu AI) and Google Antigravity are extremely close overall. Your choice comes down to specific needs -- GLM / Z.ai (Zhipu AI) is better for teams that need genuine mit-licensed frontier open weights with no commercial strings, while Google Antigravity works best for developers working on large, multi-file projects who want to parallelize their workflow.