Cohere Command A vs Llama 4 (Meta)

Llama 4 (Meta) edges out Cohere Command A by 0.4 points (7.9 vs 7.5) -- a B-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 Llama 4 (Meta)'s strengths, so if those categories are your priority, the lead translates.

Pricing-wise, both tools have a free tier (Cohere Command A starts $0, Llama 4 (Meta) 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 Cohere Command A when mid-size to large enterprises needing a multilingual open-weight model with low-ish infrastructure requirements (2x h100 for full model). 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). The two tools aren't fighting for the same person -- they're aiming at adjacent jobs that occasionally overlap. If you're squarely in Llama 4 (Meta)'s lane, the tier-list ranking and the use-case fit point the same direction; if you're in Cohere Command A's lane, the score gap matters less than the fit.

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