Asking Six Models the Same Question on One Key: Comparing Answers Side by Side

Why Compare Multiple Models?

Different LLMs excel at different tasks. Claude might produce nuanced creative writing, GPT might handle complex reasoning, DeepSeek might shine in code generation, Qwen in multilingual tasks, GLM in Chinese contexts, and Kimi in long-context summarization. By asking the same question to several models, you can triangulate the best answer, reduce model-specific biases, and gain confidence in critical decisions.

Single Key, Multiple Models

With an aggregator that supports multiple providers, you use one API key to call any model. This simplifies authentication and billing. Top up with USDC on Base (no KYC), and pay official price × 1.3 per call. If you contribute keys, you earn credits at official price × 1.1 (premium × 1.2) in USDC. The focus here is on the technical workflow of comparing outputs.

Fan-Out: Sending One Prompt to Many Models

Fan-out means dispatching the same prompt to multiple models in parallel. Use asynchronous requests to minimize latency. For example, with Python and aiohttp, you can create tasks for each model and gather results. Ensure your code handles rate limits gracefully—though exact limits vary by provider, implement retries with exponential backoff.

import aiohttp, asyncio

async def query_model(session, model, prompt):
    async with session.post('https://api.aggregator.com/v1/chat/completions',
                            json={'model': model, 'messages': [{'role': 'user', 'content': prompt}]},
                            headers={'Authorization': 'Bearer YOUR_KEY'}) as resp:
        return await resp.json()

async def fan_out(prompt, models):
    async with aiohttp.ClientSession() as session:
        tasks = [query_model(session, m, prompt) for m in models]
        return await asyncio.gather(*tasks)

Normalizing Outputs

Models return responses in slightly different JSON structures. Normalize by extracting the text content and any metadata (e.g., finish reason, token usage). Create a standard schema:

{
  "model": "claude-3-opus",
  "response": "...",
  "usage": {"prompt_tokens": 10, "completion_tokens": 50},
  "latency_ms": 1200
}

Handle errors uniformly: treat timeouts, API errors, and empty responses as null results with error tags.

Evaluating and Trusting Answers

No single model is always right. Use these strategies to decide which answer to trust:

• Consensus voting: If multiple models agree on a factual answer, confidence increases. For open-ended questions, look for semantic similarity.
• Cross-checking: Use one model to critique another's answer. For example, ask GPT to evaluate Claude's response for accuracy.
• Task-specific weighting: Assign higher trust to models known for strength in the domain (e.g., code: DeepSeek; creative: Claude).
• Human review: For high-stakes decisions, always include a human in the loop.

Practical Tips

• Keep prompts identical across models to ensure fair comparison.
• Log all responses with model identifiers for later analysis.
• Use temperature settings consistently; lower temperatures yield more deterministic outputs.
• Be aware of context window limits—truncate long inputs if needed.
• Monitor costs: each call is billed at official price × 1.3, so fan-out multiplies expenses.

Conclusion

Fan-out to multiple models behind a single key is a powerful technique for improving answer reliability. By normalizing outputs and applying structured evaluation, you can make informed decisions about which response to trust. Start with a small set of models relevant to your task, and expand as needed.