Harvey vs Legora vs CoCounsel vs Lexis+ Protégé: What the Platforms Actually Do

A lawyer on the innovation committee of a large US firm below the Am Law rankings sat through the Harvey vs Legora vs CoCounsel demos and posted the prices the firm was quoted on r/legaltech. Another lawyer in the same thread described how the decision went at their firm: “All the associates hated Harvey and weren’t too keen on CoCounsel. They loved Westlaw’s AI research, but go figure the partners went with Harvey because they think it’s magic. Going to be an interesting year” (r/legaltech). That thread is the most candid public record of what these platforms cost, because no vendor publishes a price.

That is the problem with searching for “Harvey vs Legora” in September 2026: almost every comparison that ranks is written by a challenger vendor whose conclusion is “buy us”. The antidote is to stick to the vendors’ own documentation, the one public benchmark that tested them side by side, reported prices with sources labelled, and what lawyers say when nobody from sales is in the room. One distinction explains most of it: Harvey and Legora are work platforms that sit over your documents; CoCounsel Legal and Lexis+ with Protégé are research platforms that sit over a legal corpus. That is why firms increasingly buy one of each.

Who they are in September 2026: money, customers and the model underneath

The money matters. Harvey’s annual recurring revenue was reported at $190m in January 2026 and above $350m by August (The Information, via PYMNTS), and its $550m round of 9 September 2026 came with its fourth acquisition of the year; Legora’s annual recurring revenue went from $3m at the end of 2024 to $150m by June 2026 (Sacra), with five acquisitions. You are buying a company mid-sprint, so renewal-uplift and data-export clauses matter more than the feature list. The wider map is in the legal AI tools map.

Harvey: Vault, agents, Copilot, and 80% of the Am Law 100

Harvey’s surface in 2026 is an assistant, Vault (bulk document sets with review tables), Knowledge, Workflow Agents and, since 9 March 2026, an Agent Builder. Its own figures: 25,000+ customer-built workflows and, from May 2026, more than 500 pre-built practice-group agents.

The Microsoft relationship is the differentiator. Since March 2026 Harvey runs as an agent inside Microsoft 365 Copilot: type @Harvey in Word, Outlook or Teams to ask a question, pull precedent from Vault or analyse the open document; since June it also runs inside Copilot Cowork. On 23 July 2026 Microsoft’s own legal department, roughly 2,000 people, adopted it. Firm-wide deployments are documented at A&O Shearman, Latham & Watkins (3,600+ lawyers), DLA Piper (5,000 licences) and Slaughter and May.

The criticism is documented too. The committee poster found Harvey “no more than GPT chat + vault … + workflows … not exactly groundbreaking”. On r/LawFirm in September 2026: “Nobody actually uses Harvey in earnest. It’s just a way for big firms to look forward thinking.” What the critics describe is the pilot stage: ILTA’s 2025 survey found 71% of Harvey-using firms still piloting.

Legora: Tabular Review, aOS and a Munich office

Legora started as Leya and rebranded on 19 February 2025. Its signature feature is Tabular Review: documents as rows, prompts as columns, every cell linked to its source passage, which is the shape diligence naturally takes. Around it sit Word and Outlook add-ins, a research module, Monitors for regulatory alerts, a client Portal and, since 7 May 2026, the “aOS”, its agentic operating system, sold with the image of a redline arriving at midnight and a drafted response waiting by morning.

Named customers include Cleary Gottlieb, White & Case, Linklaters and Goodwin; Mishcon de Reya went firm-wide in July 2025. For European buyers the Munich office of 31 March 2026 matters: more than 80 DACH firms and legal teams, and a CEO saying “Munich was a deliberate choice”. The sovereign alternatives are in the DACH legal AI tools guide.

The criticisms are narrower: the committee poster reported a friend quoted about $400 per user per month and “answer quality degrade once I pushed past a couple hundred pages”. Legora’s CTO has publicly credited Claude Opus 4.7 for its long-document consistency, which tells you where the model layer comes from.

CoCounsel Legal: Westlaw grounding, Deep Research Verify, and a Claude engine

On 20 August 2026 Thomson Reuters launched the next generation of CoCounsel Legal: fully agentic, built on Anthropic’s Claude Agent SDK plus a proprietary legal LLM, grounded in Westlaw and Practical Law. The headline features are Westlaw Brief Builder, Workspaces, a Drafting Agent in Word, Tabular Analysis across up to 10,000 documents and 100 questions, and Deep Research Verify, which highlights supporting passages and flags misattributions. The next generation is US-only at launch, with Canada, the UK and Australia promised for later in 2026; CoCounsel Legal itself reached the UK in January 2026 and added German and French research in June.

It has the widest footprint: ILTA found CoCounsel Core at 36% of firms and Westlaw AI-Assisted Research at 34%, ahead of Harvey’s 33%. It also has the best-documented failure rate. Stanford’s pre-registered 2024 study measured Westlaw AI-Assisted Research hallucinating on more than 34% of queries and Ask Practical Law AI on more than 17% (Stanford HAI). In Lacey v. State Farm an outline built with CoCounsel, Westlaw Precision and Gemini went into a brief unchecked and cost two firms $31,100. The committee poster’s verdict: the generations “need to consistently save rewrite time. For us, they didn’t.”

Lexis+ with Protégé: Shepard’s Verify, Workrooms and customer-held keys

Lexis+ AI became Lexis+ with Protégé in the first quarter of 2026. The 7 May 2026 expansion added Protégé Work, an orchestration layer that turns a goal into a visible plan; Agentic Drafting; a Vault of up to 100,000 documents; Workrooms shared with clients or counterparties; and bring-your-own-key encryption already deployed at Am Law 100 firms (LawNext).

The feature to understand precisely is Shepard’s Verify Trust Markers. They flag citations that cannot be verified as existing. They do not confirm that the authority supports the proposition it is cited for. That distinction is the whole game: Stanford’s researchers count “misgrounded” citations (a real case cited for a point it does not support) as hallucinations, and they are more dangerous than fabrications because a checker stops at existence; and it was LexisNexis that marketed “100% hallucination-free linked legal citations” before Stanford measured Lexis+ AI hallucinating on more than 17% of queries. It is a research product whose verification layer verifies one thing.

What the benchmarks show, and who declined to be tested

There has been exactly one public head-to-head of the big vendors: the Vals Legal AI Report of 27 February 2025, with eight participating law firms and a lawyer baseline (Vals AI).

Harvey topped five of the six tasks it entered; lawyers beat every tool on redlining, the task closest to judgement about what to change. The absences are as informative as the scores: Lexis+ AI withdrew, Legora did not take part, Harvey pulled out of EDGAR research, and when Vals ran a legal research benchmark in October 2025, Thomson Reuters, LexisNexis and vLex all declined, while plain ChatGPT scored 80% accuracy against lawyers’ 71%, according to LawNext’s write-up. The vendors most lawyers use are the least publicly measured.

Harvey’s own BigLaw Bench (Claude Opus 4.7 scored 90.9% in May 2026) is vendor-designed with no public leaderboard, and Harvey’s own diligence benchmark team admits agents’ “bias is to search efficiently, not completely.” What each number can and cannot tell you is in legal AI benchmarks explained.

What lawyers say: associates versus partners

Oz Benamram’s SKILLS survey of 130 leaders at the largest firms (March 2026) found Harvey live at 41 firms for drafting and contract review, 39 for diligence and 31 for research. His question is the one to ask your own firm: “Are your lawyers choosing the tool because it’s better for the work, or because they are used to it?” And the “wrapper” critique cuts both ways: a platform adds a legal corpus, review tables and a vendor to hold accountable, but it adds a subprocessor too. That trade is the subject of general AI versus legal-specific tools.

Pricing: what is published, what is reported

None of the four publishes a list price. Everything below is reported, with the source named so you can weigh it.

One structural point survives the noise: Harvey’s reported discounts start above roughly 100 seats, which The Legal Prompts calls “structurally optimized for exactly the firms that need discounts least”. The full scorecard, with a worked five-lawyer budget, is in what legal AI really costs.

Data residency and confidentiality terms, side by side

For a confidentiality decision the security page is the document that counts. As of September 2026:

• Harvey states it never trains on customer data, “contractually prohibits model providers from training on customer data” and “requires Zero Data Retention (ZDR) by model providers”; SOC 2 Type II, ISO 27001, 27701 and 42001; an EU region in Frankfurt. JuriScout, a German site, assesses it as “not positioned as §203-compliant at architecture level”.
• Legora lists ISO 42001, ISO 27001, SOC 2 Type 2, GDPR, bring-your-own-key encryption, and EU and US residency.
• CoCounsel states customer data is “not used to train or improve CoCounsel … or stored by OpenAI or Google”, uses zero-retention API calls, and processes requests “under the identity ‘Thomson Reuters’”.
• Lexis+ with Protégé states “We never use customer data to train AI models” and offers customer-held keys since May 2026.

Get the no-training clause in writing, ask what survives zero data retention (classifier scores, safety flags, legal holds), and ask which subprocessors sit in which region.

Which platform for which firm, and whether you need one at all

A decision tree that fits on a napkin:

1. Research-heavy litigation practice already on Westlaw or Lexis. Add the AI layer you already pay for (CoCounsel Legal or Protégé) and do the citation checks yourself. Do not buy a work platform to get a research tool.
2. Transactional practice with diligence or contract volume. Harvey if the firm lives in Microsoft 365 and wants Vault-scale sets; Legora if Tabular Review and Word-native editing fit the team and EU residency is a requirement.
3. In-house team or firm under 50 lawyers. Seat minimums make Harvey and Legora expensive or unavailable. Most get further with Claude Team or ChatGPT Business, a playbook, Gemini Notebook and a citation checker, and revisit platforms at 100 seats.

Whichever branch you are on, run a bake-off before the committee votes, on Vals’ method: identical instructions and documents to the humans and both tools, blind scoring. These three prompts turn a demo into a test.

For each contract in this set, extract into one row: Counterparty | Contract type | Effective date | Expiry and renewal terms | Change-of-control trigger (quote; consent, notice or termination right?) | Anti-assignment (quote) | Exclusivity or non-compete (quote) | Termination for convenience (who, notice period) | Governing law | Liability cap | Confidence per cell (High/Medium/Low).
Where a term is absent write NOT PRESENT. Where a clause is ambiguous, quote it and write AMBIGUOUS. Never infer a term from a similar contract in the set. Treat side letters as separate rows linked to the main agreement.

Identify the controlling authority in [jurisdiction] on [precise question]. Requirements: (a) binding authority first, then persuasive, each labelled; (b) for every case give the reporter citation, court, year, pinpoint and a one-sentence statement of what it actually holds on this point; (c) note where authority is split, distinguished or questioned since; (d) where you cannot find binding [jurisdiction] authority, write "NO VERIFIABLE LOCAL AUTHORITY FOUND" and stop rather than importing another jurisdiction; (e) end with a "Sources cited" index. I will read every pinpoint and run the citator myself.

I ran the same instruction and the same [five] documents through two legal AI platforms. Here are the outputs, labelled A and B: <output_a>...</output_a> <output_b>...</output_b>.
Score each from 1 to 5 on: completeness against my checklist <checklist>...</checklist>; accuracy of every quoted clause against <documents>...</documents>; whether every claim carries a reference I can open; and the time needed to make it client-ready.
List every statement in A or B that you cannot trace to the documents. Do not guess which platform produced which output.

A platform is worth what its output costs you to check; the Vals lawyers beat Harvey on redlining because they knew which changes mattered, and they still have to. Testing a tool blind on your own documents before anyone signs a 25-seat contract is what we practise in AI Lab for Lawyers, which is tool-agnostic for the same reason this page is: the skill is in the test, not the logo.

Where to go next: how to evaluate legal AI tools with a bake-off turns the Vals method into a firm procedure; the AI due diligence data-room workflow shows what Vault and Tabular Review do day to day; the prompt library has the prompts above ready to paste; and the tools cluster covers the general models these platforms are built on.