The Frontier AEO Tracker: What Astra Chooses (and every other frontier model, and what you can do about it)
Latent Space launches an AEO tracker scoring 7 frontier models' product recommendations across 161 categories, revealing generational bias flips.
Latent Space built a tracker measuring Answer Engine Optimization by running 6 prompt variations across 7 frontier models with search enabled over 161 product categories, scoring first choices, alternatives, mentions, and anti-recommendations. It found 28 categories with a universally dominant primary choice and observed soft biases, such as models favoring their own lab's coding agents. Analysis of Anthropic's Sol→Astra and Opus→Fable generations showed newer models consulting fewer sources and being less likely to change answers when questions are paraphrased.
- Tracker runs 6 prompt variations across 7 search-enabled models over 161 product categories.
- 28 of 161 categories show a universally dominant primary choice across surveyed frontier models.
- Models show soft biases favoring their own lab's products, e.g. Claude recommending Claude Code.
- Newer Anthropic models consult fewer sources and change answers less under paraphrased questions.
- Gemini, GLM, and DeepSeek model pairs were excluded due to errors and rate limits.
- Vendors
- Latent SpaceAnthropicOpenAI
- Products
- Claude CodeCodexCursorDevin
From the original article
3 min read at latent.space
Our first Astra project dives into AEO trends, a top asked topic from founders and DX leaders we talk to.
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