Meta just dropped Llama 4, flexing a 2-trillion-parameter behemoth like it’s no big deal. Meanwhile, the rest of us are still trying to figure out if Llama 3 was ever actually useful. But hey, bigger numbers mean progress, right? Right?
The Models: Maverick, Scout, and… Behemoth? 🎭
- Llama 4 Maverick & Scout: Available now, with 1M+ token context windows (because who doesn’t love a model that remembers everything but still can’t answer your question correctly?).
- Behemoth: Coming soon, because why release a finished product when you can hype an unfinished one?
- Multimodal: Handles text, video, images—because if there’s one thing AI needs, it’s more ways to misinterpret reality.
The Benchmark Circus 🎪
Meta claims Behemoth beats GPT-4.5, Gemini 2.0, and Claude Sonnet 3.7 in some tests. But let’s be real—benchmarks are the participation trophies of AI. Everyone wins until real users start asking hard questions.
The Real Question: Does Any of This Matter?
Sure, 10M-token context sounds impressive—until you realize most enterprise use cases still fail at basic reasoning. And “reduced political bias”? We’ll believe it when we see it. Meta’s playing the bigger-is-better game, but the real winners will be those who actually make AI useful, not just ludicrously large. Until then, enjoy the hype train. 🚂