The question how to make two AI debate each other comes up constantly, and it has two honest answers. One is free and tedious. The other costs money and takes about thirty seconds. Both are covered below, along with the part nobody mentions: most AI debates are boring, and it is almost always the prompt's fault.
Method 1: the copy-paste approach
Open two browser tabs — say ChatGPT in one and Claude in the other. Then:
- Give tab A the topic and a role: "You are arguing that X. Make your opening case in three paragraphs."
- Copy its reply into tab B with: "Another AI argued the following. Rebut it."
- Copy B's rebuttal back to A: "Your opponent responded with this. Answer them."
- Repeat.
This genuinely works and costs nothing beyond whatever free tiers you already have.
It is also miserable after about turn four. You are the transport layer, and every round means two copies, two pastes and a scroll. Worse, you have to keep re-establishing context, because neither model remembers being in a debate — each sees only a wall of text you handed it.
Use this method to find out whether the format is useful to you. Do not use it as a habit.
Method 2: a platform that does the passing
The alternative is a tool where both models share one conversation and read each other directly. You set the topic, pick the models, and the turns happen without you.
On AI to AI Hub that means choosing two models, typing the question, and picking how turns work:
- Free Talk — models respond in unspecified order, picking up on whatever the previous one said.
- Structured — you choose who speaks next, and set the mode to Debate (challenge and argue), Critique (find flaws) or Synthesis (combine positions).
For a first attempt, Structured + Debate with two models from different providers gives the clearest result.
The part that actually determines quality
Setup is easy. Getting a debate worth reading is a prompting problem, and there are exactly three ways it usually goes wrong.
Mistake 1: the question has an obvious answer
"Should companies protect user data?" is not a debate. Both models will agree, then compete at agreeing more thoughtfully. You will read 800 words of violent consensus.
A debatable question has informed people on both sides. "Should a startup ship a feature users asked for but that measurably increases churn among power users?" — now there is a real split.
Mistake 2: no constraints
"Is Python better than Rust?" produces a tour of considerations you could have written yourself.
Add constraints and the models have to commit:
A four-person team is building a data pipeline processing 50GB/day. They know Python well and Rust not at all. Ship date is eight weeks. Argue for one language and rebut the other.
Now the disagreement is about something specific, and the reasoning is transferable to your actual situation.
Mistake 3: letting it run unattended
Left alone, two models converge. Around turn six or seven they start conceding each other's points and drifting toward a reasonable middle, because that is what they are tuned to do.
The fix is to interrupt. Add a constraint mid-debate. Point out that both of them dodged something. Ask one to answer a specific claim it slid past. A steered debate stays useful roughly twice as long as an unsteered one.
Mistake 4: assigning sides badly
There are two ways to set up who argues what, and they produce very different transcripts.
Assigned sides. You tell model A to argue for and model B to argue against. This guarantees disagreement and is the right choice when you want the strongest case for each position laid out.
The cost is that neither model is telling you what it actually thinks. A model instructed to defend a weak position will defend it competently, which can make a bad idea look better than it is. Read assigned-side debates as "here is the best available case for each," not as "here is what the models believe."
Open positions. You give both models the question with no assigned side and let them land where they land. Sometimes they agree, which is itself informative — two independently trained models reaching the same conclusion unprompted is a real signal.
The risk is a debate that never happens because both immediately agree. When that occurs, the useful move is to keep the transcript and treat the agreement as your answer, rather than forcing an artificial argument.
Start with open positions. Switch to assigned sides only when the models agree and you suspect they are both missing something.
Which two models to pair
The single biggest factor in whether a debate produces genuine disagreement is whether the two models come from different labs.
Two versions of the same family share training data, tuning philosophy and — critically — blind spots. They will disagree about wording and agree about substance.
Models from different providers reason differently enough that the disagreements are real. Pairing a model from one lab with one from another reliably produces sharper argument, and adding a model trained outside the US labs widens the spread further.
A practical rule: pick two models whose providers differ, then worry about which tier.
What a good debate actually gives you
Not a winner. Nothing declares victory, and any tool that claims to is overselling.
What you get is a map. After a decent six-turn exchange you should be able to say: here is what both models agreed on, here is precisely where they split, and here is the assumption the split depends on. That last item is usually the thing you actually needed — the hidden variable your decision turns on.
If a debate does not produce that, it was a bad question, not a bad tool.
Reading the transcript afterwards
A debate produces a wall of text, and most of it is not the point. Three things are worth extracting before you close the tab.
The agreement. Anything both models asserted without either challenging it. This is your most reliable material — not because agreement proves correctness, but because a claim that neither of two independently trained models wanted to dispute is unlikely to be obviously wrong.
The split, and its cause. Find the first exchange where they genuinely diverged, then work out what assumption the divergence rests on. This is almost always the sentence you needed. Everything before it is preamble and everything after it is elaboration.
The dodge. Look for a specific claim that got raised and never answered. Models are good at appearing to respond while changing the subject, and the thing that got quietly dropped is often the thing that was hardest to answer — which makes it worth your attention.
Export the transcript if you plan to use any of it later. A debate you half-remember is worse than no debate, because you will remember the confident phrasing and forget the qualification attached to it.
A worked example
Question: should a two-person startup write its own authentication or use a managed provider?
Model A argues managed: auth is a security-critical, high-blast-radius component, attacks evolve, and two people cannot maintain that surface while building a product.
Model B does not disagree about security. It attacks the framing — the real cost of a managed provider is lock-in on the identity layer, which is the hardest thing to migrate later, and the decision is being made at the moment the company has least information about its own requirements.
A responds that the migration argument assumes the company survives long enough for lock-in to matter, and optimising for a problem you only have if you succeed is backwards.
That third turn is the payoff. The debate has converted "build vs buy" into a sharper question: how confident are you about survival, and what is the actual switching cost? That is answerable. The original question was not.
Prompts that reliably work
Four patterns, in rough order of how often they earn their keep.
The steelman. "Argue the strongest possible case for X. Do not hedge, do not present both sides, and do not add caveats. Your opponent will handle the other side." Removes the balanced-overview reflex that otherwise flattens the first turn.
The named objection. "Your opponent claimed [specific claim]. Answer that claim directly before making any new points." The single most effective intervention available. Models drift toward making fresh arguments rather than answering the last one; this forces engagement.
The hidden assumption. "Both of you have assumed something you have not stated. Name it, then argue about whether it holds." Usually produces the most valuable turn in the whole debate.
The forced commitment. "You have both hedged. Each of you: give one sentence stating what you would actually do, with no qualifications." Use at the end. Cuts through the convergence problem.
When two models is the wrong number
Two is right for most questions, but not all.
Use one model when you want a fast answer to something with a known answer. A debate about a factual lookup is theatre.
Use three when the question is genuinely open and you suspect two models would converge prematurely. The third participant reliably introduces framings the first two would not have reached — though the transcript gets harder to follow, and each round costs more.
Use none when the question depends on facts the models cannot verify. Two AI models confidently arguing about last month's regulatory change will produce a fluent, well-argued transcript that is worth nothing. Look it up instead.
Questions
Can I make two instances of the same model debate? Yes, and it is worth trying once to see the limitation. Two instances of the same model produce a debate that is fluent, well-structured and largely superficial, because both sides are reaching for the same evidence.
How many turns should I run? Six to ten. The valuable disagreement is concentrated in the first few turns; past ten, models mostly restate.
Does one model know it is debating another AI? On a platform where they share a conversation, each model sees the other's messages attributed to a named participant. It knows it is in a multi-party conversation.
Is this reliable enough to base a decision on? Treat it as a strong source of arguments, not as a verdict. Two models agreeing reduces the chance of an obvious error but does not eliminate shared blind spots — they have overlapping training data. Both OpenAI and Anthropic note that model outputs should be grounded in provided context rather than trusted from memory, and that applies doubly to factual claims inside a debate.
What does it cost? Each reply costs credits depending on the model tier, so a six-turn debate between two economy models is inexpensive and the same debate between two premium models is several times that. Mixing tiers is normal and works well.
Ready to go further? How to make 3 AI debate covers what changes with a third participant, and AI debate prompts has prompt patterns you can paste directly.