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The End of AI Monogamy: Let AI Find the Best Model for Your Task

#ai #llm #productivity

Most of us spend an insane amount of time using AI. Whether it’s coding, writing, or analyzing data, we are glued to our prompts.

But here is the problem: We are almost all "monogamous" with our AI.

You probably have a subscription to ChatGPT, or maybe Claude, or Gemini. You know deep down that other models exist. You know that for certain tasks, a specialized model like DeepSeek or Llama 3 might be faster, cheaper, or smarter.

But you don’t switch. Why?

It's not just the hassle of jumping into a new playground. It's that generic benchmarks rarely match reality.

We see leaderboards claiming a model is "#1 in Coding," but that is based on a standardized dataset. It doesn't tell you if the model is good at your specific legacy code, your unique tone of voice, or your particular data structure. A global average is meaningless when you have a specific problem.

This is inefficient. relying on a general-purpose winner for every single task is a compromise.

What if you didn't have to guess? What if your current AI assistant could run a "micro-benchmark" for you—using your actual prompt—right in the middle of your conversation?

The "Auto-Pilot" Benchmark

Imagine this workflow:

You are in your favorite IDE or chat interface, and you ask for a complex Python refactor. Instead of just guessing an answer, your AI agent pauses and thinks:

  1. "This is a coding task."
  2. "I need to check **LiveCodeBench* to see who is currently leading the leaderboard for Python."*
  3. "It looks like Model X and Model Y are outperforming everyone else right now."
  4. "I’m going to send this specific prompt to those models instantly and show the user the results side-by-side."

This isn't science fiction. This is possible right now with the Super AI Bench MCP Server.

You Don't Need to Be an Expert

The beauty of this approach is that you don't need to know the benchmarks.

You don't need to know that LiveCodeBench is for coding, or that GPQA is for deep reasoning, or what an ELO rating is for image generation. You don't need to know the specific names of the latest models.

You just do your work. The AI handles the logistics:

  • It analyzes your intent.
  • It searches real-time benchmark data.
  • It accesses a "Model Garden" (via Replicate).
  • It runs the test.

How It Works Under the Hood

By connecting an MCP (Model Context Protocol) client to live data sources, we bridge the gap between static leaderboards and active workflows.

  1. Context Awareness: The AI detects if you are doing creative writing, logic puzzles, or hardcore engineering.
  2. The Lookup: It queries the Super AI Bench tool to find the highest-performing models for that specific category.
  3. The Execution: It uses the Replicate API to spin up instances of those top models, feeds them your prompt, and aggregates the results.

You get 3 or 4 distinct answers from the smartest models on the planet, tailored exactly to the problem you are solving right now.

Disclaimer: The tools and workflows presented in this article provide a preliminary glimpse into the performance of various AI models, but these results should not be taken for granted. Automated comparisons are illustrative and may not reflect performance across all scenarios. To fully understand the specific strengths and weaknesses of candidate models, you must independently verify the results against your own data and requirements.

Getting Started

You might think this requires an expensive enterprise setup. It doesn't. You only need two things to start building this agentic workflow:

  1. Apify Account: This powers the benchmark scraping and data lookup. The free account gives you $5/month in credits, which is more than enough to test the platform for free.
  2. Replicate Account: This provides access to the models (Llama, Mistral, Kimi, Flux, etc.). It is pay-per-use, meaning no monthly subscription fees—you only pay for the seconds the models are actually generating text.

Step 1: Configure Your MCP Client

You can use Cursor, Claude Desktop, or any other AI interface that supports the Model Context Protocol (MCP). Just check your provider's manual for how to add an MCP server, and paste in the configuration from the README.

{
  "mcpServers": {
    "ai-live-benchmark": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://flamboyant-leaf--ai-livebenchmark-mcp.apify.actor/mcp",
        "--header",
        "Authorization: Bearer <APIFY_API_TOKEN>",
        "--header",
        "X-Replicate-API-Key: <REPLICATE_API_KEY>"
      ]
    }
  }
}
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Step 2: Run the Test

Now for the fun part. We don't need to specify which benchmark to use. We just give the AI a task. Let’s try a specific multilingual request:

"Find the 3 most powerful LLM models and run on Replicate to do this task: Write an email to my boss excusing being late in German."

Here is what happens next in real-time:

Phase A: The Smart Lookup

First, the AI analyzes your request. It realizes this is a text generation task involving a foreign language. It automatically decides to query the benchmark API for the current top-performing Large Language Models.

Phase B: Finding the Models

Next, it takes those top-ranked models and searches the Replicate "Model Garden" to see which ones are available for immediate access.

(Note: Sometimes a specific model version might not be hosted on Replicate. In that case, the agent is smart enough to just pick the next best model from the benchmark list—or you can simply ask it to "try the next one.")

Phase C: The Live Showdown

Finally, it runs the prediction. It doesn't just give you one answer; it executes the task on all three models in parallel.

Please note that sometimes Claude or another AI might guess the 'best' model by itself and start searching for it on Repclaiase. To avoid this, tell it explicitly to look up suitable benchmarks and let it search without outputting the result. This will give you a better understanding of what it is doing under the hood and what is suitable for your specific use cases.

Final thought

This isn't limited to emails or code. This workflow fully supports Image Models (Nano Banana, Qwen Image etc.) too. You can ask it to "Generate a cyberpunk city using the top 3 image models," and you will get a side-by-side comparison of Flux, Stable Diffusion, and others in one shot. And if you are using an interface like Claude Artifacts or Canvas, you can even ask the AI to build a simple HTML gallery to display these results side-by-side for a true "blind taste test." But that’s a topic for another post!

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