Head to head

Both of these submitted to the same independent evaluator, which puts them in a small group. The 2025 Vals Legal AI Report ran 200 United States legal research questions against a rubric published in advance, weighting accuracy at 50 percent, authoritativeness at 40 percent and appropriateness at 10 percent, and both vendors opted in. That shared willingness to be measured is the most important thing this page can tell you, and it is why both carry the top grade on citation accuracy. They separate on everything around the answer. Alexi holds the index's strongest confidentiality position, stating that nothing leaves the firm's private environment and that a firm's data never trains global models and never mixes with other firms. Midpage is narrower and says so, purpose built for litigators, and publishes almost nothing about oversight. Alexi is the safer institutional purchase.

These two tie on the graded grid at twelve axes in the top two bands each, which is the highest score in the index and means the separation is not about quality. It is about what you are buying. CoCounsel Legal is an answer engine sitting on a corpus its parent owns, Westlaw and Practical Law, and it grades higher on who it serves and on proof that deployments worked: named customers with figures and a commissioned 2026 Forrester study whose method a reader can assess. Harvey is a workspace that reaches into the systems you already run, and it takes the only A on integration depth in this category, with the iManage connection documented at the level an administrator needs rather than announced. Both publish independently measured accuracy. Neither publishes what happens when the output is wrong.

Two cloud native ediscovery platforms, level on the grid at nine axes each in the top two bands, separating on the kind of evidence behind them. DISCO's adoption numbers come from a regulated annual filing rather than a marketing page: 1,549 customers including 330 large customers and a 98 percent dollar based net retention rate as at 31 December 2025. A retention figure audited into a public filing is a materially stronger class of evidence than a logo wall, and DISCO also names its collection integrations, Microsoft 365 and Google Workspace, with hold and audit built in. Everlaw's answer is the strongest confidentiality posture anywhere in this index, and the only one where an outside authority has tested the generative features specifically. It is also the platform United States attorneys' offices actually run. Pick DISCO for integration and commercial evidence, Everlaw for confidentiality assurance.

The incumbent against the cloud native challenger, and the grid is closer than the market positioning suggests: Relativity in the top two bands on eleven of fifteen axes, Everlaw on nine. Relativity's advantage is reach and proof. It publishes an adoption figure a reader can actually test, 192 of the Am Law 200, names its collection integrations into Microsoft, Google, Slack and Box, and documents a platform API. It also discloses one control nothing else in this index discloses: it has opted out of Microsoft's abuse and harmful content monitoring, closing the standard route by which provider staff can reach raw inputs and outputs. Everlaw's counter is that its confidentiality posture is the strongest in the index and has been tested on the generative features specifically by an outside authority, and that it is the platform government litigators actually run. Both are defensible. The tie breaker is usually procurement.

This is the most watched head to head in legal AI and the grid does not treat it as a close one. Harvey sits in the top two bands on twelve of fifteen axes, Legora on nine, and the gap is concentrated in exactly the place a buyer should care about: what each vendor is willing to publish. Legora's product story is genuinely distinctive, a shared workspace where the matter team and the AI work in the same place, and it publishes its contract documents openly rather than hiding behind a trust page, which is rarer than it should be. But on citation accuracy it publishes no measurement, no evaluation framework and no description of its retrieval method, while Harvey submitted to an outside evaluator. On oversight, Legora asserts the principle and does not describe the mechanism. Harvey is the better documented vendor. Legora may still be the better tool for how your team actually works.

Two enterprise contract lifecycle platforms of similar age and ambition, and the grid separates them decisively: Ironclad sits in the top two bands on ten of fifteen axes, Icertis on three. The gap is almost entirely about what is currently published. Ironclad states its own training practice directly, names Salesforce and Coupa as documented integrations, and publishes a named customer with a figure. Icertis asserts capability at platform level and the specifics do not follow: the clearest statement of data handling the index could locate is a company news item from 2017, which for a platform claiming 30 percent of the Fortune 100 is a striking thing to be true in 2026. Icertis does hold one genuine advantage, and it is conceptual rather than documentary: its framing that agents act autonomously within boundaries you set is a better description of the control problem than most vendors manage.

The real question here is whose playbook you want. LegalOn ships more than 135 playbooks written by its own attorneys covering around 10,000 legal issues, so a team without settled standards gets a position on day one. Spellbook expects you to bring yours and encodes it. That difference explains almost everything else. LegalOn takes the strongest measurement disclosure anywhere in this index, publishing a 2026 benchmark of 3,282 pairwise reviews against eleven named models with the method described and the judge independently verified, and it backs deployments with named customers carrying figures. Spellbook counters on the supply chain, naming OpenAI and Anthropic and describing zero data retention precisely, where LegalOn names only its Azure OpenAI arrangement. Spellbook is the better fit for a law firm. LegalOn is built for the contracting function, lawyers or not.

These two are often shortlisted together and they are not really the same purchase. Spellbook lives inside Microsoft Word, which for transactional drafting is the deepest integration available rather than a connector alongside the work. Legora is a separate collaborative workspace the matter team moves into. On the grid Spellbook is ahead, in the top two bands on twelve of fifteen axes against nine, and it wins the axes that decide a confidentiality review: it names its model providers, describes zero data retention precisely enough that a reader can see the mechanism, and takes the top grade on model supply chain disclosure while Legora does not name its providers at all. Spellbook also explains why its architecture reduces hallucination rather than only claiming it does. Legora's counter is contractual openness and a working surface built for teams rather than individuals.

This is the widest gap on any pair page in the index and it is worth understanding why before treating it as a verdict on the products. Luminance sits in the top two bands on ten of fifteen axes, Robin AI on three, and almost every point of separation is a document Luminance publishes and Robin AI does not. Luminance publishes a benchmark built on 189,000 annotated data points, gives every customer a dedicated single tenant instance with no data co mingling, and documents access control down to the fact that its own staff cannot view customer documents. Robin AI publishes real controls too, but its training commitment is the weakest located anywhere in this index: it says customer data will not be used for training without express consent, which is a permission you can grant rather than a prohibition it has accepted. For a confidentiality review, that is the sentence that matters.

These two are shortlisted against each other constantly and they answer different buyers. Vincent AI runs on vLex's corpus of more than a billion documents across 100 plus countries with United States primary law from Fastcase, and takes the index's most complete coverage disclosure as a result. Paxton AI is the self serve option: it publishes a real price, $499 per user per month, which is the only published rate in this comparison and almost unheard of in legal AI. On the grid Vincent leads, nine axes in the top two bands against five, and the separation is confidentiality. Paxton's own footer states that communications with it are not protected by attorney client privilege or as work product, which is honest and is also the thing a firm has to reckon with. Vincent documents matter level segregation inheriting a firm's own ethical walls through Clio.

Relativity leads on the grid, eleven axes in the top two bands against eight, and the gap is mostly evidence rather than capability. Relativity publishes a checkable adoption figure and names its integrations; Reveal relies on a service provider partner account and a single attributed testimonial where deployment evidence should be. But Reveal owns two things Relativity does not. It takes the top grade on deployment and data residency, because private deployment is a real published option rather than a cloud only product with a footnote, and it describes deep integration with a customer's own authentication controls, logging frameworks and enterprise security policies. Its AI is also customer trained by design, with a control model that is unusually concrete: build a model as easily as creating a tag, train it in AI selected batches, evaluate after each round. If you need the platform inside your own perimeter, that is the pair breaker.