Claude Opus 4.7 vs GPT-5.5 Legal Research

Claude Opus 4.7 vs GPT-5.5 for legal research is the procurement question landing on every managing partner's desk in late April 2026. Anthropic shipped Opus 4.7 on April 16; OpenAI shipped GPT-5.5 a week later, on April 23. Both ship as the flagship reasoning model from their respective labs. Both list at $5 per million input tokens. Output is where they diverge: Opus 4.7 sits at $25/M output, GPT-5.5 at $30/M output (Anthropic pricing; OpenAI API pricing). For a firm running 50,000 queries a month on legal research, the output-token gap alone moves a $4,000 monthly bill by roughly $1,000. That's before you factor calibration, citation behavior, or how each model handles long-document analysis under the new agentic workflows. Here's the operator read for in-house counsel and BigLaw procurement.

Pricing and per-query economics: where the $1,000/month gap actually shows up

Per the Anthropic pricing page and the OpenAI API pricing page, Opus 4.7 lists at $5/M input + $25/M output. GPT-5.5 lists at $5/M input + $30/M output, with a Pro variant at $30/M input + $180/M output. Sticker prices look comparable until you model real usage.

A typical legal research query produces a 70/30 input/output token split: roughly 7,000 input tokens (the documents, the question, the prior turns) against 3,000 output tokens (the answer, the citations, the reasoning trace). At those ratios, Opus 4.7 costs about $0.110 per query. GPT-5.5 costs about $0.125. On 50,000 queries a month, that's $5,500 vs $6,250 — a $750 monthly delta, $9,000 annual.

That gap doubles or triples for output-heavy work. Long memo drafting, multi-clause contract review, and discovery summarization can run 30/70 input/output. At those ratios, Opus 4.7 lands at $0.190/query against GPT-5.5's $0.225. Same 50,000-query baseline = $9,500 vs $11,250. The tokenizer matters too: Opus 4.7's new tokenizer counts 1.0-1.35x the previous count depending on content type, with legal prose closer to the ceiling (see the Opus 4.7 vs Claude 4.6 cost analysis).

Second-order effect: GPT-5.5 cached input drops to $0.50/M (90% off) per the OpenAI pricing page. Firms running repetitive workflows (same case file across 30 associate queries) recover most of the gap. Third-order: the Pro variants flip the math. GPT-5.5 Pro at $30/$180 is six-to-seven times Opus 4.7. For complex agentic legal reasoning, you're not comparing flagships against flagships — you're comparing whichever variant your associates actually trigger by default.

Calibration and citation behavior: the hallucination math

Both labs claim improved calibration in the April releases. OpenAI's GPT-5.5 system card describes the model as "less likely to proceed confidently with a bad plan." Anthropic's Opus 4.7 release notes name the same behavior in different language. For legal research, that single behavior matters more than any benchmark.

The practical context: Damien Charlotin's hallucination database cataloged 1,227 documented sanctions cases globally as of early 2026, up from 719 in January. The Cherry Hill federal sanction on April 27, 2026 (per the Inquirer) named an attorney who couldn't recall whether he'd used Claude, ChatGPT, or Grok. That's the floor: model brand doesn't matter when the workflow lacks verification.

On calibration specifically, Opus 4.7 in our internal testing surfaces fewer overconfident answers on niche legal questions (state-bar variations, recent Supreme Court holdings, statute renumberings). GPT-5.5's edge is speed: shorter latency means faster verification cycles, which can functionally compensate for residual hallucination if the firm's workflow includes a citation-checker step.

The second-order angle: 300+ federal judges now have AI standing orders, with most requiring tool disclosure but not version disclosure. Per the Bloomberg Law standing-order tracker, this is fragmenting fast. Picking a model with better calibration only helps if your disclosure policy and verification steps are tight enough to catch the residual errors. Otherwise the model choice is a rounding error against process discipline.

Long-document analysis: 1M context vs multi-session memory

GPT-5.5 ships with a 1M-token context window per OpenAI's announcement. Opus 4.7 holds a 200K context window but adds multi-session memory persistence via scratchpad/notes file (Anthropic docs). Two different solutions to the same problem; different operational fits.

For a single massive document set — a full M&A data room, a 600-document discovery production, a 5,000-page regulatory record — GPT-5.5's 1M context wins. You load the whole set, ask the question, and the model can attend to everything. Opus 4.7 at 200K either rejects the load or requires chunking with retrieval.

For a 12-day M&A diligence engagement that spans 30+ sessions across multiple deal teams, Opus 4.7's multi-session memory wins. Claude writes notes mid-session, the firm saves the file with the matter, and the next session resumes where the prior one stopped. Same parties, same facts, same line of analysis. GPT-5.5's 1M context resets every session by default; persistence requires custom infrastructure.

The operator read: pick by workload shape. Single-shot megadoc analysis = GPT-5.5. Long-horizon multi-session matters = Opus 4.7. For most legal practices, the realistic answer is both (different tools for different jobs), not one. Firms building procurement around a single model will eventually hit the workload pattern that doesn't fit. Plan for it. See the Opus 4.7 vs Sonnet 4.6 use-case split for the parallel within-Anthropic question.

Tool use and agentic legal workflows: where each model finishes the job

Legal research increasingly runs through agentic loops: pull cases from a research database, summarize, cross-check against secondary sources, draft the memo. Both models support tool use. The behavioral differences show up in how each finishes a task.

Opus 4.7's task budgets cap token spend per agentic loop. Set 2M tokens for a discovery first-pass; the model tracks against the cap with a running countdown and reports gracefully when it hits the limit. That's deterministic spend per matter, which matters for partners writing budget memos. The task budgets in discovery deep-dive walks the configuration.

GPT-5.5's edge on agentic work is error recovery mid-task. Per the OpenAI release notes, the model handles tool-call failures better — when a Westlaw API call returns an error, GPT-5.5 retries or pivots more cleanly than GPT-5 did. For brittle integrations (older internal systems, third-party legal databases with rate limits), that's meaningful.

Anthropic ships Claude Code defaulting all paid plans to xhigh effort level. That means associates running Claude Code without firm authorization are getting xhigh by default — and the bill reflects it. OpenAI's o1 Pro mode and GPT-5.5 Pro carry similar latent-cost dynamics on the ChatGPT Pro $200/month tier. AI policies that name vendors but not effort levels or tier configurations are now stale on both sides.

Procurement, deployment surfaces, and security posture

Opus 4.7 deploys via claude.ai (Pro $20/Max $100/Team $25/Enterprise $20+ per Claude pricing), the Claude API, AWS Bedrock, Vertex AI, and Microsoft Foundry. GPT-5.5 deploys via ChatGPT (Plus $20/Pro $200 per ChatGPT pricing, Business $25/user/month, Enterprise quote-only per OpenAI Business pricing) and the OpenAI API.

For BigLaw running on Microsoft 365 (90%+ of law firms), Microsoft Foundry typically wins on procurement velocity for Anthropic models — same vendor relationship, same contract paper, same data-handling commitments. The Microsoft Foundry procurement guide covers the BigLaw playbook. For AWS-native firms, Bedrock inherits AWS compliance posture; for GCP-native firms, Vertex AI does the same.

OpenAI's procurement story is more direct: ChatGPT Enterprise or the OpenAI API, both directly with OpenAI. Microsoft 365 Copilot embeds OpenAI models but functions as a separate procurement track at $30/user/month per Microsoft's enterprise pricing. Different SLAs, different audit trails, different model versions sometimes lag the OpenAI flagship.

On privilege: the *United States v. Heppner* ruling (SDNY, Feb 17, 2026) confirmed that consumer-AI exchanges aren't privileged (Heppner explainer). Both Anthropic and OpenAI's enterprise tiers carry stronger commitments. For privileged work, neither consumer Claude Pro nor consumer ChatGPT Plus is the right surface — the procurement floor is Team/Business tier minimum on either side.

Recommendations by firm size and practice mix

Solos and small firms (1-10 attorneys): Pick the model your existing tools already integrate with. If you live in Microsoft 365, Copilot's GPT-5.5 access via the $30/user/month add-on is closest to zero-friction. If you've already built a Claude workflow, Opus 4.7 on Pro ($20/month) covers the basics. The output-cost gap doesn't matter at solo volume.

Mid-market firms (10-100 attorneys): Run both for 30 days, then pick by where your workflows actually settle. Claude Team at $25/user/month and ChatGPT Business at $25/user/month are within $0/seat of each other on annual billing. The differentiator is which model your associates self-serve onto. Most firms find the answer is uneven — different practice groups gravitate to different tools. Don't force consolidation early.

BigLaw and AmLaw 100: Procurement question shifts. Existing vendor relationships, deployment surface (Foundry/Bedrock/Vertex/Azure), and AI governance posture matter more than the model itself. Firms with active Anthropic deals (Freshfields is the public reference per the Freshfields × Anthropic analysis; more in negotiation) optimize for Claude. Firms with deep Azure/Microsoft tooling optimize for GPT-5.5 via Foundry's OpenAI surface. Most BigLaw firms run both at portfolio scale and let practice groups specialize.

By practice area: Discovery-heavy litigation = Opus 4.7 task budgets. M&A diligence = Opus 4.7 multi-session memory. Single-shot megadoc analysis (regulatory rule comments, legislative history briefs) = GPT-5.5's 1M context. High-volume rapid research with citation downstream = either, pick by latency preference.

The Bottom Line: My take: This isn't a winner-takes-all comparison. Opus 4.7 wins on calibration, deterministic per-matter spend, and long-horizon memory. GPT-5.5 wins on raw context size, agentic error recovery, and per-token output efficiency on cached workloads. For most firms, the right answer is both, picked by workload shape — not one model standardized across the firm. Procurement teams forcing single-vendor consolidation in April 2026 will redo the work in October when their associates have already routed around the policy.