Llama 4 (Meta) vs Augment Code Intent
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
Augment Code Intent
Spec-driven multi-agent orchestration for code -- coordinator + implementor agents in isolated git worktrees + verifier. Works with Augment's Auggie, Claude Code, Codex, and OpenCode. Public beta 2026-02-10
| Category | Llama 4 (Meta) | Augment Code Intent |
|---|---|---|
| Ease of Use | 5.0 | 7.0 |
| Output Quality | 8.5 | 8.0 |
| Value | 9.0 | 8.0 |
| Features | 9.0 | 9.0 |
| Overall | 7.9 | 8.0 |
Pricing Comparison
| Feature | Llama 4 (Meta) | Augment Code Intent |
|---|---|---|
| Free Tier | Yes | No |
| Starting Price | $0 | Included in Auggie subscription |
Benchmark Head-to-Head
Llama 4 Maverick (17B/400B MoE) benchmarks — Augment Code Intent 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)
- ✓Has a free tier
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 Augment Code Intent if...
- ✓Easier to use (7 vs 5)
Engineering teams already using Augment Code's Auggie or running mixed Claude-Code + Codex workflows who want higher-level orchestration than writing LangGraph graphs from scratch. Also teams that want git-worktree-isolated parallel agent work with a verifier in the loop.
Visit Augment Code IntentOur Verdict
Llama 4 (Meta) and Augment Code Intent 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 Augment Code Intent works best for engineering teams already using augment code's auggie or running mixed claude-code + codex workflows who want higher-level orchestration than writing langgraph graphs from scratch.