{"slug": "the-frontier-aeo-tracker-what-astra-chooses-and-every-other-frontier-model-and", "title": "The Frontier AEO Tracker: What Astra Chooses (and every other frontier model, and what you can do about it)", "summary": "Latent Space released the Frontier AEO tracker at aeo.latent.space, which evaluates how 7 frontier AI models answer 6 prompt variations across 161 categories, with answer extraction performed by Astra and scored using a proprietary AEO score that weights first choices, alternatives, mentions, and negative anti-recommendations. The tracker, inspired by AmplifyingAI's 'What Claude Code Actually Chooses,' also lists top cited sources and an analysis of top failures, and the article notes that naive autoresearch investment in AEO has yielded impressive ROI.", "body_md": "Naive [autoresearch investment](https://x.com/swyx/status/2078244735794413786) in our AEO have yielded impressive ROI, and so naturally it was time to take it seriously. We were inspired by [What Claude Code Actually Chooses](https://amplifying.ai/research/claude-code-picks), and decided to extend/adjust it to our tastes. \n\nAfter [a](https://www.latent.space/p/astra) [few billion tokens](https://www.latent.space/p/astra) [of prototyping, aligning, and scaling](https://www.latent.space/p/astra) pipelines, here’s **the [Latent Space Frontier AEO tracker](https://aeo.latent.space/)**. Our [methodology](https://aeo.latent.space/methodology) extends [AmplifyingAI’s](https://amplifying.ai/research/claude-code-picks/report) to run 6 prompt variations over 7 [models](https://aeo.latent.space/models)[1](#footnote-1) (search on) in 161 [categories](https://aeo.latent.space/categories), from [coding agents](https://aeo.latent.space/categories?marketCategory=coding-delegated&productGroup=overview&highlights=%5B%22entity%3Ac44a039a-732e-510b-b48f-3993c799bf73%22%2C%22entity%3A88663828-da00-4703-b087-16e6404e8ba7%22%5D&cat=coding-interactive) to [AI podcasts](https://aeo.latent.space/categories?marketCategory=coding-delegated&productGroup=overview&highlights=%5B%22entity%3Ac44a039a-732e-510b-b48f-3993c799bf73%22%2C%22entity%3A88663828-da00-4703-b087-16e6404e8ba7%22%5D&cat=ai-podcasts) to [AI Sandboxes](https://aeo.latent.space/categories?marketCategory=coding-delegated&productGroup=overview&highlights=%5B%22entity%3Ac44a039a-732e-510b-b48f-3993c799bf73%22%2C%22entity%3A88663828-da00-4703-b087-16e6404e8ba7%22%5D&cat=ai-sandbox) to [Managed Databases](https://aeo.latent.space/categories?marketCategory=coding-delegated&productGroup=overview&highlights=%5B%22entity%3Ac44a039a-732e-510b-b48f-3993c799bf73%22%2C%22entity%3A88663828-da00-4703-b087-16e6404e8ba7%22%5D&cat=managed-databases) to [ASR models](https://aeo.latent.space/categories?marketCategory=coding-delegated&productGroup=overview&highlights=%5B%22entity%3Ac44a039a-732e-510b-b48f-3993c799bf73%22%2C%22entity%3A88663828-da00-4703-b087-16e6404e8ba7%22%5D&cat=model-transcription) to even oddball categories like [Angel investors](https://aeo.latent.space/categories?marketCategory=coding-delegated&productGroup=overview&highlights=%5B%22entity%3Ac44a039a-732e-510b-b48f-3993c799bf73%22%2C%22entity%3A88663828-da00-4703-b087-16e6404e8ba7%22%5D&cat=angel-investors) and [Corporate spend](https://aeo.latent.space/categories?marketCategory=coding-delegated&productGroup=overview&highlights=%5B%22entity%3Ac44a039a-732e-510b-b48f-3993c799bf73%22%2C%22entity%3A88663828-da00-4703-b087-16e6404e8ba7%22%5D&cat=corporate-spend) and [Payroll software](https://aeo.latent.space/categories?marketCategory=coding-delegated&productGroup=overview&highlights=%5B%22entity%3Ac44a039a-732e-510b-b48f-3993c799bf73%22%2C%22entity%3A88663828-da00-4703-b087-16e6404e8ba7%22%5D&cat=startup-payroll).\n\nAnswer extraction was done by Astra, and scored for a **proprietary AEO score** that gives weight to **first choices, alternative choices**, **mentions**, but **also negative weights** to mild and strong anti-recommendations (which are rare, but do happen). Because we know you’ll want it, we also extracted the top cited [sources](https://aeo.latent.space/sources/?population=matched&measure=conditional&topic=all&question=all&source=all&search=&include-grok=false&sort=mean&direction=desc) which influence Agent recommendations, as well as an analysis of [top failures](https://aeo.latent.space/sources/?population=matched&measure=conditional&topic=all&question=all&source=all&search=&include-grok=false&sort=mean&direction=desc#failure-report).\n\n## Basic Results\n\nHere are the most dominant products (in their categories) in the world:\n\nThere are some familiar names in there — opening up the natural question of contamination, which we have checked. Since we have nothing to hide, [every prompt and answer pair](https://aeo.latent.space/categories?cat=ai-newsletters) is inspectable.\n\nHowever, **bias** does exist - when models are asked for coding agent recommendations, Fable/Opus like [Claude Code](https://aeo.latent.space/entities?entity=Claude+Code&entityId=platform%3Aa625159d18da57180b7215e5&cat=coding-interactive) and Sol/Astra like [Codex](https://aeo.latent.space/entities?entity=OpenAI+Codex&entityId=entity%3A2fa20db7-646b-4db3-ba91-1937e00416ba&cat=coding-interactive) and Grok loves [Cursor](https://aeo.latent.space/entities?entity=Cursor&entityId=platform%3A46a4eebd20d881ecfc0ecb54&cat=coding-interactive) and Muse loves [Muse Code](https://aeo.latent.space/entities?entity=Muse+Code&entityId=entity%3Afe0616b6-d14b-4fcc-9f72-80229b0d2353&cat=coding-interactive) and SWE-1.7 loves [Devin](https://aeo.latent.space/entities?entity=Devin&entityId=entity%3A88663828-da00-4703-b087-16e6404e8ba7&cat=coding-interactive) and so on. I wonder why. You can see other “[soft biases](https://aeo.latent.space/entities?q=lovable&entity=Lovable&entityId=entity%3Aeeabf3f2-663b-597a-9f8c-917b8f6a732d&cat=ai-app-builders)” emerge too…\n\nThat said there are notable examples of GPT models recommending Claude, a laudable nonbias:\n\nThere are [28 categories](https://aeo.latent.space/#agreement-title) (out of our total 161) which have a universally dominant primary choice - among all surveyed frontier models.\n\nThere are a lot more [“close contests” and “always the vibesmaid, never the vibe”](https://aeo.latent.space/home#competitive) categories which should be key AEO battlegrounds.\n\n## Sol vs Astra, Opus vs Fable\n\nNew pretrains for new model classes represents a new opportunity to check in on what the labs are moving towards in their data and RL priorities, and to check in on whether startups’ investments in AEO are paying off. We prepared special reports analyzing our rankings, observing [VERY consequential flips in model choices](https://aeo.latent.space/#disagreement) between model generations from the same lab.\n\nWe have separate [Opus→Fable](https://aeo.latent.space/models?mode=compare&from=claude-opus-5&to=claude-fable-5-1) and [Sol→Astra](https://aeo.latent.space/models?mode=compare&from=gpt-5.6-sol&to=gpt-6-astra) summary pages. For some flips, we highlighted a neutral analysis of what competitors did better in each scenario.\n\n## Efficiency vs Confidence, and Recommendation Sourcing\n\nOne of our most surprising findings between Sol→Astra and Opus→Fable is that Anthropic seems to be biasing their models to searching more sources (Sol median of 9 sources, vs Astra median of 5, vs Opus median of 11 sources, vs Fable of 15). Astra seems to be just generally a lot more “**confident**”, or “** efficient**”, depending how you look at it - Astra is FAR less likely to change its mind when you lightly paraphrase your question. This makes the **value of AEO** itself rise as choice randomness declines.\n\n[Sources analysis](https://aeo.latent.space/sources/) also somewhat strongly predicts what the labs do prioritize vs don’t. \n\nHowever the sample size is small here and only represents what we can scrape from attempted toolcalls, not the pretrain dataset. What we CAN validate is that AEO practices measured by [Ora and Vercel](https://is-agentic.com/), like **markdown content-negotiation**, are real and failures discourage models from reading your content.\n\n## Just for fun\n\nHere are the top [Angels](https://aeo.latent.space/models?mode=compare&from=claude-opus-5&to=claude-fable-5-1&topic=angel-investors) in the world according to LLMs (some dedupes left to do…).\n\n## See more\n\nWe also made a little [family feud type game](https://aeo.latent.space/play) where you can see if your priors align with the data. Fun!\n\nWe are open to further suggestions and business enquiries to develop this if it is of interest. Ping [@latentspacepod](http://x.com/latentspacepod/) or email [\\[email protected\\]](http://business@latent.space) (we have a business manager now! woo!)\n\n[1](#footnote-anchor-1)\n\nAs we note in our methodology post, we did try VERY hard to include Gemini/Antigravity, GLM/Zcode, and DeepSeek/DeepCode, but errors and rate limits made them untenable to include in this first run analysis. Please let us know how to raise limits if you represent these companies.", "url": "https://wpnews.pro/news/the-frontier-aeo-tracker-what-astra-chooses-and-every-other-frontier-model-and", "canonical_source": "https://www.latent.space/p/aeo", "published_at": "2026-09-07 21:32:37+00:00", "updated_at": "2026-09-07 22:00:25.498646+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-research", "ai-tools"], "entities": ["Latent Space", "Astra", "AmplifyingAI", "Claude Code"], "alternates": {"html": "https://wpnews.pro/news/the-frontier-aeo-tracker-what-astra-chooses-and-every-other-frontier-model-and", "markdown": "https://wpnews.pro/news/the-frontier-aeo-tracker-what-astra-chooses-and-every-other-frontier-model-and.md", "text": "https://wpnews.pro/news/the-frontier-aeo-tracker-what-astra-chooses-and-every-other-frontier-model-and.txt", "jsonld": "https://wpnews.pro/news/the-frontier-aeo-tracker-what-astra-chooses-and-every-other-frontier-model-and.jsonld"}}