Mage vs. Harvey: A Feature-by-Feature Comparison for M&A Counsel

This is the deeper-than-marketing version of how Mage and Harvey compare for M&A counsel. Written by the team at Mage, but with a deliberate ground rule: factual, with claims about Harvey sourced to their public materials, no unverified negative assertions about a competitor we respect.

The category lines

Harvey is a firm-wide AI assistant. The product is positioned across a firm's practice areas — corporate, litigation, regulatory, employment, IP — and a meaningful chunk of the value proposition is consolidating cross-practice queries on a single platform. Their public materials describe a copilot model: legal professionals ask the tool questions, draft documents, and run analyses across the firm's document corpus.

Mage is an M&A platform. The product is positioned around the deal-team workflow specifically: data room ingestion, classification, risk-driven document review against partner-defined checklists, amendment chain resolution, disclosure schedule synthesis, redline review, memo drafting, and post-signing covenant tracking. The full workstream a buy-side or sell-side team executes on a deal.

Both companies are using frontier LLMs underneath (current-generation Claude, GPT, etc.). Neither has a meaningful model-quality advantage. The differentiation lives in the layer above the model — the pre-processing, the workflows, the prompts, the validation, the integrations. That's where the products diverge.

Where each is built to win

Harvey's strength is breadth. A firm with a serious litigation practice plus a regulatory practice plus a transactional practice gets a single tool everyone uses for everyday legal work. The cross-practice consolidation is real value, especially when a question crosses practice lines (a regulatory question that affects a deal, for example). The user experience is conversational, which lowers the activation cost for adoption across a large firm.

Mage's strength is depth in M&A. The deal team gets a tool that ingests the data room, classifies every document, runs the partner-defined risk pass overnight, reconstructs amendment chains, drafts memos in the firm's voice, and produces disclosure schedules from the underlying agreements. The user experience is workflow-shaped rather than conversational — by the time the associate logs in on Day 2, the work is partly done.

For a firm whose primary AI need is firm-wide coverage, Harvey is a strong fit. For a firm whose M&A practice is the high-volume workhorse, Mage is built for that workstream specifically. Many large firms end up with both.

Workflow fit on a real deal

The clearest test is to run a real M&A deal through each tool and compare. We have written the framework for this evaluation in Evaluating Legal AI Tools. The short version:

• Issue spotting accuracy. What fraction of real issues did the tool flag? What fraction of flagged items are real? Compare against manual ground truth. The bar is partner-grade.
• Amendment chain handling. Pick a multi-amendment MSA from the data room and ask each tool what the current operative termination provision is. Bonus points for the right answer with a citation to the specific amendment.
• Disclosure schedule synthesis. Sell-side counsel: ask each tool to draft Section 3 schedules from the underlying agreements. Compare to what the team would build manually.
• Output voice and structure. Does the first-draft memo match the firm's house style, or does the partner end up rewriting it from scratch?
• Time-to-deliverable. From data room access to partner-reviewable issues list and memo, how many hours? How does that compare to the manual baseline?

The tool that wins on these metrics is the right tool for the firm asking. Different firms get different answers; that is fine.

Trust posture

Both companies have credible security postures. Both publish SOC 2 Type II reports. Both have written no-training-on-customer-data positions. Both are used by Am Law firms on privileged content. We document Mage's posture specifically on the security page; Harvey's posture is documented in their public materials and DPA.

A firm running serious procurement should still send a written security questionnaire to both, get the SOC 2 reports under NDA, and let GC and privacy counsel review before any privileged document touches either platform. The posture-on-paper is necessary; the posture-in-practice is what GC verifies.

Pricing model

Pricing for both tools is enterprise: not a published per-seat number, negotiated per-firm. The pricing structure that fits varies by firm. Harvey's firm-wide model tends toward per-user-per-month with tiered features. Mage's specialist model tends toward per-deal or per-matter pricing aligned to M&A utilization. Both vendors will scope a pricing structure that fits a specific firm's volume; this is not a meaningful axis of differentiation in the early evaluation.

How firms tend to choose

A reasonable decision tree we have seen across our customer base and at firms we have lost to Harvey:

• Firm primarily wants firm-wide AI assistance for a wide cross-practice base, with M&A as one of several practices: pick Harvey or a similar firm-wide assistant.
• Firm has a high-volume M&A practice and the partners want partner-grade output on diligence specifically: pick Mage (or another M&A-specific tool).
• Firm wants both: deploy them both, with Harvey for cross-practice work and Mage owning the M&A deal-team workflow. Many of our customers run this way.

The mistake to avoid is treating the choice as binary when it isn't. A general physician and a cardiac surgeon serve different needs; a firm with serious M&A volume often justifies both layers.

Where to read more

Our broader competitive landscape view, including how generic LLMs (ChatGPT, Claude, Gemini) fit into the picture: Legal AI vs. Harvey vs. Generic AI.

Our marketing-page version of this comparison, more product-focused: /harvey-alternative.

Our buyer's-guide framework for running an honest evaluation: Evaluating Legal AI Tools.

If you want to see Mage on a real deal alongside whatever else you are evaluating: request a demo. Bring the data room. We will run end-to-end diligence and walk through the result against your manual workproduct. The comparison should be obvious in either direction.