Llama 4 (Meta) vs Cursor

Cursor edges out Llama 4 (Meta) by 0.4 points (8.3 vs 7.9) -- a A-tier vs B-tier split that's narrow but real. Not a blowout; both belong on a shortlist. The score gap shows up most clearly in the categories that matter for Cursor's strengths, so if those categories are your priority, the lead translates.

Pricing-wise, both tools have a free tier (Llama 4 (Meta) starts $0, Cursor starts $0), so you can test either without committing. Compare what each free tier actually unlocks -- usage caps, model access, and feature gates differ a lot more than the headline price suggests, especially as both vendors have tightened limits in 2026.

By use case: pick Llama 4 (Meta) when developers and teams who need a permissively-licensed open-weights model with strong tooling, long context (scout), or multimodal (maverick). Pick Cursor when developers who want the deepest ai integration possible and who are ready to work with agents rather than just autocomplete. The two tools aren't fighting for the same person -- they're aiming at adjacent jobs that occasionally overlap. If you're squarely in Cursor's lane, the tier-list ranking and the use-case fit point the same direction; if you're in Llama 4 (Meta)'s lane, the score gap matters less than the fit.

Bottom line: Cursor is the safer default for most readers, but Llama 4 (Meta) is competitive enough that the tie-breaker is your specific workload, not the spec sheet.