Recommendation
Use Claude as the deep extractor/analyst and ChatGPT as the concise summarizer. Have Claude process full texts (or large chunks) to produce a strict, machine-readable intermediate (JSON rows per paper: claims, methods, key results, limitations, evidence quotes, confidence). Then hand that JSON to ChatGPT to produce short TL;DRs, standardized bullet summaries for stakeholders, and a short synthesis across batches.

Why this works
Claude handles long context and careful analysis better; that makes it ideal for extracting structured facts, nuance, and evidence. ChatGPT is faster and excels at compressing information into readable summaries for reports or slide decks.

Decision criteria (pick per batch)
- Use more Claude if you need high-fidelity extraction, many quoted evidence strings, or cross-section meta-analysis.
- Use more ChatGPT if speed, short stakeholder-ready blurbs, or lower cost per token is the priority.
- Budget/skill: deeper Claude runs cost more and need careful prompt engineering; small labs may prefer lighter Claude passes + more ChatGPT.
- Team size/workflow stage: if you have a QA reviewer, run aggressive automation; if small team, keep human-in-the-loop at extraction verification.

Practical hand-off pattern (concise)
1) Ingest: PDF -> OCR -> split into chunks (3–5k token chunks, 200–500 token overlap).
2) Claude analysis (per chunk or whole paper depending on token limits): prompt Claude to return a strict JSON with fields: paper_id, title, abstract, methods_summary, main_claims [text+confidence], key_results [fig/table refs], important_quotes [text+location], limitations, citation.
3) Aggregation: merge chunk outputs into a single paper JSON (dedupe quotes, reconcile claims). Add a simple confidence score based on count of corroborating quotes.
4) ChatGPT summarization: feed ChatGPT the aggregated JSON for each paper or batch, prompt for: 2-sentence TL;DR, 5 bullet points (context, method, result, limitation, why it matters), one-sentence recommended citation line.
5) QA: spot-check 10% or highest-impact papers; fix prompt & rerun as needed.

Template prompts (short)
- Claude (analysis): "Read text and return ONLY JSON with these fields: paper_id, title, abstract, methods_summary, main_claims[], key_results[], important_quotes[], limitations[], confidence. For each claim include supporting_quote and page_location."
- ChatGPT (summarize): "Given the JSON for each paper, produce: (A) 2-sentence TL;DR, (B) 5 bullets: [context, method, main result, limitation, implication], (C) 1-line citation. Keep it concise and consistent across papers."

Checklist before scale
- Ensure reliable OCR and metadata extraction
- Decide chunk size and overlap
- Implement strict JSON schema for handoff
- Rate-limit handling and batching scripts
- QA sampling plan
- Storage (DB or S3) and provenance tracking

Best-for / Avoid-if
- Best for: large corpora where you need evidence-backed extraction + readable summaries for stakeholders.
- Avoid if: you have only a handful of papers (manual read may be quicker) or you can't afford human QA for critical decisions.

If you want, I can draft the exact Claude JSON schema and the two short prompts tuned for a pilot 50-paper batch. For automated tooling, use Claude for analysis and ChatGPT for summaries (cta: Claude).

Compare Claude and ChatGPT