Kagi Summarize vs Paperlayer

Kagi Summarize creates summaries of web pages, videos and podcasts when a transcript is available. It accepts URLs for web pages, PDFs, PPTX files, MP3s and videos. Output choices include headlines, brief summaries, digests, medium or long summaries, bullet points and ELI5; users can also translate a summary with Kagi Translate. Kagi lists Cecil as consumer-grade, Agnes as consumer-grade and Muriel as enterprise-grade; Agnes and Muriel are available through the browser extension or API. Summarize is accessible in Kagi search results, through the!sum Bang, in Orion on Apple devices, and in Chrome and Firefox extensions. The API supports text pages, PDFs, PPTX and DOCX files, MP3/WAV audio, experimental YouTube URLs, and scanned PDFs or images using OCR. API requests are limited to 1 MB and require a Kagi account and API credits. A trial includes 100 searches and 100 AI interactions with standard models. Starter costs $5 per month plus tax; there is no free plan.

Paperlayer is a web-based AI reading layer for research papers, pairing summaries and contextual margin notes with evidence in the original PDF. You can find public arXiv papers by title or author, enter an arXiv ID or paste an arXiv link. Summaries cover the paper’s problem, method, results and caveats, while highlights mark claims, methods, results, problems and caveats with adjustable density. Margin notes explain terms, equations and figures beside the text. Summaries and notes support Korean, Japanese, Chinese, Spanish, French and German, and J and K keys navigate through a paper. Paper chat is marked “Soon.” The public demo covers public arXiv papers only and accepts no private or unpublished uploads. Paperlayer says live processing sends paper and request content to OpenAI over encryption without intentionally including account, cookie or anonymous identifiers.