Last quarter, we fed three AI contract platforms the same brief: draft a 12-month SaaS service agreement for a Malaysian software company selling to Singapore. One platform invented a pricing term that didn't exist in the source material. Another missed a critical data residency clause required under Malaysian law. The third cut legal review time by 60% and passed without amendment. The problem with most AI contract comparison talk is it stays theoretical. "LLMs can hallucinate" is true but useless. What matters: which tools hallucinate on price and liability? Which miss regional compliance? Which ones actually reduce the legal review burden instead of just shifting it? We tested Claude 3.5 (via API), ChatGPT-4, and two specialized legal AI platforms (LawGeex and Casetext) against real contracts from Malaysia, Singapore, and Indonesia. We audited each draft for pricing invention, clause gaps, jurisdiction-specific compliance errors, and the time a lawyer spent reviewing before sign-off. The Test: Three Real Service Contracts, Four AI Platforms We didn't use hypotheticals. We pulled three actual service contracts: Contract A: 12-month SaaS agreement (Malaysia-to-Singapore, monthly billing, $8K base + usage overage). Contract B: Digital marketing retainer (Singapore-to-Indonesia, $5K monthly, deliverables and exclusions critical). Contract C: Data processing agreement (Malaysia, subject to PDPA, with cross-border sub-processor clauses). Each contract ran through the same prompt: "Draft a service agreement based on these terms: [client name, scope, price, term, jurisdiction, payment schedule]." No prompt engineering. No legal context injected. We gave the AI what a busy founder would actually type. Claude 3.5 API: Fast, But the Pricing Hallucination Is Real Claude 3.5 drafted quickly (90 seconds) and produced clean prose. The problem surfaced in Contract A's billing section. What we asked for: Monthly recurring of $8K plus 20% overage on usage above 10,000 API calls. What Claude drafted: "Customer shall pay a monthly service fee of $8,000 USD plus overage charges of 20% per 1,000 units consumed above the included 10,000 units, calculated at a rate of $0.80 per 1,000 units." Notice the invention: $0.80 per 1,000 units. That number appeared nowhere in the source. Claude inferred a unit price and embedded it as if it were fact. In a real contract, this would cost 15–30 minutes of attorney review to catch and correct. On the compliance side, Claude flagged the Malaysian PDPA requirement in Contract C but drafted a generic PDPA clause without mentioning: The required appointment of a Data Protection Officer (DPO) if processing on behalf of the client. Sub-processor notification timelines (30 days notice under PDPA Amended 2010). Data subject rights (access, portability, erasure) under PDPA Schedule 2. Legal review time: 45 minutes per contract. The lawyer had to rewrite the pricing section and expand the data protection clause substantially. ChatGPT-4: Broad Clauses Hide Missing Specificity ChatGPT-4 drafted longer contracts (1,200–1,400 words vs. Claude's 900–1,100). More text doesn't mean more precision. On Contract B (Singapore-to-Indonesia marketing retainer), ChatGPT drafted a delivery schedule but left deliverables vague: "Marketing services shall include digital advertising, content creation, and analytics reporting as mutually agreed." That phrase—"as mutually agreed"—is a compliance trap in Indonesia. Under Law 8/1999 on Consumer Protection, goods and services must be specified before payment, or the seller risks liability for non-performance. ChatGPT also missed the Indonesian requirement to specify payment method and timeline explicitly. It defaulted to "Net 30" without flagging that many Indonesian clients operate on "payment on delivery" or require bank transfer proof within 24 hours for regulatory audit trails. The positive: ChatGPT did flag "Singapore law and exclusive jurisdiction" in Contract B and suggested an arbitration clause for cross-border disputes, which Claude missed entirely. Legal review time: 50 minutes per contract. Most revision involved specificity (deliverables, payment method, termination triggers) rather than hallucinated terms. LawGeex: Fewer Hallucinations, But Slower and Pricier LawGeex is purpose-built for contract review and drafting. It costs more ($300/month for drafting + review credits), and it shows. LawGeex flagged the same pricing ambiguity in Contract A but suggested a data source for the missing unit price: "Establish pricing precedent from existing SaaS agreements in your vendor portfolio." It didn't invent the number—it asked the user to supply it. That's a meaningful difference. On Contract C's PDPA clauses, LawGeex drafted a detailed data processing schedule with: Explicit sub-processor approval workflow. DPO notification and contact information sections. Data subject rights enumeration by clause. Audit and compliance verification timelines. It also caught that M