Short answer / recommendation
Use an enterprise Claude deployment as your primary drafting engine and pair it with a retrieval system (RAG) for citations, then use Google Workspace (Docs/Drive) for collaboration/versioning. Claude is built for careful analysis and long-context work, which suits 20–40 page internal whitepapers; Google Workspace remains best for team collaboration if your org already uses it.

Why (quick): decision criteria
- Confidentiality and compliance: choose a provider and plan that explicitly offers non‑retention, enterprise tenancy, audit logs, and regional data residency. Enterprise contracts matter more than the model name.
- Long-context and analysis: whitepapers benefit from models that keep larger document context and summarize/chain arguments without losing structure.
- Citation handling: use retrieval + citations (RAG) rather than relying on the model’s memory. Look for models with first‑class support for source anchoring.
- Workspace integration: native integration reduces friction for review cycles; but integrations vary by vendor and plan.

High-level comparison (practical takeaways)
- Claude (recommended for drafting): strong at careful analysis and long-context documents; Anthropic enterprise offerings support private deployments and better controls for internal docs. Best when you prioritize rigorous synthesis and internal confidentiality. (See an overview: Read more about Claude)
- ChatGPT (easy starting point): default option for many teams; broad ecosystem and plugins (Drive, etc.) make integration easier, but ensure you buy the right enterprise tier for data protections. (Overview: Read more about ChatGPT)
- Gemini / Google: strongest native Workspace integration (Docs/Drive) and convenience for collaborative editing, but confirm controls (data residency, non‑retention) in your Google Workspace enterprise contract.

Best-for / Avoid-if
- Best-for Claude: long drafts, complex synthesis, teams that require tighter private deployments. Avoid if you need native Docs-first collaboration without building connectors.
- Best-for ChatGPT: small teams who want fast setup and plugin-driven workflows. Avoid if you need high-assurance data residency and non-retention guarantees without enterprise procurement.
- Best-for Gemini: orgs already deep in Google Workspace who want the smoothest Docs/Drive UX. Avoid if your compliance needs demand contract-level guarantees not available by default.

Practical checklist (do these before you let a model output the first draft)
1. Define security requirements: retention, region, audit logs, contractual SLAs.
2. Choose model + plan that matches those requirements (enterprise offering, private VPC or on‑prem).
3. Prepare sources: collect internal reports, citations, and external references you must include.
4. Ingest into a vector store (FAISS/Pinecone/Weaviate) and enable RAG so the model can cite source passages.
5. Create templates/prompts that require in-text citations and a sources appendix (e.g., “Cite source ID and quote span for every factual claim”).
6. Draft in Claude (long-context) to produce structured sections; export to Google Docs for collaborative editorial passes.
7. Human verification: subject-matter experts must check every citation and flagged claim.
8. Finalize metadata: version history, access controls, and a red-team privacy review before distribution.

When the right answer depends
- Budget: enterprise privacy and RAG tooling cost more.
- Skill level: small teams may prefer ChatGPT for speed; teams with ML/engineering resources can build a stronger RAG + Claude pipeline.
- Workflow stage/team size: early-stage drafting (one author) vs large cross-functional review (Docs + Workspace integration) will shift which tool you lean on.

If you want, I can sketch a minimal RAG architecture and prompt template tailored to 20–40 page whitepapers (Claude-centric) and show how to wire Google Drive for collaboration.