Claude vs. ChatGPT for Lawyers: Which AI Is Better for Legal Work in 2026?

For lawyers, Claude vs. ChatGPT is a task-by-task split rather than a single winner, and neither tool wins legal work outright: Claude handles long-document contract review and precedent-matched drafting better, and ChatGPT handles fast targeted retrieval and high-volume standardized drafting better. Both now search the live web, both edit inside Microsoft Word with tracked changes, both start at roughly $20 per user per month, and neither checks a clause against your playbook nor against market language.

Commercial teams often run both, and the decision that matters is which tool touches which document, a call that lands differently for a law firm running third-party paper at volume than for a single-matter user.

Claude vs. ChatGPT for Lawyers at a Glance

Long-document recall and targeted retrieval point in different directions within the same task set: a first read of a 90-page master services agreement (MSA) rewards the model that keeps every defined term in view, and a question about one clause inside it rewards the model built to pull that provision out quickly.

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How Claude and ChatGPT Approach Legal Tasks Differently

Claude and ChatGPT differ on legal tasks because they were trained differently: Claude is trained against a written set of principles and defaults to caution, and ChatGPT is trained on ranked human feedback and defaults to fluency and breadth. That difference shows up in the first draft of a clause, in how much a model speculates about risk, and in how often it hedges.

The training choices predict which tool will refuse to characterize an ambiguous indemnity and which will characterize it confidently, and a lawyer reviewing output needs to know which habit is in front of them. Fabricated output is a live risk on both sides, and the failure pattern differs enough that it is worth understanding how AI hallucination shows up in legal work before either tool touches a filing.

How Claude and ChatGPT Were Trained

Primary Task Performance Differences

• First read of a long agreement: Claude holds the defined terms and cross-references across the full document, so the summary it produces is internally consistent.
• Targeted retrieval: ChatGPT finds a specific provision inside a dense document quickly, which suits a question about one clause rather than a whole file.
• Research with live sources: both tools search the web and return citations; neither reaches a licensed legal research database.
• Volume drafting: ChatGPT holds a rigid format instruction across dozens of documents with less drift.

Professional Output Characteristics

• Register: Claude's default prose is closer to a transactional lawyer's register; ChatGPT's needs prompting into formal legal English.
• Hedging: Claude hedges more, which means fewer wrong characterizations and more sentences a lawyer has to firm up.
• Structure: ChatGPT produces more aggressive headings, bullets and summaries than a client deliverable usually wants.
• Consistency across a session: Claude tracks a position taken earlier in a long session; ChatGPT is more likely to re-answer the same question differently.

Claude vs. ChatGPT for Contract Review and Document Analysis

For contract review, Claude vs. ChatGPT is close on capacity and different in behavior: both models read roughly a million tokens in a single pass, Claude holds defined terms consistently across a long agreement, and ChatGPT retrieves a specific provision out of a dense document faster. Neither model verifies that what it found is the whole picture.

Capacity has stopped being the differentiator. The question a reviewer should ask is no longer whether the agreement fits, but whether the model keeps the agreement's internal logic in view while it reads, which is a different property from raw context length and the one that determines whether a review is usable.

Claude's Context Window for Long Agreements

All four current Claude models read 1M tokens of input in a single request, which covers a full commercial agreement with its schedules, exhibits and the prior-round markup alongside it. The practical benefit is consistency: when a definition in Schedule 2 contradicts the body, Claude tends to surface the conflict rather than answer from whichever passage it saw last. That behavior is what makes it useful for reading and analyzing a long legal document in one pass rather than in chunks.

ChatGPT's Context Window for Long Agreements

GPT-6 Astra reads 1.05M tokens of input, marginally more than the Claude line, with a knowledge cutoff of April 30, 2026. The retrieval behavior inside that window is the difference worth knowing: ChatGPT is strong at pulling the one provision a question asks about, and comparatively weaker at noticing that the provision it pulled is inconsistent with a definition thirty pages earlier.

Capacity Across a Multi-Document Matter

One million tokens covers essentially any single commercial agreement, including the long-form credit agreements and asset purchase agreements where length was previously the binding constraint. Capacity still matters in the multi-document matter: a diligence set of forty third-party contracts does not fit in either window at once, and both tools therefore read it in batches. Batching reintroduces the problem the large window solved, because a position taken in batch one is not visible in batch three unless someone carries it across by hand.

Managing Hallucination Risks in Contract Review

• Invented clause numbers: Both tools can cite a section that does not exist in the document in front of them; check every reference back to the text.
• Confident mischaracterization: A clause summarized as a mutual obligation when it runs one way is a more dangerous error than an obvious fabrication, because it reads correctly.
• Silent omission: Neither tool reliably tells you what it did not find, so an absent limitation of liability can pass unremarked.
• Stale general knowledge: A model's training data has a cutoff date, so a statutory or regulatory change after that date is not in the model unless it is supplied.

Claude vs. ChatGPT for Legal Research and Citation Accuracy

For legal research, neither Claude nor ChatGPT is a citation source: ChatGPT and Claude both search the live web and return real links, neither one reaches a licensed research database, and both can produce a case name that does not exist. The verification duty does not move.

The distinction that matters in practice is between a fabricated authority and a real authority described wrongly. The first is caught by opening the link. The second survives a link check, which is why reading what an AI tool says a case holds against the case itself is a separate step from confirming the citation resolves.

How Each Tool Sources Legal Authority

Both tools now run an integrated web search and return links alongside their answer, which is a meaningful change from when only one of them could reach the live web. Claude additionally reasons over whatever documents are supplied directly in the conversation, which suits a question anchored to a specific filing rather than the open web. Neither one returns a citator signal telling you whether an authority is still good law.

What Happened in Mata v. Avianca

The sanctions case that practitioners cite is still the clearest illustration of the failure, and courts have continued to sanction unverified filings since. In Mata v. Avianca, counsel filed a brief containing cases that ChatGPT had fabricated, and the court sanctioned the lawyers after the fabrications were identified. The failure was not that a tool invented a case, which tools do, but that nobody opened the citations before filing.

The Verification Duty Does Not Move

A lawyer's duty of candor to the tribunal is set by ABA Model Rule 3.3, and no feature of either tool shifts it. The verification sequence is the same whichever tool produced the draft:

• Resolve every citation in a licensed research database, not by asking the model whether the case is real.
• Read the authority, not the model's summary of it, before relying on the proposition.
• Check the authority is still good law, which neither tool reports.
• Confirm the quotation word for word, because paraphrase presented inside quotation marks is a common failure.

Claude vs. ChatGPT for Contract Drafting and Writing Quality

For legal drafting, Claude vs. ChatGPT splits on register: Claude mirrors a precedent's tone and handles layered exceptions with fewer stylistic passes, and ChatGPT produces standardized clauses and holds rigid formatting instructions across a high volume of documents.

A mismatched register is worth fixing before a clause reaches the counterparty, and the library of prompts that push ChatGPT into a formal drafting register is the practical fix rather than a reason to avoid the tool.

Where Claude Drafts Better

Claude is stronger where the drafting task is to match something: a precedent clause, a counterparty's defined-term conventions, or the tone of a signed agreement from the same relationship. It also handles layered conditionality with fewer passes, which matters in a clause carrying an obligation, two carve-outs and a proviso. The standard demonstration is the shift from "best efforts" to "commercially reasonable efforts": Claude tends to carry that change through every dependent clause in the document, including the remedies section, rather than changing the phrase where it appears and leaving the rest inconsistent.

Where ChatGPT Drafts Better

ChatGPT is stronger on volume and standardization. Given a format instruction and a set of variables, it produces thirty non-disclosure agreements (NDAs) that follow the same structure, with less drift between the first and the last than Claude shows. It is also the better tool for generating alternative phrasings quickly when the drafting problem is to find a formulation the other side will accept.

Four Drafting Scenarios Compared

• Marking up third-party paper: Claude, because the task is to match and respond to an existing document's conventions.
• Templating a standard form: ChatGPT, because the task is consistent output against a fixed instruction.
• Negotiating alternative language: ChatGPT for breadth of options, then Claude to check each option against the rest of the agreement.
• Drafting from a long precedent set: Claude, because the precedents have to stay in view while the new clause is written.

Which Claude and ChatGPT Models to Use for Legal Writing

Anthropic recommends Claude Opus 5.5 as the default for most work, which makes it the starting point for legal writing, with Claude Fable 5.1 reserved for the longest reasoning work; on the ChatGPT side, GPT-6 Astra is currently the flagship and GPT-6.1 Sol the cheaper option for routine drafting. All four current Claude models read 1M tokens, and GPT-6 Astra reads 1.05M.

For confidentiality the model selector matters far less than the account tier, because the terms governing whether your client documents train a model are set by the plan you bought, not by the model you picked inside it.

The current line on each side is published by the vendor, and Anthropic's model documentation names Claude Opus 5.5 as the recommended starting point for most workloads rather than as a legal-specific recommendation.

The Current Claude Models

• Claude Opus 5.5: the recommended default for most workloads, and the starting point for legal writing.
• Claude Sonnet 5.5: the balanced option, suited to routine review and drafting at lower cost than Claude Opus 5.5.
• Claude Haiku 5.5: the fastest and cheapest of the line, suited to high-volume classification and triage rather than drafting.
• Claude Fable 5.1: positioned for demanding reasoning and long-horizon agentic work, which in a legal context means a long multi-step diligence pass rather than a single clause.

The Current ChatGPT Models

• GPT-6 Astra: currently the flagship, with a 1.05M token context window and a knowledge cutoff of April 30, 2026.
• GPT-6.1 Sol: positioned as the balance of intelligence and cost, which makes it the sensible default for routine drafting volume.
• GPT-6.1 Terra: positioned for visual reasoning and clear natural writing, relevant when the source document is a scan or an image.
• GPT-6 Luna: positioned for high-volume, cost-sensitive work such as first-pass classification across a large document set.

The cutoff date recorded in OpenAI's model documentation means the model does not know about a statutory amendment or a rule change after it, so anything turning on current law has to be supplied to the model or looked up independently.

Can Claude and ChatGPT Work Inside Microsoft Word?

Claude and ChatGPT both work inside Microsoft Word. Claude for Microsoft 365 edits a Word document with tracked changes, responds to comment threads and applies company styles, and ChatGPT ships a Word extension alongside Excel, PowerPoint and Google Sheets. Neither one arrives with your playbook positions.

The copy-paste step between a browser and a document is no longer the friction it was, which moves the real question from where the tool runs to what reference it brings with it, the difference that separates Claude for Word from a purpose-built contract platform.

What Claude for Microsoft 365 Does in a Word Document

• Edits with tracked changes, so a lawyer accepts or rejects each proposed revision in the normal review flow.
• Responds to comment threads in the document rather than in a separate chat window.
• Applies company styles and inherits the document's existing heading styles and numbering.
• Confirms before acting, because nothing saves or sends until the user approves it.
• Requires a paid plan, because Claude for Word, Excel and PowerPoint are available only on paid Claude plans.

The Word, Excel and PowerPoint integrations are generally available on all paid Claude plans and excluded from the free tier, with Outlook currently in beta, which Anthropic's Microsoft 365 page sets out alongside the rest of the feature set.

What ChatGPT's Word Extension Does

• Covers Word, Excel, PowerPoint and Google Sheets as a single extension family rather than separate add-ins.
• Varies by plan, with the extensions limited on the free tiers and fully included higher up.
• Works on the document in place, which removes the copy-paste round trip that used to sit between a chat window and a draft.

Extension access tracks the subscription on this side too: the Word, Excel, PowerPoint and Google Sheets extensions are limited on the Free and Go tiers, included on Plus and expanded on Pro, as OpenAI's published plan comparison records per tier.

What separates a usable in-document assistant from a liability is how the markup gets handled:

• Check the author name on the markup, because AI-proposed revisions land under the signed-in user's name and a counterparty reads them as the firm's own revisions.
• Review each suggestion in its own context, because a bulk accept approves substantive changes alongside the formatting ones without surfacing them.
• Run Compare Documents on the returned clean copy, against the last marked-up version, which is what catches language inserted between rounds.

Internal comments are the other exposure. Flag them with a searchable keyword and run a word search before the document leaves the firm, because the document inspector does not always surface a comment thread, and confirm that any AI-proposed language uses the agreement's own defined terms rather than a synonym.

What an in-document editor does not remove is the reference problem. Both tools can now propose a redline where the agreement lives, and neither can tell you whether the position it proposed is the one your organization takes, because no part of that history is available to them.

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Claude Projects vs. ChatGPT Custom GPTs for Legal Teams

Claude Projects and ChatGPT Custom GPTs solve different halves of the same problem for legal teams: a Project holds a knowledge base of precedents the model reads on every prompt, and a Custom GPT holds standing instructions that fix how the model behaves.

A Project is the better fit when the reusable asset is content. A team puts its executed agreements, its preferred clause formulations and its negotiation history into the Project, and every subsequent question is answered against that material. A Custom GPT fits the opposite case, where the reusable asset is behavior rather than content: a "Redline Associate" that always reviews from the buyer's perspective, always flags uncapped liability, and always returns findings in a fixed order. The two constructs split cleanly: the precedent library sits in a Claude Project built for legal work, and the standing instructions sit in a ChatGPT Custom GPT.

What decides whether either construct holds up past a pilot is who owns it after the pilot ends:

• Who maintains it: a Project's knowledge base is only as current as the last person who updated it, so the owner has to be a named knowledge-management lead or practice-group partner rather than nobody in particular.
• What happens when a precedent is superseded: a superseded agreement sitting in the knowledge base is read with the same weight as the current form, and the model does not know which is which.
• Whether the reasoning is recorded: neither construct records why a position was taken, so a team inherits the clause language without the rationale behind it.

Both constructs reproduce what was done before, and neither preserves the judgment that produced it, a gap that carries into ChatGPT's legal workflows for commercial teams as much as into Claude's.

Claude for Legal Work: Pros and Cons

Claude's strengths for legal work are long-document recall, a cautious default register and tracked-changes editing inside Word; its limits are the absence of a native legal research index and of any benchmark against market contract language.

Pros

• Holds a full agreement in view, including its schedules and exhibits, rather than reading it in chunks.
• Drafts in a register closer to transactional legal prose, which reduces the number of stylistic passes before a client sees the document.
• Edits in Word with tracked changes, including comment threads and company styles, on all paid plans.
• Carries a change through dependent clauses, which is the behavior that catches an inconsistent remedies section.
• Flags ambiguity rather than resolving it, which produces fewer confident mischaracterizations for a reviewer to catch.
• Searches the live web and returns inline citations, available on every Claude plan including Free.

Cons

• Administrative controls sit at the Enterprise tier, so automated user provisioning and custom retention are not available on Team.
• No legal-specific retrieval over case law, and no citator signal on whether an authority is still good.
• No playbook or market comparison, so the tool cannot say whether a clause is standard for this kind of deal.
• Claude for Microsoft 365 requires a paid plan, so the Word integration is not available to evaluate on the free tier.
• A cautious default produces more hedged first drafts, which a lawyer then has to firm up into a position.

ChatGPT for Legal Work: Pros and Cons

ChatGPT's strengths for legal work are live web search, fast targeted retrieval and a deep library of Custom GPTs; its limits are a conversational default register, confident citation errors and no awareness of your firm's precedent library.

Pros

• Searches the live web and returns links alongside its answer, which keeps a research question in the same window as the drafting work.
• Retrieves a specific provision quickly from a dense document, which suits a single-clause question.
• Holds a format instruction across volume, producing consistent output over dozens of documents.
• Carries a large Custom GPT library, so a standing review instruction is quick to build and share across a team.
• Ships Word, Excel, PowerPoint and Google Sheets extensions, so the drafting work happens in the document.

Cons

• A conversational register that needs prompting into formal legal English before output is client-ready.
• Web search returns real links, not primary legal authority, so it is a starting point rather than a research platform.
• Confident citation errors, including real authorities described wrongly, which survive a link check.
• No playbook or market comparison, so standardness is still a judgment the lawyer supplies.
• The Word extension is limited below Plus, and Plus bills monthly only, so there is no annual discount at the entry tier.

Claude and ChatGPT Confidentiality and Data Security for Client Files

Confidentiality with Claude and ChatGPT turns on the account tier rather than the model: the paid business and enterprise tiers of both tools exclude customer content from model training by default, and consumer accounts carry looser retention terms that ABA Formal Opinion 477R's factor-based test requires you to weigh before any client file is uploaded.

The controls that matter are attached to the paid business and enterprise tiers on both sides, which makes the account the operative decision for IT, security review and procurement alongside whoever holds the budget, rather than for the individual lawyer. A lawyer on a personal subscription is working under consumer terms regardless of the firm's own policy. What Anthropic does with customer data is set out in the Anthropic privacy policy, which is the document to read before a client file goes into a personal plan.

Where Client Data Goes on Each Tier

Those are the controls to name when someone asks whether a tier is suitable for client files. Both vendors exclude customer content from model training by default on their paid business tiers and put single sign-on at that level, and both hold the rest of the administrative layer at the enterprise tier: automated user provisioning (SCIM, the standard for creating and removing user accounts across systems), audit logging, custom retention and network allowlisting. Anthropic's trust documentation sets out that detail for Claude's Enterprise tier, and the free and personal tiers on both sides carry none of this administrative layer. Naming the specific control is more useful than a general assurance, because it is the control that goes into the firm's own policy, which is also where OpenAI's enterprise privacy page places the same detail for ChatGPT's Enterprise tier.

What ABA Formal Opinion 477R Requires

A tier's administrative controls answer the account question; they do not answer the ethics question, which is a separate analysis under professional responsibility rules. The governing authority here, ABA Formal Opinion 477R, does not set a bright-line standard for transmitting client information electronically. It sets a factor-based test, and the factors a lawyer weighs include the sensitivity of the information, the likelihood of disclosure if additional safeguards are not used, and the cost and difficulty of implementing those safeguards. Applied to an AI subscription, the analysis turns on what the account terms permit rather than on whether AI is involved at all.

Privilege and work product sit alongside that analysis rather than inside it, and the account tier is again the operative fact: material uploaded under consumer terms is handled differently from the same material under a business agreement. The firms working through the move from one general-purpose tool to another are finding that swapping models without changing the account tier leaves the confidentiality position where it started.

Workflows to Keep Off General-Purpose Tools

• Anything under a client confidentiality undertaking that the account terms do not cover.
• Material subject to a protective order or a third-party non-disclosure agreement with a named-recipient restriction.
• Personal data under a processing agreement that does not name the vendor as a permitted sub-processor.
• Unredacted documents in a matter with a government counterparty, where the transmission record itself may be discoverable.
• Material under an outside-counsel-eyes-only designation, which by its terms cannot be shared with a vendor the client has not approved.

Claude vs. ChatGPT Pricing for Legal Teams

Claude vs. ChatGPT pricing for legal teams lands within a few dollars either way: Claude Pro is $17 per month on an annual plan and $20 billed monthly, ChatGPT Plus is $20 per month, and both vendors price a standard business seat at $20 per seat per month billed annually.

Those two levels are the ones that decide a rollout, so the comparison below carries them and nothing else. The wider range matters only when a team is sizing up from a pilot.

Cost Structure for Professional and Team Tiers

Above that entry level, Anthropic's pricing page shows Claude running from $17 per month on an annual Pro plan up to $200 per month on Ultra, with premium Team seats priced at $100 per seat per month, and neither figure carries a seat minimum below Enterprise. Team pricing applies from two seats upward.

ChatGPT Plus is $20 per month, billed monthly with no annual option. The business tier is ChatGPT Business rather than the former Team plan, at $20 per seat per month billed annually with a $100 per seat premium option.

Neither vendor publishes a figure for the top business tier, which is where a firm-wide rollout usually lands. Claude Enterprise carries a twenty-seat self-serve minimum with usage priced on top of the seat, and ChatGPT Enterprise is quoted on request rather than listed.

Spellbook prices differently, with custom per-seat pricing set by team size and a free seven-day trial, so it is not a line item in the comparison above. The reason to know the general-purpose numbers is that they are the baseline a contract platform is measured against, and at roughly $20 per lawyer per month the baseline is low enough that cost is rarely what decides the question.

When to Use Claude, ChatGPT, or a Contract Platform

Claude and ChatGPT stop short on commercial contract work at the point where a judgment call needs a reference: neither tool knows the positions in your playbook or how a clause compares with the wider market, so the question of what counts as standard comes back to you.

The gap is not where the tool runs, because both tools now run in the document. The gap is what the tool can look up when it has to decide whether a clause is acceptable.

• Long-document first read and precedent-matched drafting: Claude, because the task rewards holding the whole agreement and its precedents in view.
• Fast targeted retrieval and high-volume standardized drafting: ChatGPT, because the task rewards quick pulls from a dense document and consistent output at volume.
• Playbook enforcement, market comparison and multi-document review: a contract platform, because each of those tasks requires a reference the general-purpose tools do not hold.

What Breaks When the Draft Leaves the Document

What breaks is the record of the organization's positions. An in-document assistant proposes a revision from general knowledge of how such clauses are usually written, which is not the same as knowledge of how this team writes them, so every judgment call gets re-litigated from scratch on the next matter. The team that agreed a position on liability caps in March has no way to make that agreement visible to the tool reviewing the next agreement in November, and the position is re-argued or quietly abandoned.

That is a reference problem rather than an interface problem, and it does not improve as the models improve. A better model produces a better guess about market-standard language; it does not produce knowledge of what this team accepted last quarter and why.

Running Both Tools and Cross-Checking Outputs

Teams that run Claude and ChatGPT together use the disagreement as a signal. When both tools characterize a clause the same way, the characterization is usually safe to act on; when they diverge, the divergence marks the provision a lawyer should read closely. Running both costs two subscriptions and surfaces disagreement a single tool cannot.

Running Claude or ChatGPT does not usually mean giving up the other: a team that reaches for one for drafting and research keeps the second tool in reserve, and adds a purpose-built platform only where the work is contract review at volume. Neither general-purpose tool gets replaced outright, because the research and drafting tasks it handles stay useful; a contract platform gets added at the point where the work needs a reference.

Where a Contract Platform Fits Alongside Both Tools

Spellbook is an AI platform for commercial legal work, built for the point in that sequence where the reference is what matters. The Review feature surfaces risks and proposes redlines as native Word tracked changes, so the output lands in the same review flow a lawyer already uses. The Draft feature writes a first pass against the firm's own precedent language rather than a model's general sense of standard drafting. The Playbooks feature holds the firm's negotiating positions, so a reviewer checks a clause against what the firm actually accepts rather than against an AI's best guess. The Compare to Market feature benchmarks clause language against market data drawn from 20M+ real contracts across 270+ clause types, which is the comparison neither general-purpose tool can make. Spellbook's Associate feature handles multi-document review where a diligence set runs past what a single session holds. Each of those helps the team identify what needs attention; the position taken is still the lawyer's.

Frequently Asked Questions About Claude and ChatGPT for Lawyers

Is Claude or ChatGPT better for legal work?

Claude and ChatGPT answer different questions for lawyers: Claude reads a long agreement and holds its defined terms, and ChatGPT searches the live web and drafts standardized documents at volume. The split by task is the useful answer, not a ranking.

Is Claude good for reviewing contracts?

Claude reads a full commercial agreement in one pass and flags the clauses it considers unusual, which makes it useful for a first read. It does not compare those clauses with your playbook positions or with market language.

Is there a legal version of ChatGPT?

There is no legal edition of ChatGPT itself. The category distinction is between general-purpose assistants, which are trained for broad use and adapted to legal work by the lawyer prompting them, and purpose-built legal tools, which are built against legal data and workflows and carry references a general assistant does not have.

Can lawyers use Claude and ChatGPT together in one workflow?

Yes, and it is a common arrangement. One pattern sends the first read of a long agreement to Claude, research questions and volume drafting to ChatGPT, and uses a disagreement between the two as a flag that a provision needs a lawyer's attention.

What legal work should not go through general-purpose AI tools?

Work under a confidentiality undertaking the account terms do not cover, material under a protective order, personal data governed by a processing agreement that does not name the vendor, and unredacted documents in matters with a government counterparty. The constraint is the account tier and the terms attached to it, not the task.

How do legal teams integrate AI into contract workflows?

The integration that holds up starts with a narrow task that has a verifiable output, such as summarizing third-party paper or producing a first draft from a template, and adds further tasks once the review step is reliable. What lasts is a person checking the output at a defined point, not at the reviewer's discretion.

What are the common failure modes when lawyers use general-purpose AI?

Fabricated citations, real authorities described wrongly, silent omission of a missing protection, and inconsistency across a long document. The second and third are the dangerous ones, because both read correctly and neither is caught by confirming that a link resolves.

Do courts and regulators accept AI-assisted legal work?

Courts have generally enforced the existing duty to verify what is filed rather than barring the use of AI. The sanctions decisions have turned on unverified citations rather than on the use of a tool.

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A Spellbook free trial runs contract review inside the Microsoft Word document where your agreements already live, from the first redline to the final signature. Try Spellbook free to see the Review feature propose redlines as tracked changes on an agreement your team is working on this week, and to see how a clause compares with market language before you decide whether it is standard.