Mage vs. Legora: Which Legal AI Is Right for Your Firm?

Most comparisons of this kind age badly, because they freeze a competitor at the moment the page was written and then keep selling against a product that has moved on. This one has been rewritten for that reason. If you read an earlier version of this page, or a comparison anywhere that tells you Legora only answers questions and cannot produce a deliverable, discard it. That was true once. It is not true now.

What Legora actually ships today

Legora is a Stockholm-headquartered legal AI company building a firm-wide assistant for large law firms, in the same category as Harvey. Both are credible, modern, well-engineered products, and both have moved well past the copilot-only stage:

• Agentic Workflows, launched June 2025. Legora's own M&A example is uploading the data room so the system plans the work, extracts important information, highlights potential risks, and drafts relevant report sections. Their 2026 messaging goes further, describing agents that organize and review a room exhaustively and produce the diligence report.
• A Datasite integration, added May 2026, which analyzes data room documents inside Legora and auto-flags change-of-control, assignment, and indemnity provisions, and identifies missing or inconsistent documents.
• Structured citations, which Legora markets as best-in-class.

We say all of that plainly because a comparison that requires you not to know something is not a comparison. Data room integration in particular is no longer anyone's moat. Treat it as table stakes and stop scoring it.

The question that still separates the tools

Once every vendor in the category claims end-to-end agentic diligence, and they now do, the claim itself stops carrying information. Two questions survive.

Whose playbook ran the review?

This is the real fork, and it comes straight from Legora's own positioning rather than from anything we assert about them. Their pitch is that lawyers are the orchestrators who create and fine-tune custom workflows in natural language. That is a genuine strength for a firm that wants its own methodology encoded and is willing to own it. It also means the M&A logic lives in a workflow somebody at your firm authors, maintains, and updates as deal types change, and the coverage of any given review equals whatever was authored into it.

Mage takes the other path. The M&A issues taxonomy is built into the product by M&A lawyers and runs on every deal, every time, whether the matter gets your best senior associate or a first year. Your lawyers validate findings rather than maintaining a workflow library.

Neither approach is universally correct. A firm with a strong knowledge management function and a distinctive house methodology may well prefer to author its own. A firm whose partners' actual fear is variance, the question the second year did not know to ask on the deal that later goes bad, tends to prefer a floor that runs identically every time. What matters is knowing which trade you are making, and asking who owns that workflow library at your firm and what happens when they leave.

What did the review miss, measured rather than claimed?

Every vendor says it is accurate. That is every surgeon saying they are safe. The surgeon who publishes her complication rate is the one you let operate.

In July 2026 Harvey open-sourced a Legal Agent Benchmark. On a diligence scenario from it, scored by Harvey's own unmodified judge, Mage passed 576 of 635 binary expert-written criteria, or 90.7%, and read all 3,513 documents in the room. Under identical conditions, the same model and the same document parse and the same judge, LAB's open-source reference agent scored 11.0% and read 14 of the 3,513 documents.

The honest framing of that number matters as much as the number:

• The 11.0% baseline is LAB's open-source reference implementation. It is not a measurement of Harvey's product, and we do not present it as one.
• It is a criteria-level pass rate, which is not comparable to the task-level, all-or-nothing numbers on published leaderboards. We do not place the two side by side.
• It is one run. The number is 90.7% once, not "consistently over 90."
• We published the 59 criteria we failed.

That last point is the one we would actually like you to use. Ask every vendor on your shortlist, including us, for a measured miss rate on M&A diligence, and see who has a number at all.

Where each is built to win

Legora, and Harvey, win on breadth. A firm with a serious litigation practice plus a regulatory practice plus a transactional practice gets one platform everyone uses for everyday legal work, and cross-practice consolidation is real value when a question crosses practice lines. The conversational surface lowers the cost of adoption across a large user base, which matters enormously when the goal is firm-wide leverage.

Mage wins on depth in one workstream. The deal team gets a tool that ingests the data room, groups and titles every document into an index, runs the partner-defined risk pass, reconstructs amendment chains, drafts and redlines disclosure schedules, ties out cap tables against the source documents, and produces the memo and the issues list. Corporate, IP, tax, employment, real estate, and privacy work the same deal at the same time, each group in its own workspace on the documents routed to it. Every finding cites the exact page of the source agreement, and one click opens it.

Cap table tie-out is worth calling out separately, because it is computation rather than extraction. Mage derives the fully diluted math from the source documents and raises findings where the documents do not reconcile. That is a different kind of operation from finding and summarizing a clause, and it is not something a natural-language workflow produces.

How to run the evaluation

Pick a closed deal. Your team's own memo is the answer key, which means ground truth costs you nothing to establish. Point every tool on the shortlist at the same room, and score four things:

1. Amendment chains. Ask what the current operative termination provision is in a multi-amendment MSA, with a citation to the specific amendment, then open the citation. An answer that reads well and cites superseded language is the expensive kind of wrong.
2. Citation granularity. Click through. Does the citation land on the document or on the exact clause? Do not take any vendor's word for this, including ours. It is a ten-second test.
3. Coverage. Which documents did the tool actually read, who decided that set, and what happens when the seller uploads another two hundred files on a Thursday?
4. What it missed. Run your team's memo against the tool's output in both directions. What did it catch that your team did not, and what did your team catch that it did not?

The fourth is the only one that really settles anything, and it is the one vendors avoid. We volunteer for it.

How firms tend to choose

• Firm-wide AI assistance is the primary need, across a broad cross-practice base with M&A as one of several practices: a generalist is the right shape, and both Legora and Harvey are credible choices.
• High-volume M&A practice where partners want partner-grade diligence output: a specialist is built for that workstream.
• Both, which is where most large firms with serious deal volume land. The generalist for the everyday, the specialist for the deal.

The mistake is treating it as binary. Once M&A volume is real, these tools stop being substitutes for each other, and the question becomes whether to add a specialist on top of a generalist rather than which one to swap out.

If you want to see Mage on a real deal alongside whatever else you are evaluating, request a demo and bring the data room. We will run end-to-end diligence and walk through the result against your manual work product, in both directions. The comparison should be obvious either way.