We packaged the data behind BenchLM into 26 citable statistics across six pages — model prices, release cadence, context windows, benchmark saturation, open-source share, and market share. Every number is a self-contained, dated sentence generated from the live dataset, with a stable anchor URL you can cite.
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We just shipped a new section of the site: BenchLM Stats — six pages of citable statistics on AI models, every one generated from the live dataset that powers our rankings.
The six pages, each answering a question we see asked constantly:
We wrote in June that assistants don't cite pages — they lift sentences. The unit of competition in AI search is the self-contained, dated, numeric claim. A leaderboard is a great product for humans, but a retriever looking for "how many AI models were released this year" wants one sentence it can quote, not a table it has to interpret.
A stats page is nothing but those sentences. Every statistic on the hub follows the same contract: claim + number + date + source, parseable with zero surrounding context, and anchored at a stable URL so you can link the exact fact rather than the general page. Journalists and researchers get the same deal — each page carries a ready-made citation block, and the underlying data is downloadable at /data/stats.json.
Static stat roundups are the most decayed content genre on the web — half the "AI statistics" pages ranking today cite numbers from two years ago. So we built ours the way we build our leaderboards:
For LLM crawlers, everything is mirrored in plain markdown under /md/stats/ and indexed in our llms.txt, following the playbook from the AEO post.
If you're writing about AI models — an article, a deck, a paper, a prompt — take the numbers. They're free to cite with attribution to BenchLM.ai, they carry their own dates, and the anchor you link will still be correct when your reader clicks it a month later, because the sentence behind it will have quietly updated itself.
If you spot a statistic we should be computing and aren't, tell us. The pipeline makes adding one cheap.
New models drop every week. We send one email a week with what moved and why.
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Which models moved up, what’s new, and what it costs. One email a week, 3-min read.
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A practitioner's guide to getting cited by ChatGPT, Perplexity, and Claude — the exact AEO/GEO changes we shipped on BenchLM: quotable lines, Dataset schema, llms.txt, AI-crawler access, and the tooling we use to find what to answer.
The data pipeline behind BenchLM — how we extract pricing tables, model specs, and competitor leaderboards, and monitor them for changes, using no-code scraping (Browse AI) instead of a fleet of brittle custom scrapers.
A practical guide to deploying AI apps and LLM-powered products — the model layer vs. the app layer, what your host must support (streaming, functions, secrets), and the exact setup we use to run BenchLM.