I run a site that reviews AI tools for small business owners. One question keeps coming up: "How much does this actually cost?"
The problem is AI model pricing changes constantly. OpenAI drops prices, Google launches new tiers, open-source models get cheaper on hosted APIs. There's no single place that tracks these changes month over month in a structured way.
So I built one.
What it is
An open dataset tracking pricing for 22 AI/LLM models across four categories:
- Frontier (GPT-4o, Claude Sonnet 4, Gemini 2.5 Pro)
- Efficiency (GPT-4o Mini, Gemini 2.0 Flash, Mistral Small)
- Reasoning (o3 Mini, DeepSeek R1, Gemini 2.5 Flash Thinking)
- Open Source (Llama 4 Scout, Llama 3.3 70B, Qwen)
Every model includes price per 1M tokens (prompt, completion, and blended), context window size, and provider info.
Two composite indices
AI CPI (Cost Pressure Index): Weighted average cost across all tracked models. Shows whether the market is getting cheaper or more expensive overall.
Budget Index: Ratio of efficiency-tier pricing to frontier-tier pricing. The lower this number, the bigger the savings you get from choosing a smaller model.
How it works
Data pulls from the OpenRouter API on the 1st of each month. OpenRouter aggregates pricing across providers, so it gives a standardized view of what each model actually costs through a single API.
The dataset auto-updates monthly via cron. Historical snapshots are preserved so you can track trends over time.
The data
Available as JSON and CSV:
-
data/pricing_current.json— latest month -
data/pricing_history.json— all historical snapshots -
data/models.csv— spreadsheet-friendly format
Repo: github.com/AIscending/llm-pricing-index
Free to use with attribution. Only one month of data so far (April 2026), but it'll compound.
Why I built this
I was writing pricing breakdowns for my site and realized I was manually checking the same 20+ models every month. Automating the data collection was the obvious move. Publishing it openly seemed like the right thing to do — if I need this data, other people probably do too.
If you have suggestions for models to add or data points to track, I'm all ears.
I write practical AI guides at AIscending.com — built for people running small businesses, not ML engineers. But this dataset is for everyone.