Llama 4

Meta released the Llama 4 family in April 2025, and by mid-2026 these models have settled into clear roles. Llama 4 Scout is the long-context specialist with a 10 million token window and the cheapest per-token cost, while Llama 4 Maverick is the frontier-quality generalist that beats GPT-4o on several benchmarks. Both share the same 17B active parameters via Mixture-of-Experts architecture, but they’re built for very different jobs. Here’s exactly when to use each one, with real pricing, benchmark data, and deployment strategies. ...

Llama 4 local deployment is practical if you match the model to the hardware: run Scout quantized for workstation experiments, use vLLM or SGLang on H100/H200 servers for API serving, and treat Maverick as a multi-GPU or heavily quantized model. Quick answer: what hardware can actually run Llama 4 locally? Llama 4 local deployment is the process of running Meta’s Llama 4 Scout or Llama 4 Maverick weights on hardware you control, from a 24 GB VRAM workstation to an 8xH100 server. Scout is the easier target because it has 17B active parameters, 16 experts, and 109B total parameters; Maverick also activates 17B parameters but has 128 experts and about 400B total parameters. In practice, a quantized Scout build can be useful on one high-end consumer GPU, while production Scout and most Maverick deployments belong on H100, H200, or dual 48 GB workstation hardware. The main mistake is assuming active parameters define memory use. Mixture-of-experts lowers compute per token, but disk, VRAM, and sharding still care about the full model. The takeaway: choose Scout for local iteration and Maverick only when your hardware budget is explicit. ...

Gemma 4 31B scores 89.2% on AIME 2026 — a 330% improvement over Gemma 3 27B’s 20.8% — while Qwen3-235B-A22B leads on GPQA Diamond at 77.2% and Llama 4 Scout holds the record with a 10 million token context window. Three competitive open-source model families launched in 2026, each with distinct architectural advantages that make the choice non-obvious. Gemma 4 leads on reasoning-per-parameter efficiency. Llama 4’s Scout model offers an unmatched context window for processing entire codebases. Qwen 3 provides the strongest raw coding performance at full size. This guide covers the technical and practical differences for developers choosing which family to run locally or deploy in production. ...

Llama 4 is a model family, not a promise that every hosting provider offers the same endpoint, context limit or price. A developer needs two records: which model artifact is intended and what the chosen service actually supports. This guide uses Meta’s model card and license, then provides a provider-contract checklist. It does not claim a live inference test or a GPU fit measurement. Scout and Maverick are different artifacts Meta’s Llama 4 model card records the release date as April 5, 2025 and describes: ...

The 2026 version of “which model has the largest context window” no longer has a single winner among the hosted APIs developers actually call, because those models now advertise the same ~1M-token class: OpenAI’s GPT-4.1 is documented at 1,047,576 tokens, Google’s Gemini 2.5 Pro at 1,048,576 tokens, and Anthropic’s Claude Opus 4.6 at 1M tokens. What still separates them is whether the long window is the default or a beta, how many tokens one response can generate inside that window, and how much of the window your document keeps once system prompts, tool definitions, tool results and the model’s own output are counted against it. Every figure below was read from the vendors’ own documentation on 2026-10-15, and the numbers this refresh could not confirm were withdrawn rather than restated. ...