Compare AI Models
One prompt → several models, answers side by side.Unlike a council (which debates and gives one answer), this shows each model's raw response next to the others so you can compare them yourself.
Answers are generated independently by each model and shown unedited — no synthesis. AI can make mistakes; verify important claims.
Comparing AI models side by side
The Compare tool answers one simple question: given the exact same prompt, how differently do the top AI models respond? You type a question once, it goes to several models at the same time, and their raw answers appear in columns next to each other — no editing, no merging, no “winner” picked for you. It's the fastest way to see, with your own eyes, where the models agree and where they diverge.
This is the sibling of a Council. A council has the models debate and hands you one synthesized answer; Compare skips the synthesis and just shows you everything, so you stay the judge. Use Compare to explore and form your own view; use a council when you want a single, well-supported recommendation.
How to use it, step by step
- 1Pick your models. Each column has a dropdown — choose which model goes in it (Gemini, DeepSeek, Llama, Qwen, and any others you have keys for).
- 2Write one prompt. Type your question once in the box at the top. It'll be sent to every column exactly as written.
- 3Hit Compare. All the models are queried in parallel, so each column fills in as its model finishes — you don't wait for the slowest one to start reading the fastest.
- 4Read side by side. Scan the columns for agreement (a good sign) and disagreement (where the question is genuinely uncertain). Each column shows how long its model took.
- 5Swap and re-run. Change a model in any column and run again to test a different line-up — handy for deciding which model you trust for a given kind of task.
A worked example
“Explain the difference between REST and GraphQL to a junior developer, in 3 sentences.”
Gemini, DeepSeek and Llama each return their own 3-sentence explanation in separate columns.
Reading them together, you might notice two frame it around “how you fetch data” while the third leads with over-fetching — a small but telling difference in emphasis that tells you which explanation fits your audience best. You pick; nothing was merged or hidden.
Common ways people use it
See which model writes the clearest, most accurate answer for the kind of work you actually do, before committing to one.
If three models give three different answers to a factual question, that's your cue to dig deeper before trusting any of them.
Run the same prompt across models to see whether a weak answer is your prompt's fault or the model's.
Get several distinct takes on a piece of writing or an idea, then cherry-pick the best lines yourself.
Frequently asked questions
Compare shows each model's raw answer side by side with no synthesis — you're the judge. A council has the models peer-review each other and returns one combined answer with a confidence score.
Three columns by default, and you can switch the model in any column to any provider you have a key for.
No — each answer is shown exactly as the model returned it, unedited, so the comparison is honest.
Models are queried independently, so one slow or unavailable provider only affects its own column — the others still return normally.
It's free to use right now, and runs on the same providers as the councils. Each comparison makes one call per column.