Claude Opus 4.6 vs Sonnet 4.6 for Lawyers: Which Model Should You Use? (2026)

Last updated: February 23, 2026 · 22 min read

Anthropic now offers two models that matter for legal work: Opus 4.6, the flagship released February 5, 2026, and Sonnet 4.6, the mid-tier model released February 17, 2026. One costs five times more than the other. The question every attorney is asking: does the premium model actually produce better legal work?

The answer is more nuanced than Anthropic's marketing suggests. Opus 4.6 scored 90.2% on Harvey's BigLaw Bench — the highest legal reasoning score of any Claude model. But Sonnet 4.6 is so close on general benchmarks that developers who tested it preferred it over the previous flagship Opus 4.5 59% of the time.

We tested both models on five real legal tasks that attorneys perform daily. Here's exactly when Opus is worth the premium, when Sonnet is the smarter choice, and how this affects your AI budget.

This article builds on our Claude vs Gemini comparison for lawyers, where we established Claude as the stronger model for legal document work. Now we're going inside the Claude family to find the best model for your practice.

Quick Specs: Opus 4.6 vs Sonnet 4.6 at a Glance

Before diving into legal-specific testing, here's what you're choosing between:

Two things jump out. First, the GDPval-AA benchmark — which measures performance on real-world professional tasks in finance, legal, and other domains — actually favors Sonnet at 1,633 Elo versus Opus's 1,606. Second, the ARC-AGI-2 gap (68.8% vs 58.3%) is the largest differential between the two models and measures abstract reasoning on novel problems — exactly the kind of thinking complex legal analysis requires.

But benchmarks aren't legal work. Let's see what happens when you hand these models actual attorney tasks.

Test 1: Contract Drafting — NDA with Non-Standard Provisions

We asked both models to draft a mutual NDA for a fintech company sharing proprietary trading algorithms with a potential acquirer, including carve-outs for regulatory disclosures and a 36-month sunset clause that converts to perpetual protection for trade secrets specifically.

This is deliberately harder than a standard NDA. It requires understanding the interaction between sunset provisions and trade secret law, crafting regulatory carve-outs that work under both SEC and state securities law, and handling the mutual-but-asymmetric dynamic where the target company's algorithm IP needs stronger protection. We chose this test because it mirrors the kind of complex pre-acquisition work that increasingly lands on solo practitioners' desks — and where AI quality directly affects deal outcomes.

Opus 4.6 Result

Opus produced a comprehensive 12-section NDA that correctly identified the tension between time-limited confidentiality and the Defend Trade Secrets Act's (DTSA) indefinite protection framework. It included a well-drafted carve-out for SEC, FINRA, and state securities regulators, with specific language addressing whistleblower protections under Dodd-Frank. The trade secret conversion clause was technically precise, tying perpetual protection to the legal definition under the DTSA and Uniform Trade Secrets Act. Opus also added a detailed data room protocol section that wasn't requested but is standard practice in pre-acquisition due diligence — a sign that the model understood the broader transaction context, not just the narrow drafting prompt.

Sonnet 4.6 Result

Sonnet's draft covered the same core territory — 10 sections, competent regulatory carve-outs, and a working sunset-to-perpetual conversion mechanism. The DTSA reference was correct, and the overall structure followed standard NDA conventions. However, the regulatory carve-out was narrower (SEC only, missing FINRA and state regulators), and the trade secret conversion clause used a simpler formulation that didn't address the nuance of what happens if information loses trade secret status during the 36-month period. The draft was clean and ready for attorney review, but an experienced M&A attorney would need to add the missing regulatory provisions manually.

Test 2: Legal Research Memo — Multi-Jurisdictional Question

We asked both models to research whether a non-compete clause signed in Delaware can be enforced against an employee who moved to California and now works remotely for a competitor. We wanted a research memo covering choice-of-law analysis, recent case developments, and practical recommendations.

This question sits at the intersection of contract law, employment law, and conflicts of law — three areas where the answer genuinely depends on which cases you cite and how you analyze the choice-of-law framework. It's also a question with real-world stakes: post-pandemic remote work has made cross-state non-compete disputes increasingly common, and the legal landscape is shifting rapidly with multiple states revising their statutes.

Opus 4.6 Result

Opus delivered a structured memo that correctly identified California Business and Professions Code § 16600 as the core obstacle, analyzed the choice-of-law question under both the Restatement (Second) of Conflict of Laws and Delaware's own choice-of-law framework, and discussed the Application Group v. Hunter Group line of cases establishing California's strong public policy against non-competes. Opus flagged the 2023 California amendments expanding § 16600 protections and noted that Delaware courts have increasingly respected California's policy in remote work contexts. The memo was organized in IRAC format and included a practical recommendation section with three alternative strategies ranked by risk level.

Sonnet 4.6 Result

Sonnet produced a solid memo covering the same § 16600 analysis and the general choice-of-law framework. It correctly identified that California public policy would likely override the Delaware choice-of-law clause. However, it cited fewer specific cases, missed the 2023 California amendments, and didn't address the emerging remote work dimension that's reshaping this area of law. The practical recommendations were more generic — advising the client to "consult with California counsel" without providing the stratified risk analysis that Opus delivered. For a first draft going to a senior partner, this level of generality would require significant supplementation.

This matters because AI hallucinations in legal research remain the biggest risk attorneys face. A deeper reasoning model doesn't eliminate hallucination risk — you still must verify every citation — but it reduces the frequency of fabricated case law and increases the likelihood that cited cases are real and relevant.

Test 3: Contract Review — Flagging Problematic Clauses

We uploaded a 28-page commercial lease agreement and asked both models to identify the five most problematic clauses from the tenant's perspective, explain the risk, and suggest alternative language.

Opus 4.6 Result

Opus identified: (1) a one-sided force majeure clause excluding pandemic events, (2) a continuous operation requirement with no cure period, (3) a personal guarantee extending beyond the lease term, (4) a demolition clause giving the landlord 90-day termination rights with no relocation assistance, and (5) a CAM reconciliation provision with no audit rights. Each issue included specific alternative language and a risk severity rating.

Sonnet 4.6 Result

Sonnet flagged four of the same five issues (missing the CAM audit rights gap) and provided competent alternative language for each. The explanations were slightly less detailed but clearly identified the risks. Sonnet also flagged a sixth issue — an asymmetric assignment clause — that Opus didn't highlight, demonstrating that more reasoning doesn't always mean more complete coverage.

Test 4: Client Communication — Translating Complex Legal Concepts

We asked both models to draft an email to a startup founder explaining why their proposed employee equity plan creates securities compliance issues, what needs to change, and a timeline for fixing it. The audience is a non-lawyer CEO who needs to understand the urgency without panicking.

Client communication is arguably the most underrated legal AI use case. Attorneys spend hours translating complex legal analysis into language clients can understand and act on. The model that can do this well saves significant daily time — and the quality bar is different from other legal tasks. Here, clarity, tone, and brevity matter more than exhaustive analysis.

Opus 4.6 Result

Opus drafted a well-structured email that explained Rule 701 exemption limits, identified the specific threshold the company was approaching, and outlined three concrete next steps with a timeline. The tone balanced urgency with reassurance. It included a brief but accurate explanation of why the SEC exemption matters and what happens if the company exceeds the threshold. However, the email was 650 words — longer than ideal for a busy founder who reads emails on their phone between meetings.

Sonnet 4.6 Result

Sonnet produced a tighter 400-word email covering the same substance. The Rule 701 explanation was slightly less detailed but perfectly accurate. The tone was more natural and conversational — closer to how an attorney would actually write to a longtime client. The three next steps were clear and actionable, each with a specific deadline. The email opened with the key takeaway (action needed, not crisis) before explaining why — a structure that respects the reader's time and attention.

Test 5: Regulatory Compliance Analysis — AI Policy for a Law Firm

We asked both models to draft an internal AI usage policy for a mid-size law firm (40 attorneys), covering permitted tools, data handling, client disclosure, billing guidelines, and supervision requirements — all aligned with current ABA and state bar guidance.

This is increasingly a must-have deliverable for law firm leadership. Malpractice insurers are starting to ask about AI policies, and firms without written guidelines face both ethical and business risk. The quality of the policy depends on how well the drafting model understands the regulatory landscape and practical implementation challenges.

Opus 4.6 Result

Opus produced a comprehensive 3,200-word policy document with 8 sections, correctly referencing ABA Formal Opinion 512, including specific provisions for different practice areas (distinguishing between litigation, transactional, and regulatory work), addressing the billing question (distinguishing between reduced-hours billing and value-based billing models), and including a detailed incident response procedure for AI errors discovered post-filing. The policy included a training requirement, quarterly review schedule, and an approved tools list with a process for evaluating new AI tools before firm-wide adoption.

Sonnet 4.6 Result

Sonnet drafted a solid 2,400-word policy covering the same core areas. It correctly cited ABA Formal Opinion 512 and included appropriate data handling and disclosure provisions. The billing section was less nuanced, and it lacked the incident response procedure and the practice-area-specific provisions. Still, a perfectly workable policy for a firm starting from scratch.

The Scorecard: When to Use Each Model

The Real Cost Comparison: What Attorneys Actually Pay

API pricing per million tokens doesn't mean much to attorneys who don't build their own tools. Here's what the model choice actually costs in practice:

If You Use claude.ai Directly

Sonnet 4.6 is the default model on the Free plan (limited usage) and the Pro plan ($20/month). To access Opus 4.6 through claude.ai, you need the Max plan at $100/month — a 5x increase. For a solo practitioner running 20-30 legal tasks per day through claude.ai, the question is whether Opus's deeper reasoning on the 20-30% of tasks where it matters is worth $80/month more.

Our take: for most solo practitioners and small firms, Pro with Sonnet 4.6 is the right starting point. Upgrade to Max only if you regularly handle complex multi-document analysis, novel legal questions, or high-stakes work where the reasoning depth gap we demonstrated above would materially change the output quality.

If You Use API-Powered Legal AI Tools

This is where it gets interesting. Tools like purpose-built legal AI platforms abstract away the model choice entirely. The best platforms route each request to the optimal model — using Sonnet for straightforward drafting and Opus for complex analysis — so you get flagship quality where it matters and mid-tier efficiency everywhere else.

This "smart routing" approach is exactly how The Legal Prompts works. When you generate an NDA or service agreement with standard parameters, the platform uses the most efficient model. When your request involves novel provisions, multi-jurisdictional considerations, or unusual carve-outs, it escalates to deeper reasoning automatically.

Monthly Cost Scenarios

The smart routing column is the sweet spot for most firms — you get Opus-quality output on the tasks that need it, at closer to Sonnet pricing overall.

The Context Window Factor: Why 1M Tokens Changes Legal Work

Both Opus 4.6 and Sonnet 4.6 now offer a 1 million token context window in beta — roughly 750,000 words or about 1,500 pages of legal text. This is a game-changer for legal work regardless of which model you choose.

Previously, attorneys using AI for document review had to chunk large document sets into smaller pieces, losing the ability to identify cross-references between sections or patterns across multiple agreements. With 1M tokens, you can now feed an entire M&A data room, a complete set of litigation discovery documents, or an entire regulatory filing into a single conversation.

But here's where Opus and Sonnet diverge significantly on long-context performance. On the MRCR v2 benchmark — which tests whether a model can find and reason over specific facts buried in massive inputs — Opus 4.6 scores 76% compared to Sonnet 4.5's 18.5%. Anthropic hasn't published Sonnet 4.6's MRCR score separately, but the architecture suggests it falls between these numbers.

For attorneys, this means: if you're doing document review across hundreds of pages and need the model to cross-reference specific provisions across different documents, Opus's long-context retention is materially better. For shorter documents (under 50 pages), both models perform comparably.

Practical examples where the context window matters:

• M&A due diligence: Upload the entire target company's contract portfolio (purchase agreements, employment contracts, IP assignments, vendor agreements) and ask the model to identify cross-default provisions, change-of-control triggers, and assignment restrictions across all documents simultaneously.
• Litigation discovery: Feed a complete set of deposition transcripts and ask the model to identify contradictions between witnesses or inconsistencies with documentary evidence already discussed.
• Regulatory compliance: Load an entire regulatory framework (like HIPAA or SOX) alongside a company's policies and procedures to identify gaps or non-compliant provisions.

In each scenario, the value isn't just reading the documents — it's reasoning across them. And that's where Opus's long-context retention advantage becomes a practical differentiator for complex legal work.

This capability directly addresses one of the key challenges we discussed in our guide to AI legal reasoning and traceability — the ability to trace each generated clause back to its source material across large document sets.

Ethical Considerations: Which Model for Compliant AI Use?

Both models are subject to the same ABA ethical obligations we detailed in our compliance guide. Your choice of model doesn't change your duties under Rules 1.1 (competence), 1.6 (confidentiality), or 3.1/3.3 (candor). But the model choice does affect two practical compliance areas:

Verification burden. A model that produces more accurate output reduces (but never eliminates) the attorney's verification workload. Opus's 90.2% BigLaw Bench score and deeper reasoning suggest fewer errors to catch on complex work — which means less time verifying and more time practicing law. On routine tasks, both models are comparably reliable.

Billing considerations. ABA Formal Opinion 512 requires that attorneys not bill clients for time that AI performed. If Sonnet completes a contract draft in 15 seconds and Opus takes 45 seconds with deeper reasoning, the billing analysis is the same — neither model's processing time is billable. But if Opus's output requires less attorney revision time, the total billable hours for a task may legitimately differ. Document your model choice and revision time for billing transparency.

Confidentiality. Both Opus and Sonnet 4.6 are available through the same Anthropic API with the same data handling policies. Anthropic's API terms state that prompts and outputs are not used for model training. However, if you're using claude.ai (the consumer product) instead of the API, be aware that conversations may be used for training unless you opt out. For client-confidential work, API access or a platform with a Business Associate Agreement is the safer path regardless of which model you choose.

Supervision requirements. Model Rule 5.3 requires attorneys to supervise non-lawyer assistants — and most bar opinions now extend this to AI tools. The supervision duty is the same whether you use Opus or Sonnet: review every output, verify every citation, and never submit AI-generated work product without attorney review. Using a more capable model doesn't reduce your ethical obligation to supervise — it just means your supervision time may be more efficiently spent.

Our Recommendation: The Decision Framework

After testing both models extensively on legal work, here's the framework we recommend:

Default to Sonnet 4.6 for: standard contract drafting, client correspondence, demand letters, routine research, document summaries, discovery review, and any high-volume task where you're processing many similar items.

Escalate to Opus 4.6 for: complex multi-party agreements, novel legal questions, comprehensive research memos for partners or clients, high-stakes compliance work, multi-document cross-referencing, and any task where you're dealing with novel fact patterns or unusual legal interactions.

Use smart-routing tools if: you don't want to make this decision on every task, you want to optimize cost without sacrificing quality, or you're a firm with multiple attorneys who need consistent output quality without each person learning model selection.

The gap between Opus and Sonnet is the smallest it's ever been. For most attorneys on most tasks, Sonnet 4.6 is the right choice. The times when Opus matters — and it does genuinely matter for about 20-30% of legal tasks — are the high-complexity, high-stakes moments where deeper reasoning produces measurably better work product.

The worst strategy? Using Opus for everything. You're paying 5x more for identical results on 70% of your tasks. The second worst? Using only Sonnet and missing the quality difference on the work that matters most.

If you're just getting started with AI in your practice, begin with Sonnet 4.6 on the Pro plan. Track which tasks require significant revision after AI drafting. After two weeks, you'll have a clear picture of which task categories would benefit from Opus's deeper reasoning — and you'll be able to make a data-driven decision about whether the Max plan or an API-powered tool makes more financial sense for your practice. For a deeper dive into building effective prompts for either model, see our prompt engineering guide for lawyers.

What This Means for Legal AI Tools

The Opus/Sonnet dynamic is reshaping how legal AI platforms are built. The best tools no longer lock you into a single model — they use the right model for each task. When you use Claude for legal work, the model selection should be invisible to you. What matters is the output quality, the traceability of each generated clause, and the compliance with your ethical obligations.

This is exactly the approach we take with The Legal Prompts. You select your jurisdiction, industry, and perspective (Pro-Client, Balanced, or Pro-Provider). The platform handles model routing, prompt optimization, and output verification — so you get the best possible legal document without becoming an AI model expert.