# Compare GPT, Claude and Gemini side by side

**Use case:** 

Sends the same prompts to GPT-5, Claude Sonnet 5, Gemini 3.8 Flash and Perplexity Sonar, then returns one row per model per prompt with the answer, the cost and the token count. Use it to pick a model on evidence from your own prompts instead of a benchmark table, before you commit to one in production.

## Input

```json
{
  "prompts": [
    "Explain what a vector database is to a non technical product manager, in under 120 words.",
    "Rewrite this support reply to be warmer without adding promises: 'Your refund was denied because the return window closed.'",
    "A SaaS company has 4% monthly churn and 8% monthly new customer growth. Explain in plain language whether it is growing, and what breaks first."
  ],
  "system_prompt": "You are a helpful, accurate assistant. Answer directly and concisely.",
  "model": "openai/gpt-5",
  "enable_web_search": false,
  "enable_file_parser": false,
  "temperature": 40,
  "max_tokens": 1200,
  "compare_models": [
    "openai/gpt-5",
    "anthropic/claude-sonnet-5",
    "google/gemini-3.8-flash",
    "perplexity/sonar"
  ],
  "response_format": "text",
  "reasoning_effort": "default",
  "provider_sort": "default",
  "max_retries": 2,
  "concurrency": 4
}
```

## Output

```json
{
  "prompt": {
    "label": "Prompt",
    "format": "string"
  },
  "model": {
    "label": "Model",
    "format": "string"
  },
  "response": {
    "label": "Response",
    "format": "string"
  },
  "tools_used": {
    "label": "Tools used",
    "format": "array"
  },
  "cost_usd": {
    "label": "Cost (USD)",
    "format": "number"
  },
  "usage": {
    "label": "Tokens",
    "format": "object"
  },
  "error": {
    "label": "Error",
    "format": "string"
  }
}
```

## About this Actor

This example demonstrates how to use [Bulk LLM Runner GPT, Claude, Perplexity, Kimi (No API Key)](https://apify.com/fayoussef/bulk-llm-runner.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/fayoussef/bulk-llm-runner.md) to learn more, explore other use cases, and run it yourself.


## 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.
This Task's input is already configured above — use it as-is rather than inventing a new one.

- **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 full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/fayoussef/bulk-llm-runner.md

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).