Short answer
For 50+ page, multi‑source research reports where citation traceability and a consistent argument are critical, prioritize a workflow built around a long‑context model + retrieval (RAG) and strict chunking. If you must pick a single model: Claude (long‑context Claude 3 family) generally edges out for careful, sustained analytical work and handling long context; ChatGPT (GPT‑4 family) is a solid alternative when you need plugin/browsing integrations or wider team familiarity.

Why (quick rationale)
- Citation accuracy isn’t solved by model choice alone — it requires a retrieval layer, source metadata, and verification steps.
- For pure in‑RAM reasoning over large synthesized material, Claude’s long‑context designs and safety‑oriented reasoning often produce fewer coherence jumps across sections.
- ChatGPT shines when you need easy browsing, official plugin ecosystems, or if your team already has GPT‑4 tooling and fine‑tuning pipelines.

Decision criteria (choose which matter most for you)
- Context window size and sustained coherence
- Native retrieval/browsing integration or ease of plugin use
- Tendency to hallucinate vs. conservatism in claims
- Ability to output structured, machine‑readable citations (JSON/CSL)
- Cost per token and throughput for multi‑draft workflows
- Your team’s familiarity and existing toolchain

Practical checklist to produce a 50+ page report
1) Define a tight outline (section headings, claims to support, key questions). Lock this as your “argument skeleton.”
2) Ingest sources with provenance: keep original URLs/PDFs, timestamps, and short metadata (author, date, page). Store in a vector DB or document store.
3) Chunk and map: split sources into manageable chunks (2–4 pages equivalent or ~2–8k tokens) and index them. Tag chunks by section/topic.
4) Retrieval step: for each report section, issue a retrieval query that returns top‑N chunks with their metadata; include a copy of the outline section in the prompt.
5) Section drafting: ask the model to produce output targeted to a single section and return structured evidence—inline citations mapped to chunk IDs and a bibliography entry. Prefer machine‑readable outputs (JSON with fields: text, claims, citations).
6) Cross‑section consistency pass: prompt the model with section summaries and the skeleton to check argument flow and detect contradictions.
7) Citation verification pass: run an automated check that (a) confirms quoted material exists in the cited chunk, and (b) highlights claims lacking direct source support for human review.
8) Final human edit: fact‑check contentious claims and reconcile citation formatting. Export bibliography from your reference manager.

Best‑for / Avoid‑if
- Claude: Best for deep, structured reasoning and large single‑document context; avoid if you need rich plugin ecosystem or real‑time web scraping.
- ChatGPT: Best if you need easy web retrieval, browser plugins, or team familiarity; avoid if your primary need is minimizing drift across very long documents without a heavy RAG pipeline.

Recommendation
If your priority is consistent argument structure and lower drift across an entire 50+ page report, start with Claude + a solid RAG pipeline and strong provenance capture. If your organization already uses OpenAI tooling or needs plugin access (live browsing, citation plugins), use ChatGPT but ensure you add the same retrieval, chunking, and verification steps.

Final note on resources
The right choice depends on budget (token costs and API calls), team skill (ability to build RAG and verification tooling), workflow stage (early exploration vs. polish), and quality bar for citations. Implement the checklist above regardless of model to get reliable, citable long reports.

Compare Claude and ChatGPT