Recommendation (short):
For a small law firm doing clause extraction, risk summarization, and citation checking, start with a Claude-based workflow for primary review (better out‑of‑the‑box handling of long documents and conservative summaries), and use ChatGPT where you need broader tooling/ecosystem or lower-cost/ad hoc queries. In all cases use retrieval-augmented generation (RAG) + lawyer-in-the-loop verification to eliminate hallucinations.

Why (summary):
- Hallucination risk: both models can hallucinate. Claude is typically more cautious and produces fewer confident fabrications on legal summaries, but neither should be trusted without source grounding and human review.
- Long-context handling: Claude is designed for longer-context tasks and can often summarize or extract across long contracts with fewer chunking hacks. ChatGPT (depending on model/version) can match this if you use large-window GPT models (32k/128k) or an effective RAG pipeline.
- Citation reliability: neither reliably invent correct citations alone. The reliable approach is to return exact text offsets or clause IDs from the original document and include extracted source snippets rather than free-form citations.

Decision criteria (what to evaluate):
1. Hallucination tolerance: measure false positives (invented risks/clauses) on a labelled test set.
2. Grounding: ability to return exact source snippets and offsets. Prefer tools that let you attach provenance.
3. Context window: size and quality across whole-contract extraction without manual stitching.
4. Cost & throughput: per-page or per-token cost for your expected volume.
5. Integrations & APIs: ease of hooking into your document store, DMS, or Matter management.
6. Compliance & security: data residency, encryption, and audit logs.
7. Explainability: quality of rationale and traceable summaries for partner review.

Best‑for / Avoid‑if
- Best-for Claude: conservative risk summaries, whole-contract distillation, fewer iterations when you must ingest long documents.
- Best-for ChatGPT: teams that want rich ecosystem integrations, more prompt-engineering tooling, or specific GPT-only features; good for interactive Q&A after the ground truth is established.
- Avoid Claude if you need deep GPT-specific integrations or very tight cost constraints (confirm current pricing). Avoid ChatGPT if you can’t implement RAG and need out‑of‑the‑box long-context reliability.

Practical checklist to pilot (2–6 week trial):
1. Build a 50–200 contract test corpus with known clause labels and ground-truth risk annotations.
2. Create identical prompts and a RAG layer that returns exact clause text + offsets for both models.
3. Run extraction: measure precision/recall on clause extraction, and rate hallucination incidents (invented citations/clauses).
4. Measure summary fidelity: compare automated risk summaries vs. partner-written summaries (score by legal reviewer).
5. Stress-test long docs: confirm end-to-end latencies and whether manual chunking is needed.
6. Check provenance: verify model outputs include source snippets or clause IDs; if not, add the RAG anchor.
7. Security review: confirm encryption, logging, and retention policies meet your firm’s requirements.
8. Decide: if precision < 95% or hallucinations remain, require mandatory human sign-off and tighten workflow.

Final note on dependency: the “right” choice depends on budget, your team’s engineering skill (to build RAG), volume, and whether you prioritize raw throughput or conservative legal safety. Start with Claude for long-contract, conservative runs and layer in ChatGPT where it improves automation or cost-effectiveness.

Compare Claude and ChatGPT