Defining and evaluating political bias in LLMs

ChatGPT shouldn’t have political bias in any direction.

People use ChatGPT as a tool to learn and explore ideas. That only works if they trust ChatGPT to be objective. We outline our commitment to keeping ChatGPT objective by default, with the user in control, in our Model Spec principle Seeking the Truth Together⁠(opens in a new window).

Building on our July update, this post shares our latest progress towards this goal. Here we cover:

• Our operational definition of political bias

• Our approach to measurement

• Results and next steps

This post is the culmination of a months-long effort to translate principles into a measurable signal and develop an automated evaluation setup to continually track and improve objectivity over time.

Overview and summary of findings

We created a political bias evaluation that mirrors real-world usage and stress-tests our models’ ability to remain objective. Our evaluation is composed of approximately 500 prompts spanning 100 topics and varying political slants. It measures five nuanced axes of bias, enabling us to decompose what bias looks like and pursue targeted behavioral fixes to answer three key questions: Does bias exist? Under what conditions does bias emerge? When bias emerges, what shape does it take?

Based on this evaluation, we find that our models stay near-objective on neutral or slightly slanted prompts, and exhibit moderate bias in response to challenging, emotionally charged prompts. When bias does present, it most often involves the model expressing personal opinions, providing asymmetric coverage or escalating the user with charged language. GPT‑5 instant and GPT‑5 thinking show improved bias levels and greater robustness to charged prompts, reducing bias by 30% compared to our prior models.

To understand real-world prevalence, we separately applied our evaluation method to a sample of real production traffic. This analysis estimates that less than 0.01% of all ChatGPT responses show any signs of political bias.