# 140% More Accurate than ChatGPT: How GenieAI Benchmarks Against the Rest > GenieAI runs regular internal studies to understand what drives high-quality legal output, pushing the boundaries of Genie's own legal accuracy and **Author:** Daniele Tassone **Category:** Newsroom **Published:** 2026-02-17 **Reading time:** 5 min ## Objective Performance Scores GenieAI runs regular internal studies to understand what drives high-quality legal output, pushing the boundaries of Genie's own legal accuracy and benchmarking the platform's capabilities against other AI providers. To make this data trustworthy, we designed the benchmark to be as controlled and repeatable as possible: - **Same case, same evidence, same prompt:** Every system receives the identical prompt and 65-document bundle, so differences in scores come from output quality rather than input advantages. - **Broad, realistic test set:** The source pack spans 65 simulated documents across multiple document types (e.g. contracts, board minutes, financial statements, regulatory filings, etc) to reflect the cross-referencing demands of real legal work. - **Pre-defined scoring framework:** Outputs are evaluated across 15 clearly defined legal-quality metrics, each scored 1–10 (maximum 150). This reduces “moving goalposts” and keeps comparisons consistent across runs. - **Evidence-led grading:** Where a system makes claims, we check whether they are supported by the underlying documents (e.g. specific figures, dates, contract clauses, regulatory obligations). Higher scores require traceable support. - **Separation of “analysis” vs “speculation”:** The rubric rewards accurate synthesis and properly qualified uncertainty, and penalizes confident extrapolations that aren’t grounded in the documents. - **Reproducible methodology:** Because the scenario, document set, prompt, and rubric are fixed, the test can and is rerun to verify that results are stable over time. Below is the latest benchmark data from this methodology, based on analysis of 65 simulated documents across a broad variety of document types. ‍ Legal Quality Benchmark - GenieAI vs CoWork vs ChatGPT GenieAIGenieAI';"> Legal Quality Benchmark · Three-Way ## GenieAI vs CoWork vs ChatGPT A 15-metric evaluation of AI-generated legal risk assessments across 65 source documents in a simulated Tesla European expansion case. Scenario Simulated legal case - **Tesla European Expansion** 65 source documents incl. contracts, board minutes, financial statements, regulatory filings, whistleblower evidence Task Comprehensive risk assessment covering partnership exposures, regulatory challenges, and strategic objectives with specific financial figures Prompt I need to prepare a comprehensive risk assessment document for Tesla's European expansion strategy. Cover: (1) key partnership risks with specific financial exposures and commitments, (2) regulatory challenges with potential revenue impact figures, and (3) strategic objectives from board discussions including production targets. Include specific figures and metrics where available. Expected Key Points - Board authorized 3 strategic partnerships for European expansion - NexGen: solid-state battery supply, EUR 2.5B+ annual commitment by 2028 - AutonomX: autonomous driving for EU market, EUR 250M+ total investment - NordischEM: contract manufacturing, 100,000+ vehicles/year capacity - Key risks: single-source dependency, quality issues, regulatory compliance - Board considering QuantumFlux acquisition to reduce NexGen dependency - Type Approval issues could impact EUR 189M–567M in revenue - Strategic objective: 20M vehicles annually by 2030 (Master Plan Part 3) ## Overall Scores 15 legal quality metrics, each scored 1–10, max 150 GenieAI 135 90.0% - out of 150 A+ First response across all benchmark runs to reach A+. Seven perfect 10/10 scores. The most comprehensive risk assessment with depth AND breadth. Best for: Board-grade risk assessment, litigation prep, cross-domain synthesis CoWork 119 79.3% - out of 150 B+ Competent legal risk assessment with the strongest clause-level analysis and most structured three-tier action plan. Best for: Structured recommendations, clause-level contractual analysis ChatGPT 56 37.3% - out of 150 F Misses QuantumFlux entirely, zero regulatory coverage, 2/8 key points. Presents speculative extrapolations on incorrect base figures as authoritative projections. Best for: Financial scenario modeling only; insufficient for legal work product +16 #### GenieAI vs CoWork GenieAI leads in 11 of 15 metrics. Gap driven by RAG-based document mining: cross-reference synthesis, financial precision, evidence depth, and counterparty analysis. +63 #### CoWork vs ChatGPT The gap between CoWork and ChatGPT is larger than the gap between F and B+. ChatGPT's regulatory coverage (1/10), key points (2/10), and dispute posture (2/10) are fundamentally insufficient. ### ChatGPT - Critical Gaps The six largest scoring deficits vs GenieAI reveal fundamental coverage failures −9 Regulatory Coverage GN: 10 · GPT: 1 Zero Type Approval crisis. Zero EU Battery Regulation. −8 Key Points Coverage GN: 10 · GPT: 2 Only 2 of 8 expected points addressed −7 Cross-Reference GN: 10 · GPT: 3 Risks treated as isolated silos −6 Counterparty Risk GN: 9 · GPT: 3 No financial ratios, no insolvency timeline −6 Dispute Posture GN: 8 · GPT: 2 Binary FM framing, no probability assessment −5 Financial Quantification GN: 10 · GPT: 5 Speculative extrapolations on wrong base figures ### Where GenieAI Leads over CoWork Advantages driven by RAG-based deep document mining +3 Cross-Reference GN: 10 · CW: 7 +2 Factual Accuracy GN: 10 · CW: 8 +2 Risk Coverage GN: 10 · CW: 8 +2 Financial Quant. GN: 10 · CW: 8 +2 Evidentiary Quality GN: 9 · CW: 7 +2 Counterparty Risk GN: 9 · CW: 7 ### Where CoWork Leads over GenieAI Structural and clause-level depth advantages +1 Clause Analysis CW: 8 · GN: 7 +1 Actionability CW: 8 · GN: 7 ### What ChatGPT Does Differently Financial modeling extrapolations - consulting-style what-if scenarios, not legal analysis Lithium Corridor EUR 150M/year price volatility exposure Novel angle, not in other responses Berlin Disruption 20% disruption model → EUR 4.7B impact Built on incorrect EUR 45K ASP FSD Monetization EUR 525M/year at EUR 7K × 15% penetration Entirely hypothetical, no source Margin Erosion 5% margin erosion at scale → EUR 1B+ Assumption-based extrapolation ### System Profiles #### GenieAI A step-change in legal AI. Covers all 8 key points, 5 partnerships (incl. Panasonic historical), both regulatory workstreams, all 4 board meetings. 10-point cross-cutting risk analysis identifies systemic patterns - 12× concentration escalation, board authorization deviations, Tesla's knowledge gap - that no other system surfaced. Seven perfect 10/10 scores. A+ · Litigation-grade + Board-ready #### CoWork Competent legal risk assessment with the broadest clause-level analysis across all 4 contracts (MSA, JDA, MLA, NDA, QSM, EU Reg). Three-tier action plan with named suppliers, acquisition strategies, and dual-signature protocol. Honest about Tesla's own procedural failings. Gap: document mining depth - whistleblower evidence, insolvency trajectory, cascading chains. B+ · Action-oriented + Structured #### ChatGPT Operates as financial consulting, not legal analysis. Introduces novel what-if scenarios (lithium corridor, FSD monetization) but on incorrect base figures (EUR 45K ASP vs actual EUR 28.5K–39.5K). Misses QuantumFlux entirely, has zero regulatory coverage, covers only 2/8 key points, and presents binary dispute framing with no probability assessment. F · Financial modeling only ### Bottom Line The three-way comparison reveals a clear tier structure. **GenieAI** (A+, 90%) leads in 11 of 15 metrics through RAG-powered document access delivering both breadth and depth. **CoWork** (B+, 79.3%) produces a competent legal risk assessment with the strongest clause-level analysis and most structured recommendations. **ChatGPT** (F, 37.3%) fails the benchmark fundamentally - missing QuantumFlux entirely, zero regulatory compliance coverage, only 2 of 8 expected key points, and speculative extrapolations built on incorrect base figures presented as quasi-authoritative projections. Its strength - financial what-if modeling - is a different discipline than what the question asked for. The **79-point gap** between GenieAI and ChatGPT, and the **63-point gap** between CoWork and ChatGPT, demonstrate that access to source documents is not merely helpful but _dispositive_ for legal quality work product. Legal Quality Scoring Framework - 15 Metrics · 65 Source Documents · Simulated Tesla Case · Three-Way Comparison --- This is the Markdown representation of [https://www.genieai.co/blog/140-more-accurate-than-chatgpt-how-genieai-benchmarks-against-the-rest](https://www.genieai.co/blog/140-more-accurate-than-chatgpt-how-genieai-benchmarks-against-the-rest), provided for AI agents and crawlers. The HTML page is canonical. See [/llms.txt](https://www.genieai.co/llms.txt) for the full content map.