Lately I’ve been experimenting with different AI models for tasks like summarization and sentiment analysis. The challenge is that you never know which model will give the best output for a given input—sometimes GPT-4 nails it, other times Claude is better, and sometimes a smaller model is both fast and sufficient. I tried writing scripts to call multiple APIs in parallel, but juggling different keys and handling timeouts is a pain. I’m curious if anyone has found a way to run several LLMs at once in the same automation, compare their outputs, and automatically pick the best one based on metrics like score, cost, or latency. How are you handling this in practice?
Latenode lets you call multiple LLMs in parallel in the same workflow. You can score each output based on your criteria, like quality or speed. Then you pick the one you like best. No need to mess with many API keys. Everything is under one subscription. https://latenode.com
I’ve done this manually by running models in parallel and writing code to compare results. It works, but it’s tedious. I’d love a tool that automates the scoring and selection based on my rules, so I don’t have to glue scripts together.
One thing to watch out for is cost. Running multiple big models in parallel can get expensive fast. Having a system that tracks cost per run and lets you set limits would be really useful.
In my experience, getting consistent scoring for model outputs is tricky. I’ve used simple heuristics, like response time or number of tokens, but sometimes you need custom logic—maybe sentiment score or adherence to a style guide. I’ve looked for platforms that support this kind of flexible evaluation, but most require extra coding. It would be great to have a tool where you can just plug in your criteria and let the system handle the rest. Handling errors and rate limits across multiple providers is another headache.
Running multiple LLMs in parallel is a powerful approach, especially for critical tasks where you care about output quality. The main challenges are orchestrating the calls, normalizing the responses, and applying your evaluation logic. Platforms that offer unified access to many models and let you define custom selection rules save a lot of effort. Also, monitoring usage and costs across providers is easier when everything is centralized.
parallel llms, score outputs, pick best, track cost.