# Artificial Analysis Top Lists (`truenorth/artificial-analysis-top-lists`) Actor

Extract AI model rankings by intelligence, output speed, and cost per task from Artificial Analysis.

- **URL**: https://apify.com/truenorth/artificial-analysis-top-lists.md
- **Developed by:** [TrueNorth](https://apify.com/truenorth) (community)
- **Categories:** Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$2.00 / 1,000 model 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/platform/actors/running/actors-in-store#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

## Artificial Analysis Top Lists

Extract ranked AI model and coding-agent records from public Artificial Analysis leaderboards. Eight independently ranked lists cover intelligence, output speed, task cost, intelligence-to-cost value, balanced intelligence/speed, Coding Agent Index, coding-agent value, and coding-agent completion time.

Model-only runs fetch one model page. Coding-agent runs fetch the coding-agent page only when at least one coding-agent list is requested. Mixed runs fetch each page once and give all records one run timestamp.

### Lists

| ID | Metric | Order | Eligibility |
| --- | --- | --- | --- |
| `intelligence` | Intelligence Index | Highest first | Finite intelligence |
| `speed` | Median output tokens/second | Highest first | Finite speed |
| `costPerTask` | Intelligence Index cost/task, USD | Lowest first | Finite cost, including zero |
| `intelligenceCostRatio` | Intelligence Index / cost/task | Highest first | Finite intelligence and cost > 0 |
| `intelligenceSpeed` | Geometric mean of intelligence and speed percentile ranks | Highest first | Finite intelligence and speed > 0 |
| `codingAgentIndex` | Coding Agent Index × 100 | Highest first | Finite index |
| `codingAgentValue` | Coding Agent Index points / mean cost/task | Highest first | Finite index and cost > 0 |
| `codingAgentSpeed` | Mean agent wall time/task, seconds | Lowest first | Finite time >= 0 |

`intelligenceSpeed` ranks eligible models independently on intelligence and output speed. Each rank is converted to 0–100 using `(number below + half of tied peers) / (eligible count - 1) × 100`; tied values receive the same midpoint rank. A one-model population receives 100. Final score is `sqrt(intelligencePercentile × speedPercentile)`. Calculations and sorting use unrounded source values.

Primary metric ties use deterministic display-name/identity ordering. `maxItems` applies after filtering and sorting, independently per list.

### Input

```json
{
  "lists": ["intelligenceCostRatio", "intelligenceSpeed", "codingAgentIndex", "codingAgentValue", "codingAgentSpeed"],
  "maxItems": 10,
  "creatorFilter": "Anthropic",
  "modelFilter": "Claude",
  "agentFilter": "Claude Code",
  "includeDeprecated": false
}
```

| Field | Type | Default | Meaning |
| --- | --- | --- | --- |
| `lists` | array | `intelligence`, `speed`, `costPerTask` | One to eight unique list IDs. Input order controls output blocks. |
| `maxItems` | integer | `10` | Records per list, 1–100. |
| `creatorFilter` | string | omitted | Creator substring; applies to both source types. |
| `modelFilter` | string | omitted | Model name/slug substring; applies only to model lists. |
| `agentFilter` | string | omitted | Agent name/display label/provider substring; applies only to coding-agent lists. |
| `includeDeprecated` | boolean | `false` | Include deprecated models; irrelevant to coding-agent lists. |

Filters are trimmed, NFKC-normalized, case-insensitive literal substrings. Relevant filters compose with AND semantics. Empty filters are omitted.

### Output

Every dataset item is flat. Existing `intelligence`, `speed`, and `costPerTask` model records retain their prior fields and metric behavior. New model-list records use the same model shape with new `metricName`, `metricValue`, and `metricUnit` metadata.

Coding-agent example:

```json
{
  "list": "codingAgentValue",
  "rank": 1,
  "metricName": "codingAgentValue",
  "metricValue": 60,
  "metricUnit": "indexPointsPerUSD",
  "sortDirection": "descending",
  "sourceType": "codingAgent",
  "agentId": "agent-run-id",
  "agentName": "Claude Code",
  "agentDisplayName": "Claude Code - Sonnet",
  "provider": "anthropic",
  "hostModelSlug": "anthropic_claude-sonnet",
  "hostModelName": "Sonnet",
  "agentCreator": "Anthropic",
  "modelCreator": "Anthropic",
  "codingAgentIndex": 60,
  "costPerTaskUsd": 1,
  "timePerTaskSeconds": 100,
  "benchmarkCount": 3,
  "benchmarkNames": "DeepSWE, SWE-Atlas-QnA, Terminal-Bench v2",
  "benchmarkRowsJson": "[{...}]",
  "sourceUrl": "https://artificialanalysis.ai/agents/coding-agents",
  "fetchedAt": "2026-07-22T13:49:59.964Z"
}
```

`benchmarkRowsJson` contains the complete normalized benchmark breakdown as a JSON string, keeping dataset columns flat and CSV-safe. Parser resolves inline and referenced Next Flight rows, including forward/path references split across chunks. It rejects highlights-only data, unresolved/cyclic/conflicting references, duplicate identities, incomplete benchmark sets, inconsistent weighted aggregates, and payloads below sanity floors.

Default key-value store record `OUTPUT` reports parsed, filtered, generated, and pushed counts. Each pushed dataset record uses existing `model-result` Pay-Per-Event event. Charge-limit handling stops before counting rejected record.

### Data behavior

- Direct public SSR requests; no browser, login, private API, proxy, or pagination.
- Semantic model table remains fallback for current models where selected fields exist.
- Deprecated requests and metrics unavailable in fallback fail instead of returning partial data.
- Valid filters with no matches return empty output; unfiltered metric loss fails as schema drift.
- Results represent current source snapshots, not historical data.
- Independent Actor; not affiliated with or endorsed by Artificial Analysis.

# Actor input Schema

## `lists` (type: `array`):

Lists to produce, in output block order.

## `maxItems` (type: `integer`):

Maximum number of records emitted independently for each selected list.

## `creatorFilter` (type: `string`):

Case-insensitive literal substring matched against creator name and slug.

## `modelFilter` (type: `string`):

Case-insensitive literal substring matched against model names and slug.

## `agentFilter` (type: `string`):

Case-insensitive literal substring matched against coding agent name, display label, and provider. Applied only to coding-agent lists.

## `includeDeprecated` (type: `boolean`):

Include deprecated models when ranking rich SSR payload data.

## Actor input object example

```json
{
  "lists": [
    "intelligence",
    "speed",
    "costPerTask"
  ],
  "maxItems": 10,
  "includeDeprecated": false
}
```

# Actor output Schema

## `dataset` (type: `string`):

No description

## `summary` (type: `string`):

No description

# 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("truenorth/artificial-analysis-top-lists").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("truenorth/artificial-analysis-top-lists").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).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 truenorth/artificial-analysis-top-lists --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=truenorth/artificial-analysis-top-lists",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/d76Nx4mBKV0SKV051/builds/JIevLdur6UJQERERj/openapi.json