We Built a Tool That Runs ChatGPT, Claude, Gemini and Grok Side by Side—and Flags Where They Disagree

Melissa Özbilek — Sun, 17 May 2026 09:06:27 +0000

The 2025 Multi-Model Problem

If you're a heavy LLM user, you've probably done this dance:

Ask ChatGPT a question.
Get an answer that sounds confident.
Re-paste the same question into Claude just to check.
Open Gemini for the "current information" angle.
Realize 20 minutes have passed and you're not sure who to trust.

Different models are good at different things — Claude tends to win on code refactoring and nuanced reasoning, Gemini and Grok have stronger real-time grounding, and ChatGPT remains the all-rounder for summarization. The catch: you don't know which model is right for this specific question until you ask all of them.

So our team built MultipleChat — and I want to share why and how it works, because the idea is more interesting than the "we made a wrapper" framing makes it sound.

Disclosure: I'm on the team that built this. I'll keep the post technical and focused on the multi-model question itself — the product is a side effect of the problem.

App: https://multiplechat.ai
Site: https://multiple.chat

Three modes for using multiple models

1. Solo

Use any of the four models individually. Useful as a cost-saving move — one subscription instead of ChatGPT Plus + Claude Pro + Gemini Advanced separately ($60+/month → one bill).

2. Side-by-Side

Same prompt fans out to ChatGPT, Claude, Gemini, and Grok in parallel. Responses render in a grid. This is the most-used mode in our internal data — people open it specifically when the stakes for being wrong are high (architecture decisions, legal-ish questions, medical-adjacent stuff).

3. Collaborate

This is the one I find most interesting. You design a chain:

ChatGPT (draft) → Claude (critique) → Gemini (refresh with current data) → Final synthesis

Each model passes its output to the next. The chain is configurable per-prompt, no code. Useful when you want one model's strength to compensate for another's weakness on the same task.

The Disagreement Detector

The feature I'm proudest of is also the simplest: after all four models answer, we run a quick semantic comparison and highlight only the spans where they disagree.

Concrete example. I asked all four:

Is it safe to use PostgreSQL 16 logical replication in production?

Model	Position	Caveat raised
ChatGPT	Generally safe	Watch replication lag
Claude	Conditionally safe	Lists pgoutput plugin limitations
Gemini	Cautious	Mentions known bugs in 16.1
Grok	Safe with monitoring	Links to official issue tracker

Where they all agree (it's generally a valid choice), I trust the consensus. Where they diverge — specifically on what counts as acceptable lag — the tool surfaces that span and tells me "verify this yourself, the four models are not aligned."

That single workflow has changed how I research technical decisions. Instead of one model = one opinion = trust-or-verify, I now get a confidence map for free.

When this doesn't help

To be fair, multi-model is overkill for:

Quick code completions (just use whatever your editor has)
Personal conversational stuff (one model is plenty)
Anything where latency matters more than accuracy (4 calls = 4x slower)

It earns its keep on factual research, technical judgment calls, and anything where being wrong has a real cost.

Try it

Free tier with daily messages, no credit card: https://multiplechat.ai
Marketing site (if you want the pitch): https://multiple.chat

Happy to answer questions in the comments — especially about how Disagreement Detection actually works under the hood (it's roughly: embed each response, cluster by similarity, flag spans below threshold). And honest feedback welcome, including "this is just a wrapper" if that's where you land — I'll defend the design.

DEV Community: Melissa Özbilek