AI API Pricing Dataset: LLM prices as JSON, CSV and a free API

Current input, output, cached-input and batch prices for OpenAI, Anthropic Claude, Google Gemini, xAI Grok, DeepSeek, Mistral AI, Cohere, Moonshot AI (Kimi), Alibaba Qwen, Meta, Perplexity, Amazon Nova, Z.ai (Zhipu) and MiniMax. Every record links to the provider's own pricing page and carries the date it was last checked. Unknown prices stay null, never 0.

curl -L https://aicostbudget.github.io/ai-api-pricing-data/api/v1/prices.json

Compare every model side by side → · Estimate your monthly bill · See recent price changes

Prices at a glance

USD per 1M tokens, standard list price. 191 models tracked, last verified 2026-11-10.

Batch, cached-input, cache-write, long-context and non-token prices are in the full data.

Click a provider for its full price list, or a model for its JSON record. If this saves you a scrape, a ⭐ helps other developers find it. To hear about price changes, subscribe to the price-change Atom feed.

Related MCP server: Price Tracker AI MCP

Get the data

Documentation: dataset page · model schema · pricing contract · methodology · distribution projections

Code examples

Python

import json
import urllib.request

url = "https://aicostbudget.github.io/ai-api-pricing-data/api/v1/models/openai/gpt-4.1.json"
with urllib.request.urlopen(url) as response:
    model = json.load(response)

print(model["provider_id"], model["model_id"], model["pricing"]["input"])

JavaScript

const url = "https://aicostbudget.github.io/ai-api-pricing-data/api/v1/models/openai/gpt-4.1.json";

async function main() {
  const response = await fetch(url);
  const model = await response.json();

  console.log(model.provider_id, model.model_id, model.pricing.input);
}

main();

Interactive Tools

• Calculate your API cost

• Estimate the tokens in a prompt

• Compare AI model prices

• Track AI model price changes

• Analyze retry cost

• Compare model-switch cost

• Compare cached input prices by model

• Estimate prompt caching savings

• Find the cheapest LLM API for a use case

• Compare AI coding plans (Claude Code, Codex, Cursor, Copilot)

Pricing Beyond Chat Tokens

Some APIs bill per character, per image or per page instead of per chat token. These website pages compare them:

• Embedding API pricing (per 1M tokens)

• TTS API pricing (per 1M characters)

• Image generation API pricing (per image)

• OCR API pricing (per 1,000 pages)

Explore Pricing by Provider

Dataset Trust & Freshness

• Updated: The latest dataset build timestamp (generated_at) and aggregate record verification timestamp (last_verified_at) are published in machine-readable metadata and the static API.

• Verified: Each pricing record includes its own accessed_at and last_verified_at timestamps.

• Sources: Pricing records link to official provider pricing pages or documentation through official_source_url; see the methodology.

• Validation: Pushes and pull requests run dataset and schema checks, Hugging Face export parity, and tests; a weekly freshness check surfaces stale verification timestamps and source URL failures.

This repository publishes a versioned dataset and read-only static API for AI API pricing. It is designed for developers, SaaS builders, FinOps teams, researchers, technical writers, and AI systems that need maintained pricing records instead of ad hoc scraped snippets.

This project is maintained as an independent public dataset by AICostBudget.

Why this dataset?

• Source-linked pricing records

• Unknown or unverified values remain null, never 0

• Versioned JSON and CSV outputs

• Per-model history files

• Dated full snapshots

• Read-only static API

• Suitable for calculators, dashboards, cost analysis, and AI FinOps tooling

The weekly freshness workflow checks source URLs and stale last_verified_at values. It does not guess, infer, or overwrite prices automatically.

Use Cases

• Compare AI model prices

• Estimate monthly AI API cost

• Build LLM cost dashboards

• Plan SaaS AI feature budgets

• Track model pricing changes

Dataset Coverage

The dataset covers multiple AI providers and supports both token-based and non-token pricing structures.

Coverage includes:

• Standard input and output token pricing

• Cached input and cache-write pricing

• Batch and alternative processing modes

• Long-context, usage-tier, and conditional pricing

• Multimodal and non-token pricing

• TTS, speech, and transcription pricing

• Model lifecycle, provenance, and verification metadata

• Structured conditional pricing components

Fields

The CSV output uses the following fields:

API Reference

The static API is published through GitHub Pages:

• /api/v1/prices.json

• /api/v1/prices.csv

• /api/v1/meta.json

• /api/v1/providers/<provider>.json

• /api/v1/models/<provider>/<model>.json

Unknown or unverified prices are represented as null, never 0.

The CSV output includes normalized pricing fields for provider, model, pricing unit, source URL, verification dates, and notes.

Historical Pricing

To read the history instead of parsing it, see the LLM API price history page.

Per-model history files are stored under:

data/history/<provider>/<model>.jsonl

Dated full snapshots are stored under:

data/snapshots/<YYYY-MM-DD>/prices.json
data/snapshots/<YYYY-MM-DD>/prices.csv

The first pricing release of a UTC day uses the plain date. A later release on the same UTC day gets a sequence suffix (2026-10-21-2, 2026-10-21-3, …) so earlier snapshots are never rewritten. Snapshots order by date, then sequence; the date part is the event detected_at date.

Price changes between snapshots are recorded as events in data/price-change-events/events.jsonl; see price change events for the rules.

Older price changes from OpenAI, Anthropic, Google, DeepSeek, Mistral and xAI, going back to 2024, are in data/archive-history/price-changes-2024-2026.v1.json. Each one links the archived pricing page before and after the change; see archived price changes.

Cost per successful task

Token prices are only half of the bill: retries and fallbacks decide what one correct answer costs. examples/llm_cost_diagnostics.py splits first-attempt, retry and fallback spend from your own attempt logs, and the open LLM cost-per-task benchmark measures it on 16 tasks with your own API keys. Its first run found a flagship model matching a cheap one on accuracy at 5–9× the cost per successful task.

Methodology

Prices are accepted only from official provider pricing pages, official documentation, official APIs, or official announcements. Third-party calculators, SEO pages, Reddit posts, and competitor aggregators are not used as final price sources.

See METHODOLOGY.md.

Cite this dataset

For reproducible research, cite the frozen v1.1.0 release:

AICostBudget. AICostBudget AI API Pricing Dataset, v1.1.0 (snapshot 2026-09-30). https://doi.org/10.5281/zenodo.23087250.

• GitHub Release v1.1.0

• Dataset commit: 6882e95a8cdf5fb39b75b47bcd9ba0bff63b13d8

• Data license: CC BY 4.0

The DOI identifies this fixed release, not the latest data. CITATION.cff provides its machine-readable citation metadata. The canonical live dataset continues to update; for current operational use, cite that URL with your access date. The export's generated_at and last_verified_at timestamps are separate from the access date. See the release process for projection and checksum rules.

Contributing

Contributions are welcome when they include official sources and preserve null for unknown values. See CONTRIBUTING.md.

License

Code is licensed under MIT in LICENSE-CODE. Data is licensed under Creative Commons Attribution 4.0 in LICENSE-DATA.

Disclaimer

AI API prices change frequently. Always verify official provider pricing pages before making production budget decisions.

This dataset is informational and may lag provider pricing changes. Provider names and trademarks belong to their respective owners. This project is not affiliated with, endorsed by, or sponsored by any listed provider.