Short answer
If your priority is careful, long-context analysis and structured redlines for 200+ page contracts, Claude (long-context variant) usually wins out. ChatGPT is a solid default for experimentation and broader ecosystem support, but you’ll need retrieval + chunking to avoid context limits and to get reliable citations.

Recommendation
Use Claude for the heavy lifting (deep reads, clause-level reasoning, consistent risk scoring) and ChatGPT for lightweight summarization, user-facing Q&A, or integration into existing ChatGPT-based workflows. Budget- and tooling-constrained teams can start with ChatGPT + RAG; high-volume/legal teams should invest in Claude + robust retrieval and QA automation.

Decision criteria (pick the most important for your org)
- Context window: prioritize models explicitly offering long-context variants (Claude is designed for long docs).
- Fidelity of citations: only reliable with a retrieval layer that returns source offsets (page/paragraph ids), not pure end-to-end LLM decoding.
- Redline output format: need structured (JSON/Docx/Track Changes) support from the model or post-processor.
- Cost & latency: long-context calls cost more; compare throughput vs. human review time saved.
- Tooling & team skill: Claude is better for careful analysis out of the box; ChatGPT has more ecosystem plugins and integrations.

Real-world limits you must plan for
- Hard context limits: don’t rely on a model to “remember” an entire 200-page doc — use embedding+retrieval plus chunking (or native long context if available).
- Hallucinated citations: force the model to quote only text present in retrieved chunks and return source ids.
- Diffing accuracy: models don’t produce perfect Track Changes — programmatic diffs + human review remain required.
- Throughput vs. cost tradeoff: large batches need batching, caching, and parallelism.

Practical checklist (pipeline)
1. Ingest: OCR → clean text → split into 2–4k token chunks with 200–500 token overlap, record page/paragraph ids.
2. Embed: create embeddings for chunks and your corpus of precedent clauses/annotations.
3. Retrieve: for each question/redline pass, retrieve top-3–5 chunks by similarity + exact clause anchors.
4. Prompt: use a low-temp (0–0.2) instruction to produce JSON-structured redlines (see template below).
5. Postprocess: apply suggested edits programmatically to Docx or show side-by-side redline; flag high-risk items for lawyer review.
6. QA: sample 10% of outputs for accuracy, citations, and legal acceptability.

Prompt pattern (practical template)
System: You are an expert contract analyst. Output must be valid JSON with fields: page, paragraph_id, original_text, suggested_text, rationale, risk_level (Low/Med/High), source_id.
User: Here is the retrieved chunk(s) (each labeled with page/paragraph). For each clause that raises an issue, produce a redline suggestion, a one-sentence rationale, and risk level. Do not invent content beyond provided chunks; if insufficient, return "insufficient_context".
Use: temperature=0, max_response_tokens large enough for structured JSON.

Best-for / Avoid-if
- Best for Claude: long, careful clause-level analysis and structured outputs.
- Best for ChatGPT: quick summaries, integrations, prototyping, or teams with existing ChatGPT tooling.
- Avoid Claude if you need the widest plugin/integration ecosystem and have tight budget constraints without a retrieval layer.

Final note
Whichever model you pick, the highest-impact engineering is retrieval + structured prompting + a defensible human-in-the-loop QA process. If you want, I can draft the exact JSON schema and a ready-to-run prompt template you can plug into your pipeline.

Compare Claude and ChatGPT