tokencanopy/price

LLM Price Index

A public, machine-readable record of what LLM inference actually costs — and how that price changes over time. Refreshed every 6 hours.

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13,389 price points · 886 models · 73 platforms · updated 2026-09-03 12:40 UTC · history since 2026-09-02 (2 snapshots)

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Every provider publishes today's price. Nobody publishes yesterday's. This repo fixes that by writing the number down every 6 hours, in git, forever.

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Same model, very different price

<picture> <source media="(prefers-color-scheme: dark)" srcset="charts/dispersion-dark.png"> <img alt="Input price per provider as a multiple of the cheapest provider, for the most widely-served open models" src="charts/dispersion-light.png"> </picture>

Open-weight models are served by dozens of platforms at wildly different prices for what is nominally the same set of weights. Across the 44 open models served by 5 or more platforms, the median spread is 2.0× and 48% of models span more than 2×.

The obvious explanation — cheap endpoints are quantized harder — does not hold. Of those 44 models, the cheapest endpoint runs at full BF16 precision in 20 cases, more often than FP8 (14) or FP4 (10). The most expensive endpoint is the BF16 one in 31 of 44. What you are mostly paying for is hardware, margin and throughput: the priciest gpt-oss-120b endpoint is Cerebras at 11.7× the cheapest, and it is selling speed, not precision. The chart marks quantization by shape and colour so you can check this yourself.

What it costs to run each model today

<picture> <source media="(prefers-color-scheme: dark)" srcset="charts/cheapest-dark.png"> <img alt="Input and output price per 1M tokens at the cheapest platform for each popular open model" src="charts/cheapest-light.png"> </picture> <!-- BEGIN TABLE -->

| Model | Platforms | Cheapest | Input $/M | Output $/M | Spread | |---|--:|---|--:|--:|--:| | DeepSeek: DeepSeek V4 Flash 0