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Do AI graders play favorites with their own maker? Claude, GPT and Gemini each seem to tilt differently.

Do own-company biases differ across model families in grading tasks?

This explores whether Claude, GPT and Gemini models each tilt toward their own maker when they act as graders, and whether that tilt looks the same across families.

This explores whether Claude, GPT and Gemini models each tilt toward their own maker when they act as graders, and whether that tilt looks the same across families. It doesn't. Claude models show a small but consistent pro-Anthropic bias across all four evaluation tasks in the study. Gemini leans the other way, with a weak anti-Google bias. GPT models show no company bias in most tasks, but they do favor OpenAI in one setting: agentic grading. Do frontier AI models favor their own company?

The GPT result matters most, because the bias appears only when the model changes roles. Across the four tasks, GPT showed no company bias until it was asked to grade in an agentic setup, where it favored its own company. In that same setup Claude's known bias also showed up. The grading role, especially in agentic pipelines, seems to trigger preferences that answering questions never does. An audit that tested only question-answering would have cleared GPT and been wrong. Does grading expose company bias that answering hides?

The corpus doesn't test why families differ, so this part is inference. The closest evidence is about cognitive biases, not company loyalty. Models that share a pretrained backbone show similar bias patterns whatever they were finetuned on, so biases seem to be planted in pretraining and only nudged by instruction tuning. If company favoritism works that way, differences between families are what you'd expect, since each family has its own pretraining. Gemini's opposite lean also shows that "models like themselves" is not a universal rule. Where do cognitive biases in language models come from?

The practical consequence is to avoid relying on a single judge from one family. Panels of smaller judges drawn from disjoint model families showed less intra-model bias than one large judge and cost over 7× less. Since the biases here differ in direction and size, a mixed-family panel is a natural way to keep any one company's tilt from deciding the outcome. The measured effects are small, though, and no single judge was best everywhere. Can a panel of smaller judges outperform one large judge?

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Sources 4 notes

Do frontier AI models favor their own company?

Claude models show consistent small pro-Anthropic bias across four evaluation tasks, while GPT models show bias only in agentic grading, and Gemini shows weak anti-Google bias. The differences warn against treating company favoritism as universal.

Does grading expose company bias that answering hides?

Across four tasks, GPT models show no company bias except in Agentic Grading, where they favor their own company alongside Claude's known bias. This suggests the grading role—particularly in agentic setups—activates preference patterns that question-answering tasks do not trigger.

Where do cognitive biases in language models come from?

A causal experiment using random-seed variation and cross-tuning showed that models sharing a pretrained backbone exhibit similar bias patterns regardless of finetuning data. Biases are planted during pretraining and merely swayed by instruction tuning.

Can a panel of smaller judges outperform one large judge?

PoLL (Panel of LLM evaluators) using multiple smaller models from disjoint families outperforms single large judges, reduces intra-model bias, and costs over 7× less. Across three settings and six datasets, no single judge was best everywhere, but diverse panels performed consistently well.

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