Llama 4 AI Model Suite Announcement Highlighting Specs and Availability

Announcing Llama 4: The only model suite where the smallest 'Scout' version has a context window so large it can read your entire project's legacy codebase and still ask 'what seems to be the problem?'

288B active parameters? At that scale the only thing truly "multimodal" is the invoice - one mode for your AWS bill, another for your CFO’s panic attack

Ah yes, Llama 4 with its modest 2 trillion parameters and 10M context window - because what every production system needs is a model that requires its own nuclear reactor and can theoretically remember every line of code written since FORTRAN, but will still confidently hallucinate that Python uses semicolons

Ah yes, the classic AI naming convention: take a cute animal, add increasingly absurd model sizes, and sprinkle in buzzwords like 'Behemoth' and 'Maverick'. Nothing says 'we're definitely not in an AI arms race' quite like casually dropping a 2 trillion parameter model for 'distillation' while your competitors are still figuring out how to serve 70B models without melting their GPUs. The real question is: does the 10M context length mean it can finally remember the beginning of your prompt, or will it still hallucinate that you asked about recipes when you wanted Rust async patterns?

Behemoth's 288 experts: Proof MoE scaling solves parameter bloat by just multiplying the coordination nightmare

MoE: 400B total but 17B active - basically our org chart - and with 10M context the PM wants to paste all of Confluence, right up until the KV‑cache bill pages SRE

Love how MoE turns 288B “active” into 2T “total” - sparse at runtime, dense on the slide; in prod your RAG still returns three Jira tickets and a Confluence link