Short answer
Use Claude if your top priorities are careful, long‑context synthesis and fewer hallucinations. Use ChatGPT if you need richer ecosystem integrations (plugins/APIs) and built‑in export/automation for BibTeX.

Recommendation
- Primary pick for cleaner, verifiable literature reviews: Claude — better at steady, long‑document analysis and producing focused, citation‑anchored summaries.
- Pick ChatGPT when you need workflow automation (reference‑manager plugins, direct BibTeX export via tools, or company-standard GPT integrations).

Why (decision criteria)
- Citation precision: Claude tends to produce more conservative, concise answers and better maintain context across long threads; that reduces stray or invented citations. ChatGPT can be precise too but often requires stronger prompt constraints and verification steps.
- BibTeX/export: ChatGPT’s plugin/ecosystem and API make automated BibTeX exports easier to integrate into pipelines. Claude can output BibTeX text reliably when prompted, but you may need an extra step to ingest it into your reference manager.
- Reproducibility: Claude’s handling of long contexts helps keep consistent summaries across many papers. Reproducibility also depends on prompt templates, saved prompts, and versioned inputs more than model selection alone.
- Scale & workflow: If you’re ingesting dozens or hundreds of papers, Claude’s long‑context models reduce the amount of external chunking required; for automated ETL into Zotero/Mendeley, ChatGPT + plugins can be faster to wire up.

Best‑for / Avoid‑if
- Claude — best for: careful analytic summaries, long document synthesis, smaller teams that prize output fidelity. Avoid if: you need a seamless, out‑of‑the‑box BibTeX export or tight plugin integrations.
- ChatGPT — best for: teams automating exports to reference managers, building reproducible pipelines with APIs/plugins, or needing GUI tools. Avoid if: you want the model to be inherently conservative about inventing citations without heavy verification.

Practical checklist to produce reproducible, verifiable literature reviews
1. Start with a canonical source list (PDFs + DOIs). Store them in a versioned folder.
2. Use a fixed prompt template (save it) that requests: inline citations, DOIs, and a BibTeX entry for each referenced paper.
3. Feed the model the paper’s abstract/introduction or canonical citation metadata. Prefer giving the model text snippets rather than asking it to “find” papers.
4. Ask explicitly for: (a) a one‑sentence claim, (b) 3 evidence bullets with direct quotes + page numbers, (c) DOI, (d) BibTeX entry.
5. Validate outputs: automatically check returned DOIs/BibTeX with CrossRef or DOI resolver; flag mismatches.
6. Save model outputs and the prompt + model version. Use a small notebook or Git repo to record runs.
7. If you need bulk work: split large corpora into chunks (unless you’re using Claude’s long‑context mode), then stitch with a meta‑prompt.

When the right answer depends
- Budget: Claude (often paid tiers) vs ChatGPT (various pricing, plugin costs).
- Skill level: less prompt‑engineering skill favors Claude for steadier results; developer teams may prefer ChatGPT for integrations.
- Workflow stage: exploratory syntheses → Claude; automation/production exports → ChatGPT.

If you want, I can provide a tested prompt template that asks for DOI‑verified citations + BibTeX and a small validation script to check returned DOIs.

Compare Claude and ChatGPT