Llama

If you are deciding between Llama 4 Scout and Maverick for production APIs, start with one rule: Scout for ultra-long context and summarization pipelines, Maverick for higher expert routing on mixed multimodal tasks, then validate on your exact endpoint with real traffic. On real systems, throughput and contract behavior vary more by provider implementation than by paper spec alone. What are Scout and Maverick in real API terms, and how do they differ for workloads? Scout is a long-context-first generation model profile and Maverick is an expert-heavy multimodal profile, and the difference matters because API architectures optimize around context depth, inference cost, and failure modes. In Meta’s April 5, 2025 launch, Scout was positioned with 17B active parameters and 16 experts plus a 10M token context target, while Maverick used 17B active parameters with 128 experts and 1M context in provider-facing specs. In a production retrieval summarizer I ran, Scout handled legal bundles and internal policy docs more consistently because prompts could keep prior evidence in-context; Maverick shined in mixed text-image assistants where short-to-medium context combined with strong routing logic won. The takeaway is clear: pick the model family based on your payload shape and context contract, not only benchmark headlines. ...

Choosing a local LLM starts with the exact model identity and the resources it needs. This guide covers the families discussed in the original article and explains how to evaluate their fit. It does not present a locally measured benchmark leaderboard. Correction — October 7, 2026: Llama 3.3 was incorrectly described as an 8B model, and Mistral Small 3 as a 7B model. Their correct sizes are 70B and 24B. The previous hardware-speed table, blanket privacy claims and inconsistent cloud-cost calculations have also been removed or corrected. ...