AllocateOpen-weight models with the longest context windows
The longest-context open-weight models on the catalog reach 1M tokens: Llama Guard 4 12B leads at $0.20 per million input tokens. A 1M-token window holds roughly 750,000 words, an entire policy library or codebase in one prompt, on weights you can fine-tune and own.
Provider list prices from the Allocate catalog, checked 2026-07-21.
What long context is worth
Long context replaces retrieval plumbing for bounded corpora: instead of chunking and fetching fragments, the model reads the whole source. The tradeoff is per-request cost, which grows with the tokens actually processed, and memory on the serving side, where the KV cache grows linearly with context.
Open weights change the economics of long-document fine-tuning too: training examples that are whole documents need a window that holds them, which this list ranks directly.
Common questions
What is the longest context window on an open model?
1M tokens, on Llama Guard 4 12B, at $0.20 per million input tokens at list.
Does long context cost more?
Per token, no: you pay the same list price per million tokens. Per request, yes, because you send more tokens. A full 1M-token prompt on a $0.18 model costs about $0.18 at list before caching.
Related
Every model here sits behind one key on Allocate: route by name, meter per route, and swap the model in one click.