> ## Documentation Index
> Fetch the complete documentation index at: https://docs.ai-stats.phaseo.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> Learn how to explore models, benchmarks, providers, and pricing data across the AI ecosystem.

The **AI Stats Platform** gives you a complete view of the global AI landscape - from the newest models and providers to performance benchmarks, pricing, and real world usage insights.

Whether you’re a developer choosing what model to use for your newest product, a researcher analysing benchmark results, or a contributor helping expand the database, this section will help you make the most of what AI Stats offers.

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## What you can do

<Columns cols={2}>
  <Card title="Discover Organisations" icon="building" href="./organisations.mdx">
    See what models are offered by organisations, as well as their latest
    releases and how they stack up.
  </Card>

  <Card title="Explore Models" icon="brain" href="./models.mdx">
    View detailed model pages with key information like capabilities,
    pricing, benchmark results, and real world performance metrics.
  </Card>

  <Card title="Compare Benchmarks" icon="chart-no-axes-column" href="./benchmarks.mdx">
    See how models perform across common benchmark datasets and tasks.
  </Card>

  <Card title="Analyse Pricing & Performance" icon="scale" href="./pricing-performance.mdx">
    Compare model costs and token efficiency to find the right balance for
    your use case.
  </Card>
</Columns>

***

## Data structure

AI Stats collects and organises data in several key categories:

| Category                                           | Description                                                                   |
| -------------------------------------------------- | ----------------------------------------------------------------------------- |
| **[Organisations](./organisations.mdx)**           | The companies or research labs that develop and publish models.               |
| **[Models](./models.mdx)**                         | The individual models that are released by the organisations.                 |
| **[Benchmarks](./benchmarks.mdx)**                 | The standardised tests that organisations use to evaluate model performance.  |
| **[API Providers](./api-providers.mdx)**           | The platforms that offer programmatic access to models.                       |
| **[Pricing](./pricing-performance.mdx)**           | Detailed cost breakdowns per model, measured per token, image, or generation. |
| **[Subscription Plans](./subscription-plans.mdx)** | Information on different pricing tiers and their included features or limits. |

Each of these categories is discussed in the documentation to help you navigate these pages and get the most out of the data.

***

## How AI Stats collects and updates data

AI Stats is powered by a vast, fully open database - built and maintained through a mix of automation and incredible community effort.
While we have automated systems that track updates across providers and models, human contribution is the back bone of the project and is what keeps it running smoothly.

When updates do occur:

* 🧩 New models are added swiftly and documented with as much detail as possible.
* 🔄 Provider changes - from pricing to endpoint tweaks - are continuously monitored and reviewed.
* 🧠 Community contributions are carefully reviewed and merged through GitHub pull requests, ensuring quality and transparency.

You can contribute corrections or additions yourself, check out the [Contributing guide](../contributing/overview.mdx) for details.
AI Stats is only as strong as the community behind it - and together, we’re building the most accurate and transparent record of AI progress in the world.

***

## Interpreting data on AI Stats

All data on AI Stats has been normalised for consistency:

* **Dates** should always be formatted as `DD MMM YYYY` (e.g. `01 Jan 2022`).
* **Prices** are displayed in USD by default, normalised per 1 million tokens where applicable or appropriate units.
* **Performance scores** are standardised to a common scale where possible.
* **Model families** group together related models (e.g., GPT-5, Claude 4, Gemini 2.5, etc.).

***

## Example use cases

Here are some examples of how you might use AI Stats:

| Goal                               | Example                                                          |
| ---------------------------------- | ---------------------------------------------------------------- |
| Compare two models                 | “Which performs better on GPQA - Claude 4.5 Sonnet or GPT-5?”    |
| Find the cheapest model for a task | “What’s the most cost-efficient model for summarisation?”        |
| Analyse benchmark trends           | “How have reasoning models improved on GPQA over the last year?” |
| Explore API access                 | “Which providers currently support JSON mode?”                   |
| Identify gaps for contribution     | “Which models are missing key benchmarks or metadata?”           |

***

## Next steps

<Columns cols={2}>
  <Card title="Explore Models" icon="brain" href="https://ai-stats.phaseo.app/models">
    Explore the latest models in the database and dive deeper into how each
    one performs.
  </Card>

  <Card title="Learn About Benchmarks" icon="chart-column-increasing" href="./benchmarks.mdx">
    Understand what benchmarks measure and how we can interpret results.
  </Card>
</Columns>

***