Latent Space measured which products seven frontier models recommend, and the newest ones change their minds less

Latent Space published on 7 September a tracker that ran six prompt variations through seven frontier models, search on, across 161 product categories. For each category it scored which products a model recommends first, as an alternative, or against.

The score is proprietary and the answer extraction was done by a model, which is why this nearly did not run. The finding that survives the methodology is that each lab’s model recommends its own coding agent.

First, the labs’ models favour their own tools: on the tracker’s reading, Fable and Opus recommend Claude Code, Sol and Astra recommend Codex, Grok recommends Cursor, and it found GPT models recommending Claude in places. Twenty-eight of the 161 categories, fewer than a fifth, have one universally dominant choice across every model surveyed. Second, the models differ in how much they read before answering: Latent Space reports a median of 15 sources for Fable and 11 for Opus against 5 for Astra and 9 for Sol, and says Astra is far less likely to change its recommendation when the question is lightly paraphrased. It also says the content-negotiation practice measured by Ora and Vercel, serving a markdown rendering when a request’s Accept header asks for text/markdown, is real, and that a page which fails it is one the models stop reading.

Gemini, GLM and DeepSeek were left out of this run because of errors and rate limits, so the sample is the models that answered, and the source counts come from scraped tool calls, not from training data. Every prompt and answer pair is published, which is the reason to read it rather than the reason to believe it.