Llama 4 Scout and Maverick are Meta’s open-weight multimodal models — available today via multiple API providers with OpenAI-compatible endpoints. Scout offers a 10M-token context window at $0.08–$0.15 per 1M input tokens; Maverick beats GPT-4o on MMLU, HumanEval, and SWE-bench. Here’s how to integrate both.
What Is Llama 4? Scout, Maverick, and Behemoth Explained Llama 4 is Meta’s fourth-generation open-weight large language model family, released in April 2026 as a multimodal, Mixture-of-Experts architecture covering three tiers: Scout, Maverick, and the research-preview Behemoth. Scout has 17B active parameters out of ~109B total across 16 experts, with a groundbreaking 10-million-token context window — the largest available in any production API as of May 2026. Maverick scales to ~400B total parameters (still 17B active per forward pass) across 128 experts and delivers benchmark scores of 91.8% MMLU, 91.5% HumanEval, and 74.2% SWE-bench, outperforming GPT-4o and Gemini 2.0 Flash. Behemoth sits at ~2 trillion total parameters with 288B active — still in training and research preview, not yet available via public API. All three models support multimodal inputs (text + images), structured output, function calling, and streaming. The key architectural insight is that active parameter count — not total — determines inference cost, which is why both Scout and Maverick run at the speed of a ~17B dense model while achieving quality far above their class. Meta released these models under a custom Llama 4 Community License that permits commercial use with attribution for most use cases.
...
