# Llm Model Pricing Context Window Database (`kawsar/llm-model-pricing-context-window-database`) Actor

Collect LLM model pricing and context window data from LiteLLM into a clean dataset. Export results to JSON, CSV, or Excel with no login required.

- **URL**: https://apify.com/kawsar/llm-model-pricing-context-window-database.md
- **Developed by:** [Kawsar](https://apify.com/kawsar) (community)
- **Security:** Limited permissions
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.99 / 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.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## LLM Model Pricing Context Window Database

![LLM Model Pricing Context Window Database cover](https://images.bigibyte.com/cdn-cgi/image/width=1260,height=540,fit=cover,format=webp/apify-actors-covers/llm-model-pricing-context-window-database-cover.png)

Gather large language model (LLM) pricing structures, context window limits, and provider details into a clean, structured dataset. Easily organize models by provider, mode, or input limits, then export your data to JSON, CSV, or Excel formats effortlessly.

### What it does

This actor aggregates model specifications into tabular rows that you can filter, schedule, and export. Use it to compare model providers, keep track of context limits, and maintain an organized dataset for cost planning without having to manual-check each model provider.

### Modes

- **All models:** Collects the complete catalog up to your set item limit.
- **Filter by provider/mode:** Keeps only entries matching specific providers (e.g., OpenAI, Bedrock) or modes like chat or image generation.

### Input example

```json
{
  "mode": "filter",
  "providers": ["openai"],
  "modeFilter": "chat",
  "maxItems": 50,
  "requestDelaySecs": 1,
  "userAgent": "ActorResearch/1.0",
  "timeoutSecs": 300,
  "requestTimeoutSecs": 30
}

```

### Output example

```json
{
  "model": "1024-x-1024/50-steps/bedrock/amazon.nova-canvas-v1:0",
  "deprecation_date": "2026-09-30",
  "litellm_provider": "bedrock",
  "max_input_tokens": 2600,
  "mode": "image_generation",
  "modelMode": "image_generation",
  "output_cost_per_image": 0.06,
  "scraped_at": "2026-10-26T00:00:00+00:00"
}

```

The output overview table provides clear details on model name, deprecation status, provider, maximum input tokens, operational mode, image/token cost parameters, and extraction timestamp.

### Practical use cases

- **Pricing research:** Build spreadsheets comparing model costs and context limits to budget software infrastructure.
- **Provider comparison:** Evaluate offerings from OpenAI, Bedrock, Anthropic, or others side-by-side.
- **Product dashboards:** Feed structured data into internal tools and software dashboards.
- **Cost audits:** Save dated snapshots of model metadata for historical tracking and financial reviews.
- **Data workflows:** Export structured datasets to JSON, CSV, or Excel for analysis.

### Frequently asked questions

**Who is this tool for?**

Developers, researchers, and product teams who want LLM pricing and context window metrics organized in one readable format.

**What export formats are supported?**

You can export data directly to JSON, CSV, Excel, and other tabular formats supported by Apify datasets.

**Can I automate runs?**

Yes, you can schedule periodic runs using Apify schedules to keep your dataset updated over time.

### Trademark Notice

All product names, logos, brands, and trademarks belong to their respective owners. Their reference here is solely for identification and descriptive purposes, and does not imply endorsement, affiliation, or sponsorship.

### Disclaimer

This Actor is an independent collection tool. Users are responsible for ensuring their usage complies with relevant terms, rate limits, and guidelines of target services.

### Is web scraping legal?

This actor extracts publicly accessible data without bypassing login screens, paywalls, or technical security layers. While collecting public web data is widely permitted, compliance depends on how data is gathered and used:

- Copyrighted content: Public text, images, and visual assets are often protected by intellectual property rights. Avoid directly duplicating or re-distributing full creative works.
- Platform terms & rate limits: Practice responsible data extraction by avoiding aggressive traffic spikes, adhering to rate limits, and respecting published platform policies.
- Disclaimer: This summary serves informational purposes only and does not constitute formal legal counsel. Seek professional legal advice for specific compliance queries.

This is not legal advice. For the longer explanation, see Apify's article [Is web scraping legal?](https://blog.apify.com/is-web-scraping-legal/).

# Actor input Schema

## `mode` (type: `string`):

What to collect. All models flattens every entry. Filter keeps matching providers and optional mode values.

## `providers` (type: `array`):

litellm_provider values to keep when mode is filter. Leave empty to keep all providers.

## `modeFilter` (type: `string`):

Optional model mode to keep when mode is filter, for example chat, embedding, or image_generation. Leave empty to keep all modes.

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

Stop after this many successful model rows. Hard max is 1000.

## `requestDelaySecs` (type: `number`):

Seconds to wait before the request for polite pacing.

## `userAgent` (type: `string`):

User-Agent header sent with each request.

## `timeoutSecs` (type: `integer`):

Overall run budget in seconds.

## `requestTimeoutSecs` (type: `integer`):

Per-request timeout in seconds.

## `proxyConfiguration` (type: `object`):

Optional proxy. Leave off unless you need it.

## Actor input object example

```json
{
  "mode": "all",
  "providers": [
    "openai"
  ],
  "modeFilter": "",
  "maxItems": 100,
  "requestDelaySecs": 1,
  "userAgent": "ActorResearch/1.0",
  "timeoutSecs": 300,
  "requestTimeoutSecs": 30,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# Actor output Schema

## `overview` (type: `string`):

Dataset rows in the overview table.

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

Full dataset for this run.

## `items` (type: `string`):

Dataset items as JSON.

# 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 = {
    "providers": [
        "openai"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("kawsar/llm-model-pricing-context-window-database").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 = { "providers": ["openai"] }

# Run the Actor and wait for it to finish
run = client.actor("kawsar/llm-model-pricing-context-window-database").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 '{
  "providers": [
    "openai"
  ]
}' |
apify call kawsar/llm-model-pricing-context-window-database --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,kawsar/llm-model-pricing-context-window-database"
        }
    }
}
```

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/b4Dq1CLDHQTIFPcqc/builds/scKXB17nseh4xOjmT/openapi.json