Context-Window

Every six months, someone declares RAG dead. The argument is always the same: “Now that GPT-4.1 has 1M tokens and Gemini 2.5 Pro handles 2M, why bother with retrieval? Just dump everything into context.” I’ve been building production RAG systems since the LlamaIndex 0.5 days, and I can tell you: bigger context windows didn’t make RAG obsolete. They made the problem more interesting — and harder to get wrong. Here’s what the 2026 data actually shows, and what techniques deliver real results when you’re building a retrieval system that needs to work in production. ...

Llama 4 Scout is Meta’s open-weight model with a 10 million token context window — the largest of any open-weight model released in 2026. At roughly 4 tokens per line of code, that covers approximately 2.5 million lines of code in a single prompt. In practice this means you can load an entire mid-size production repository — including tests, docs, and config — without chunking, vector databases, or retrieval pipelines. ...

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below was read from the vendors’ own documentation on 2026-10-15, and the numbers this refresh could not confirm were withdrawn rather than restated. ...