AI Debate sets multiple large language models against each other in a structured argument — each AI debater holds an assigned position, responds to the opposing side round by round, and builds a case over several exchanges. Unlike asking a single model to "argue both sides," this tool runs separate models simultaneously, so differences in training data and reasoning style produce genuinely different arguments.
How topic specificity changes argument quality
The more specific the topic, the more substantive the arguments. "Should AI be regulated?" produces generic talking points; "Should governments require independent safety audits before deploying large language models?" forces debaters to engage with real trade-offs. Topics accept up to 2,000 characters, but a single clear yes/no question is usually enough. Each debater's position field accepts up to 1,000 characters — the more precise the stance ("Against: compliance costs are unenforceable for open-source models"), the sharper the speeches.
Pairing debater count with round count
2 debaters and 3–5 rounds is the most common setup: arguments have room to develop without becoming repetitive. Adding a third or fourth debater introduces more perspectives but increases token consumption quickly — every debater reads all prior speeches before generating a new response. Rounds max out at 10, but arguments tend to repeat after 6 unless the topic is genuinely complex. For multi-angle exploration, 3 debaters × 5 rounds tends to be more informative than 2 debaters × 8 rounds.
When the judge adds value
The judge runs after all rounds end and scores the full debate across four dimensions: argument quality, rebuttal effectiveness, persuasiveness, and consistency. If you need to quickly identify which side argued more convincingly, or want a structured summary you can reference elsewhere, the judge is worth enabling. If you are collecting raw argument material or comparing how different models reason, turn it off to save credits.
Enable the judge when
- You need objective scoring across debaters
- Teaching context — analyzing argument structure
- The topic has a defensible answer and needs a clear close
- You plan to share the debate transcript externally
Skip the judge when
- Collecting raw argument material for other use
- Comparing reasoning styles across models
- Budget is limited and you want fewer token charges
- The topic is open-ended with no clear right answer
Mixing models versus using one model for both sides
Assigning different models to different debaters produces more authentic clashes than giving the same model opposite instructions, because the underlying training data and reasoning tendencies differ. Models with visible reasoning chains display a collapsible "Thinking" panel — clicking it shows the internal steps before the final speech. Standard chat models skip straight to conclusions, which reads more concisely but offers less insight into how the argument was formed.
Pause vs. stop
Pause suspends the debate after the current debater finishes speaking and waits for you to resume. Stop ends the debate after the current speech and cannot be reversed. Neither operation discards completed content. If a judge is enabled and you stop early, the judge still evaluates the rounds that were completed.
What the exported file looks like
The export is a plain text file. Topic and round headers use separator lines; speaker names appear in square brackets:
Debate: Should governments mandate AI safety audits?
==================================================
--- Round 1 ---
[Alpha]
(speech content)
[Beta]
(speech content)
--- Judge Evaluation ---
[Judge]
(verdict content)
Export only becomes available once the debate reaches "Completed" status. In-progress debates cannot be exported.