Llama 4 (Meta) vs Google Antigravity
Which one should you pick? Here's the full breakdown.
Llama 4 (Meta)
Meta's open-weights flagship family -- Scout (10M context), Maverick (multimodal 400B MoE), Behemoth in preview
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 | Llama 4 (Meta) | Google Antigravity |
|---|---|---|
| Ease of Use | 5.0 | 8.0 |
| Output Quality | 8.5 | 8.5 |
| Value | 9.0 | 6.0 |
| Features | 9.0 | 9.5 |
| Overall | 7.9 | 8.0 |
Pricing Comparison
| Feature | Llama 4 (Meta) | Google Antigravity |
|---|---|---|
| Free Tier | Yes | Yes |
| Starting Price | $0 | $0 |
Benchmark Head-to-Head
Llama 4 Maverick (17B/400B MoE) benchmarks — Google Antigravity has no published benchmarks
| Benchmark | Description | Score |
|---|---|---|
| MMLU-Pro | Harder multi-subject reasoning | 80.5% |
| GPQA Diamond | Graduate-level science questions | 69.8% |
| HumanEval | Python code generation | 88% |
| MMMU (multimodal) | 73.4% |
Which Should You Pick?
Pick Llama 4 (Meta) if...
- ✓Better value for money (9/10)
Developers and teams who need a permissively-licensed open-weights model with strong tooling, long context (Scout), or multimodal (Maverick). Safe default choice given the ecosystem.
Visit Llama 4 (Meta)Pick Google Antigravity if...
- ✓Easier to use (8 vs 5)
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
Llama 4 (Meta) and Google Antigravity are extremely close overall. Your choice comes down to specific needs -- Llama 4 (Meta) is better for developers and teams who need a permissively-licensed open-weights model with strong tooling, long context (scout), or multimodal (maverick), while Google Antigravity works best for developers working on large, multi-file projects who want to parallelize their workflow.