Open-Weight vs Proprietary
Whether a model’s weights are published (so any host can serve it) or kept private and sold only by the vendor.
The open-weight vs proprietary distinction is about who can serve a model. Proprietary models — Claude, GPT, Gemini — are sold only by the vendor that trained them, so there is one authoritative price per model. Open-weight models — Llama, DeepSeek, Qwen — have published weights, so many hosts can run them and each sets its own price, and you can even self-host.
For pricing, this matters in two ways. First, an open-weight model has no single price: the figure you see depends on which host you pick. Meta, for example, does not sell Llama tokens at all, so this explorer shows Together AI’s serverless rate and labels the host explicitly — a different host may charge more or less. Second, open-weight models are often dramatically cheaper per token because hosts compete on price, though you trade away the vendor’s managed tuning, safety, and support.
This explorer labels third-party-hosted prices with a host badge so you always know whose rate you are looking at, and never presents one host’s price as the model’s only price. When evaluating an open-weight model, compare hosts, check throughput and reliability alongside the token rate, and factor in the engineering cost of self-hosting if you go that route. The cheapest sticker is not always the cheapest total.
FAQ
Why does an open-weight model have different prices?
Because its weights are public, many hosts can serve it and each sets its own rate. The price you see depends on the host — this explorer labels which one.
Are open-weight models always cheaper?
Often cheaper per token, since hosts compete on price, but you trade the vendor’s managed tuning and support. Weigh throughput, reliability, and self-hosting effort alongside the rate.