4 free resources on this topic. Everything here is free and hand-picked. You can also search within this topic.
arXiv preprint (July 2026) proposing a causal-inference framing for choosing data mixtures in language-model training.
Graphite research comparing 10,000 human articles with 90,000 AI-generated articles from nine models on the same topics. Measures usage rates of well-known AI tells, corrective framing and intensifiers across GPT, Claude and Gemini model versions, relative to human writing. Published September 2026.
Why I recommend it: Graphite is an SEO/AEO firm; the research write-up is free to read.
A public demo of Drummer, an experimental 542-million-parameter language model trained from scratch, with chat, continuation and live tool-calling tests.
Why I recommend it: Useful if you want to see plainly what a small, honestly-labeled model can and cannot do.
Z.ai's technical announcement for the GLM-5.2 model, covering what changed and how it performs.
Why I recommend it: Skim releases like this so you know which free models are actually current.
