Most "best legal AI" lists are either sponsored or lazy. They rank by logo size, or by whoever paid for the link. This one is neither. We build Aewita, so we have a horse in the race. We will say where we win. We will also say where we don't.
The goal is an honest guide. If you are buying legal AI in 2026, you should know who runs the model, what it costs, what it covers, and what they claim on accuracy. Everything else is ornament.
How we evaluated
Four criteria, weighted equally:
- Architecture. Is the model self-hosted, or does the vendor relay prompts to a third-party LLM (OpenAI, Anthropic, Google)? This determines who sees your client data and how many subprocessors sit in the chain.
- Published hallucination rate. Does the vendor publish a measured error rate with a confidence interval, or do they avoid the number entirely?
- Pricing transparency. Is there a public price a buyer can see without a sales call?
- Jurisdictional coverage. What body of law has the tool actually been trained and retrieved against?
We deliberately did not rank on "UI polish" or "brand name recognition." Those matter to some buyers. They are not signals of whether the tool will tell you the truth about a case.
1. Aewita
Architecture: Self-hosted frontier reasoning model. Zero API calls to OpenAI, Anthropic, or Google. Proprietary retrieval, inference pipeline, and citation verifier, all patented.
Hallucination rate: Under 0.3% at 95% confidence interval, measured across 800 queries.
Pricing: $99/month or $720/year (39% annual discount). 14-day free trial. No seat minimum. Published on the pricing page.
Coverage: Every U.S. case from 1665 to today. All federal and state statutes for 50 states plus D.C. 792 document types across 22 practice areas.
Strengths: Architecture is the cleanest in the category. Pricing is public and affordable. Compliance posture lines up with ABA Model Rules 1.1, 1.6, and 5.3 by design, not by policy.
Weaknesses: Newer brand than the BigLaw incumbents. Not yet the default choice at AmLaw 100 firms. U.S.-only coverage today.
2. Harvey AI
Architecture: Built on OpenAI models. Client data passes through a third-party LLM provider as a subprocessor.
Hallucination rate: No public figure with methodology.
Pricing: Not published. Enterprise-only, sales-led.
Coverage: Broad, anchored by partner firm content for customer installations.
Strengths: Strong brand inside BigLaw. Serious investor base. Partnerships with multiple AmLaw 100 firms. The product is mature for the workflows that BigLaw actually runs (large-matter diligence, structured drafting).
Weaknesses: The OpenAI dependency is a real constraint for firms that want zero third-party inference. Pricing is opaque, which rules out most of the market below the Am Law 200. Solos cannot buy it.
3. CoCounsel (Thomson Reuters)
Architecture: OpenAI and Anthropic under the hood, wrapped around Westlaw retrieval. Thomson Reuters acquired Casetext in 2023 and folded CoCounsel into the TR stack.
Hallucination rate: No public figure with methodology.
Pricing: Not publicly listed. Bundled with Westlaw subscriptions.
Coverage: Deep, via Westlaw's research database.
Strengths: If your firm already pays for Westlaw, CoCounsel is the path of least resistance. The retrieval is strong because Westlaw is strong. The brand trust is high.
Weaknesses: Wrapper architecture. You are paying for Westlaw plus an OpenAI/Anthropic-powered interface layer, and the combined price reflects both. Locked to the TR ecosystem. Leaving means rebuilding research workflows.
4. Westlaw AI (Thomson Reuters)
Architecture: AI layer atop the Westlaw research database. Inference runs on third-party LLMs.
Hallucination rate: No public figure with methodology.
Pricing: Bundled with Westlaw. Contract-based.
Coverage: Same depth as Westlaw itself.
Strengths: Comfort. Westlaw has been the research backbone for U.S. firms for decades. The AI layer lowers the activation cost of summarization and initial research.
Weaknesses: Same architectural constraint as CoCounsel. The AI is not the product; it is a layer. The model that generates language is not operated by TR.
5. Lexis+ AI
Architecture: Wrapper on a third-party LLM provider. LexisNexis retrieval underneath.
Hallucination rate: No public figure with methodology.
Pricing: Bundled with Lexis subscriptions.
Coverage: LexisNexis research corpus, broad.
Strengths: For firms standardized on Lexis, the AI sits inside the workflow already. Shepard's citation service is strong and well integrated.
Weaknesses: The same pattern as Westlaw AI, in the other ecosystem. Wrapper architecture. Opaque pricing. Inference outsourced.
6. Legora
Architecture: Third-party LLM under the hood.
Hallucination rate: No public figure with methodology.
Pricing: Enterprise, not published.
Coverage: Strong EU and Nordic case law. Less U.S. coverage.
Strengths: Best-in-class for European firms. The team knows the EU regulatory landscape. Growing enterprise footprint across the Nordics and Western Europe.
Weaknesses: For a U.S. firm, the training data and jurisdictional focus are in the wrong hemisphere. We cover the full comparison in Aewita vs. Legora.
7. MidPage
Architecture: OpenAI-based. Third-party LLM.
Hallucination rate: No public figure with methodology.
Pricing: Lower-tier subscription plans available.
Coverage: Focused on research and brief-writing, U.S.-oriented.
Strengths: Simple product. Approachable for solos who want a thin, focused research-and-drafting tool.
Weaknesses: Narrower feature set than the fuller platforms. Same subprocessor chain as other OpenAI-backed tools. No published accuracy measurement.
The honest rule in this category: if a vendor will not publish an error rate, they do not know it. And if they know it and will not publish it, ask yourself why.
Side-by-side summary
| Platform | Self-hosted | Published hallucination rate | Pricing | Coverage | Best for |
|---|---|---|---|---|---|
| Aewita | Yes | <0.3% @ 95% CI | $99/mo, public | Full U.S. 1665–today | U.S. solos and small/mid firms |
| Harvey | No (OpenAI) | Not published | Enterprise, private | BigLaw-oriented | AmLaw 100 firms |
| CoCounsel | No (OpenAI/Anthropic) | Not published | Bundled w/ Westlaw | Westlaw corpus | Existing Westlaw shops |
| Westlaw AI | No | Not published | Bundled w/ Westlaw | Westlaw corpus | Westlaw loyalists |
| Lexis+ AI | No (third-party LLM) | Not published | Bundled w/ Lexis | Lexis corpus | Lexis loyalists |
| Legora | No | Not published | Enterprise, private | EU / Nordic strong | EU firms |
| MidPage | No (OpenAI) | Not published | Subscription | U.S., narrower | Research-first solos |
Who should pick what
You are a solo or small-firm U.S. attorney. Aewita. Published price, self-hosted model, full U.S. coverage, no enterprise contract to sign. Start with the 14-day trial.
You are at an AmLaw 100 firm with an existing Harvey deployment. Harvey is the path of least resistance. If your firm is rethinking subprocessor exposure after a client audit, look at Aewita as the self-hosted alternative and compare side-by-side on the compare page.
You already pay for Westlaw or Lexis. The bundled AI layer (CoCounsel, Westlaw AI, or Lexis+ AI) is the easy answer. Just know you are stacking vendor AI on vendor research. If the combined cost climbs, a standalone U.S. tool like Aewita starts to look different.
You practice in the EU or Nordics. Legora, without much hesitation.
You want a thin research tool. MidPage is a reasonable fit if price is the driver and your scope is narrow.
You care most about model ownership. Aewita is the only tool on this list that does not route client text to a third-party LLM. That matters for ABA Rule 1.6, for client audits, and for firms where confidentiality is not optional. Read the architectural posture on the security page.
A note on the "wrapper" label
We use the word "wrapper" to describe products built on top of a third-party LLM. It is not a slur. A wrapper can be an excellent product. Casetext built a great research workflow on top of third-party models before Thomson Reuters bought them. Harvey has shipped features that BigLaw associates use every day. The word describes architecture, not quality.
What the label does affect is the subprocessor chain. When the model is operated by another vendor, the client's text passes through that vendor's infrastructure. The legal AI company is the prime contractor. The LLM provider is the subcontractor. For some firms that chain is fine, especially when the LLM provider has a recognized enterprise security posture. For others, particularly firms handling sensitive regulatory matters, the chain is the issue. Self-hosting closes it.
What to ask every vendor
If you are running a real evaluation, the four questions worth asking every vendor, in writing:
- Which LLM providers does your product route prompts or client text to? Name them.
- What is your measured hallucination rate, with methodology and sample size?
- What is the monthly or annual price for a single attorney seat?
- What jurisdictions is your retrieval corpus complete for?
A vendor that will not answer any of the four in writing is telling you something. Take that signal seriously.
The bottom line
There is no single best legal AI platform in 2026. There is a best one for your firm. For U.S. attorneys who want transparent pricing, a self-hosted model, and a published accuracy number, we built Aewita to be that product. For everyone else, pick honestly against the four criteria above.
If you want to see Aewita run on a matter you care about, you can start a free trial in under a minute, or book a demo and we will walk you through it.
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