# AI Models & LLM Benchmark Tracker (`papa_developers/ai-model-benchmarks-scraper`) Actor

Scrapes and consolidates live LLM leaderboard benchmarks, Arena Elo ratings, MMLU, and HumanEval scores into structured JSON/CSV datasets.

- **URL**: https://apify.com/papa\_developers/ai-model-benchmarks-scraper.md
- **Developed by:** [Hunny](https://apify.com/papa_developers) (community)
- **Categories:**
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $5.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## AI Models & LLM Benchmark Tracker

**Track, compare, and extract frontier & open-source LLM benchmark metrics across LMSYS Chatbot Arena, Open LLM Leaderboard, and coding benchmarks in structured JSON/CSV format.**

The **AI Models & LLM Benchmark Tracker** provides standardized intelligence on artificial intelligence models, including Arena Elo ratings, MMLU general reasoning accuracy, HumanEval/coding benchmarks, MATH problem solving, license types, context windows, and parameter sizes.

***

### ⚡ What Does This Actor Do?

- **Consolidated Leaderboard Intelligence:** Aggregates metrics from leading evaluation sources into a unified, queryable dataset.
- **Granular Filtering:** Filter models by minimum Arena Elo rating (e.g. models rated 1200+ Elo), license type (`open-source` vs `proprietary`), and specific benchmark suites.
- **Pay-per-Event Pricing:** Pay only for results produced ($0.002 per model record), with $0 startup fees.
- **AI Agent & MCP Native:** Fully compatible with Model Context Protocol (MCP) clients, Cursor, Claude Code, and autonomous AI data agents.
- **Multiple Export Formats:** Download data instantly as **JSON**, **CSV**, **Excel**, **XML**, or access programmatically via REST API.

***

### 🛠️ Input Configuration

| Field | Type | Default | Description |
|---|---|---|---|
| `benchmarks` | Array | `["arena-elo", "open-llm", "coding-benchmark"]` | Select evaluation suites to track. |
| `max_models` | Integer | `50` | Maximum number of models to return (1 - 200). |
| `min_arena_elo` | Integer | `1100` | Minimum Chatbot Arena Elo threshold. |
| `license_filter` | String | `"all"` | Filter by license: `all`, `open-source`, or `proprietary`. |

***

### 📊 Sample Output

```json
{
  "model_name": "Claude 3.5 Sonnet",
  "organization": "Anthropic",
  "arena_elo": 1285,
  "coding_score": 93.7,
  "mmlu_score": 88.7,
  "math_score": 78.3,
  "license": "proprietary",
  "parameters_billion": null,
  "context_window": 200000,
  "data_source": "LMSYS Chatbot Arena / Open LLM Leaderboard Consolidation",
  "scraped_at": "2026-09-04T02:00:00Z"
}
```

***

### 💰 Pricing

This Actor utilizes Apify's **Pay-Per-Event (PPE)** pricing:

- **Startup Cost:** **$0.00**
- **Per Result:** **$0.002 USD** ($2.00 per 1,000 model benchmark rows)
- **Apify Store Discounts:** Enabled for paying platform tiers (Personal, Team, Enterprise).

***

### 🚀 How to Run via API or CLI

#### Python

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("papa_developers/ai-model-benchmarks-scraper").call(run_input={
    "max_models": 25,
    "min_arena_elo": 1200,
    "license_filter": "open-source"
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["model_name"], item["arena_elo"])
```

#### Apify CLI

```bash
apify call papa_developers/ai-model-benchmarks-scraper -i '{"min_arena_elo": 1200}'
```

# Actor input Schema

## `benchmarks` (type: `array`):

Select which benchmark sources and leaderboards to track.

## `max_models` (type: `integer`):

Limit the number of top models to output.

## `min_arena_elo` (type: `integer`):

Filter models having at least this Arena Elo score.

## `license_filter` (type: `string`):

Filter models by license (e.g. 'open-source', 'commercial', or 'all').

## Actor input object example

```json
{
  "benchmarks": [
    "arena-elo",
    "open-llm",
    "coding-benchmark"
  ],
  "max_models": 50,
  "min_arena_elo": 1100,
  "license_filter": "all"
}
```

# Actor output Schema

## `results` (type: `string`):

Consolidated AI model benchmark profiles stored in default dataset

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("papa_developers/ai-model-benchmarks-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("papa_developers/ai-model-benchmarks-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call papa_developers/ai-model-benchmarks-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,papa_developers/ai-model-benchmarks-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/WumLNwU9baeXdc6Ai/builds/XOAB1pgP1b43Ph3Kr/openapi.json