DescrybeLM answered all 200 bar exam questions correctly. ChatGPT, Claude, and Gemini each missed between 13 and 23—and scored lower on legal reasoning quality.
BOSTON, MA, UNITED STATES, March 5, 2026 /EINPresswire.com/ — When AI gets a legal question wrong, the most dangerous failure isn’t an obvious error. It’s an answer that sounds authoritative: fluent, confident, well-structured, and yet applying the wrong legal standard. The error reads like competent lawyering.
Today, Descrybe launched DescrybeLM — an AI system built specifically for legal reasoning — and published a white paper with benchmark data to show what that difference looks like in practice.
Descrybe ran a controlled benchmark against ChatGPT 5.2, Claude Opus 4.5, and Gemini 3 Pro on 200 multistate bar exam questions. The study measured not just whether each system chose the correct answer, but whether the legal reasoning behind it was sound: Did it identify the right rule? Apply it correctly to the facts? Avoid the traps that produce persuasive but wrong analysis?
“We had a thesis that purpose-built legal AI produces meaningfully different results for legal reasoning tasks. Legal professionals deserve to make tool decisions based on real evidence. So we tested ourselves, published our methodology, and invite anyone to replicate it,” said Kara Peterson, Co-Founder and CEO of Descrybe.
What the benchmark showed
All four systems were tested under standardized, no-external-web conditions using the NCBE MBE Complete Practice Exam (Questions 1–200, no exclusions), producing 800 separate evaluation runs with blinded scoring.
When general-purpose models were wrong, they were confidently wrong. Among 52 incorrect outputs, 49 delivered assertive, well-structured reasoning that did not signal uncertainty — the failure mode that imposes the highest verification burden on practitioners. The dominant patterns were applying the wrong legal standard or misapplying the correct one, while the prose read like competent analysis.
Two models — Claude Opus 4.5 and Gemini 3 Pro — exhibited overconfident tone on correct outputs as well as incorrect ones. DescrybeLM and ChatGPT 5.2 received zero overconfidence flags across all 200 outputs. A system that sounds equally confident whether it is right or wrong gives practitioners no reliable signal from tone alone.
The study also found that cross-checking between general-purpose models is not a reliable substitute for getting the answer right. Across 200 questions, 40 were missed by at least one model, 11 by two or more, and only 1 by all three — meaning errors were largely unpredictable and non-overlapping.
What’s behind the results
DescrybeLM is built on a curated primary-law corpus of more than 100 million structured records, requiring more than 100 billion tokens of preparation.
“Most AI tools are built for general use and adapted for law. DescrybeLM was built differently: from the foundation up, specifically for legal reasoning, on more than 100 million structured records individually cleaned and organized for that purpose. That kind of data work is painstaking and takes years — but it’s the difference between a system that sounds right and one that is right,” said Richard DiBona, Co-Founder and CTO of Descrybe.
Why this matters
The headline problem in legal AI isn’t systems that obviously fail. It’s systems that fail invisibly, confidently, and in a way that reads like competent analysis. In a crowded market, sounding right is easy to mistake for being right. Legal professionals need real evidence to decide which tools to use for which purposes — which is why Descrybe published its methodology and invites independent replication.
“It’s rare to see something that genuinely stops you in your tracks. When I saw DescrybeLM answer all 200 multistate bar exam questions correctly while ChatGPT, Claude, and Gemini each missed double digits — that’s not a marginal difference. That’s a different category of tool,” said Ken Friedman, legal technology pioneer and advisor to Descrybe.
The full white paper, Beyond Confidently Wrong: How Purpose-Built AI Mitigates Legal Reasoning’s Hidden Risk, is available now.
Kara Peterson
Descrybe
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