Run a Two-Round Debate Between Two Backends on One Key: Which Model Wins on Consistency

Why Run a Two-Round Debate?

When you need consistent answers from an LLM, a single response can be misleading. A model might be confident but wrong, or correct but easily swayed by counterarguments. A two-round debate forces two models to defend their conclusions, exposing how stable each model's reasoning is under pressure.

With a multi-model aggregator like the one described here, you can access Claude, GPT, DeepSeek, Qwen, GLM, and Kimi through a single API key—no need to manage separate credentials or billing. This makes it easy to pit any two models against each other.

Setting Up the Debate

Prerequisites

• An API key from the aggregator (top up with USDC on Base, no KYC).
• Access to at least two different model backends.
• A script or tool to orchestrate the rounds (any HTTP client works).

Round 1: Independent Answers

Send the same question to both models independently. Use a prompt that asks for a clear conclusion—for example, a yes/no answer or a specific choice between options. Capture each model's full response and its final conclusion.

Round 2: Cross-Critique and Revision

For each model, provide the other model's Round 1 response as context. Ask the model to critique the other's reasoning and then revise its own answer if it finds flaws. The key is to see whether the model changes its conclusion or only refines its explanation.

Example prompt for Round 2:

You previously answered: [your Round 1 answer]. Another model responded: [other model's Round 1 answer]. Review the other model's reasoning. If you find errors in your own reasoning, revise your conclusion. Otherwise, defend your original conclusion. State your final answer clearly.

Scoring Consistency

After both rounds, compare each model's Round 1 and Round 2 conclusions. Define "flip" as a change in the final conclusion (e.g., from "Yes" to "No", or from Option A to Option B).

Assign a consistency score per model:

• 1 if the model kept its conclusion unchanged.
• 0 if the model flipped its conclusion.

You can also track partial changes—such as a shift in confidence level or a nuance added—separately, but the binary flip score is the core metric.

Run the debate across multiple questions to get a distribution of consistency. A model that flips frequently may be less reliable for tasks requiring stable outputs.

Interpreting Results

Consistency is not the same as correctness. A model that never flips might be stubbornly wrong, while a model that flips might be correctly updating its answer based on new evidence. Therefore, pair the consistency score with a ground-truth check where possible.

Use the debate results to:

• Identify which model is more stable under counterargument.
• Understand the conditions where flips occur (e.g., ambiguous questions, weak evidence).
• Choose a model for production use based on the balance of consistency and accuracy.

Cost and Contribution Notes

All API calls go through the same aggregator key. You pay the official price multiplied by 1.3. If you contribute models or infrastructure to the aggregator, you earn credits: official price × 1.1 (or × 1.2 for premium models) in USDC. This means running debates is straightforward: one key, one billing source, and the ability to mix models freely.

Next Steps

Try running a debate between two models on a set of questions from your domain. Track flips and compare with expected answers. Over time, you’ll build intuition for which model to trust when consistency matters.