Context Length Requirement Model Finder

Find the right model for your context window

Picking an LLM starts with one hard constraint: does your prompt fit? This finder takes the minimum context window your task needs, filters every model down to the ones that support it, then sorts the survivors by price so you can see the cheapest model that can actually hold your data.

How context windows work

The context window is the total number of tokens a model can take in a single request — system prompt, retrieved documents, chat history, and the generated output all count against it. If the sum exceeds the window, the API rejects the request or truncates it, dropping information silently.

To size your requirement, add up the largest realistic version of each part:

required = system_prompt + documents + history + expected_output + buffer

Always leave a buffer (10–20%) so an unusually long input does not blow the limit in production.

Tips for choosing

• Smallest window that fits, with headroom is almost always the best value — bigger windows cost more and recall degrades when you stuff them full.
• Output is the expensive half. A model with a huge window but pricey output tokens can cost more than a mid-size model for an output-heavy workload.
• Chunk and retrieve instead of paying for a giant window when only a small, relevant slice of your documents matters per request.