Short answer / recommendation
- If your single biggest requirement is reliable multi‑document synthesis over very long context and you can adapt prompts and pipelines, favor Claude. If your team already runs on ChatGPT, needs plugin/connector ecosystem, or wants tighter integration with existing GPT tooling, use ChatGPT with a strong RAG + chunking pipeline.

Why (decision criteria)
- Context length & coherence: Claude is purpose-built for “long-context” synthesis; it tends to hold and combine many documents more reliably. ChatGPT is fine for iterative work but usually needs explicit retrieval and chunking to scale beyond the active context window.
- Citation provenance & reproducibility: Neither model magically produces perfect citations. The quality comes from your pipeline (indexing, retrieval, chunk IDs, citation templates, verification passes). Choose the model that fits your existing infra and team skillset.
- Ecosystem & integration: ChatGPT has broader 3rd‑party plugins and familiar UIs; Claude can be easier for careful analysis-style prompts but may require adapting workflows.
- Cost & throughput: Running 30k+ word reports across many documents will cost time and tokens—factor model pricing and API throughput into the decision.

Practical checklist (pipeline you can copy)
1) Preprocess: OCR / normalize documents and add metadata (title, date, section IDs). Prefer paragraph-level splits with source IDs. 2) Chunking & embeddings: Chunk ~500–1,000 words (or whatever your retrieval performs best on), embed and store in a vector DB with source+offset. 3) Retrieval: Use top‑K retrieval per report section; include overlap between chunks to avoid lost context. 4) Prompt template (reproducible): fix a system message, use temperature=0 for determinism, include a fixed citation format (e.g., [sourceID:paragraph#]). Save prompts and model versions in a repo. 5) Synthesis step: Map→Reduce pattern — ask the model to summarize each chunk (with quoted excerpts + source IDs), then synthesize section-level summaries citing sources. 6) Citation verification: Run an automated pass that checks each citation text against stored chunk text (string match / fuzzy match). Flag discrepancies for human review. 7) Final human review & reference list: Humans verify high‑risk claims and the assembled bibliography. 8) Logging & reproducibility: Save all prompts, responses, retrieval results, model/version, and random seeds. 9) Regression tests: Keep small gold datasets to verify synthesis quality after model updates.

Best‑for / Avoid‑if
- Best for Claude: long uninterrupted synthesis, careful analytical prompts, when you prioritize holding large context across many docs. Avoid Claude if you need deep plugin integrations or your team already has heavy ChatGPT infrastructure.
- Best for ChatGPT: teams that value ecosystem, rapid iteration, and existing ChatGPT pipelines. Avoid ChatGPT if you need a single-pass model to ingest huge context unless you build a robust RAG/chunking system.

Extra practical tips
- Use temperature=0 and fixed system instructions for reproducibility. Log every retrieval result together with the prompt used so you can re-run exactly. Always include a machine-checker that verifies that quoted citations exist verbatim in the source chunk. For final QA, have 1–2 domain experts read and mark errors before publishing.

When it depends
- Budget: Claude may be more expensive per long-run but can reduce editing time. ChatGPT may be cheaper if you can split work into many small calls.
- Skill and team size: Smaller teams often prefer ChatGPT for ease; research teams with engineering support will benefit more from Claude’s long‑context strengths.

Recommendation (one line)
- If you can invest in a reproducible RAG + chunking pipeline and need maximal long‑context synthesis, start with Claude; if you need ecosystem, integrations, or lower-friction adoption, use ChatGPT but build the same retrieval + verification safeguards.

Compare Claude and ChatGPT