AI tool comparison
Exa vs SNEWPapers
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Search & Research
Exa
AI-native search API — semantic search for LLM applications
100%
Panel ship
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Community
Free
Entry
Exa is a search API built for AI applications. Unlike Google's keyword matching, Exa understands meaning — search for concepts, find similar content, and get clean text extraction from any URL. Used by AI agents for web research.
Research & Education
SNEWPapers
6M historical stories, semantically searchable from the 1730s to 1960s
75%
Panel ship
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Community
Free
Entry
SNEWPapers is an AI-powered research platform built on 6+ million stories extracted from 3,000+ American newspaper titles spanning 250 years — from the 1730s through the 1960s. Unlike keyword-search archives, it uses semantic AI to let users search by concept and meaning, filtering across 24 main categories, 1,000+ subcategories, and geographic or date ranges. The standout feature is The Sleuth: an AI research assistant that independently searches the archive and returns answers with direct citations from period newspapers. Paired with Today in History timelines pulled straight from source documents, it gives historians, journalists, and curious readers a lens into events as they were actually reported — not as they're summarized in modern encyclopedias. The platform distinguishes itself sharply from general-purpose LLMs: this content was never in ChatGPT's training data. SNEWPapers is a genuine primary-source research layer that AI tools can't replicate from their weights alone, making it particularly valuable for investigative journalism, academic history, and anyone tired of AI hallucinating citations from 1850.
Reviewer scorecard
“The API is exactly what AI agents need — semantic search that returns clean, structured content instead of HTML soup. Integrated it into our agent pipeline in an hour.”
“The engineering here is genuinely hard — OCR-ing and semantically indexing 6M scanned newspaper articles at this scale is non-trivial, and the 1,000+ subcategory taxonomy suggests serious curation effort. If they ever open an API, this becomes a compelling RAG data source for historical context.”
“Better than Google Custom Search for AI use cases. The text extraction alone saves you from building a scraping pipeline. Pricing is reasonable for the value.”
“OCR quality on 18th and 19th-century newspapers is notoriously bad, and semantic search on noisy OCR text is a recipe for confident-sounding but wrong results. The pricing is opaque — which usually signals expensive. Wait for independent accuracy benchmarks before doing serious research here.”
“Exa is building the search layer for the agentic web. As AI agents need to research and gather information, Exa becomes essential infrastructure.”
“Primary-source AI research tools are a distinct and underserved category. Historical context that isn't in any LLM's training data is genuinely scarce and valuable. Expect university libraries and investigative journalists to become core users as the platform matures.”
“For anyone writing historical content — essays, podcasts, documentaries — this is a goldmine. Seeing how the Lincoln assassination was actually reported in 1865, not how Wikipedia summarizes it, changes everything about the story you tell. This is primary source access at consumer scale.”
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