GPT-5.5 vs Claude Opus 4.7 Legal Comparison 2026

GPT-5.5 vs Claude Opus 4.7 for legal work 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 flagship reasoning models. 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 (per Anthropic pricing and OpenAI API pricing). For a firm running 50,000 queries a month, the output gap alone moves the bill by roughly $750. That's before factoring calibration, context size, multi-session memory, or which deployment surface fits your existing vendor relationships. This is the bidirectional companion to the Claude Opus 4.7 vs GPT-5.5 comparison from Cluster 1 — same models, GPT-5.5-first framing here.

Pricing economics: $750-$1,750 monthly gap depending on workload shape

Both list $5/M input. Output diverges: GPT-5.5 at $30/M, Opus 4.7 at $25/M — a 17% gap on output. For a typical legal research query at 70/30 input/output split (7K input / 3K output tokens), GPT-5.5 standard costs about $0.125 per query; Opus 4.7 costs about $0.110. On 50,000 queries a month, that's $6,250 (GPT-5.5) vs $5,500 (Opus 4.7) — a $750 monthly delta, $9,000 annual.

The gap doubles or triples for output-heavy workloads. Long memo drafting, multi-clause contract review, and discovery summarization can run 30/70 input/output. At those ratios, GPT-5.5 lands at $0.225/query against Opus 4.7's $0.190. Same 50,000-query baseline = $11,250 vs $9,500 — a $1,750 monthly delta.

The Pro/xhigh variants flip the math. GPT-5.5 Pro at $30/M input + $180/M output is six-to-seven times Opus 4.7's standard pricing. Anthropic's xhigh effort level on Opus 4.7 doesn't carry a separate price tier — it consumes more output tokens at the same $25/M rate. For complex agentic workloads, comparing GPT-5.5 Pro against Opus 4.7 xhigh is comparing apples to oranges on cost structure.

The second-order economics: GPT-5.5 cached input drops to $0.50/M (90% off) per OpenAI's API pricing — a meaningful saver on repetitive workflows. Anthropic offers prompt caching but the discount and behavior differ. The API pricing firm cost analysis spoke covers GPT-5.5's cost structure in detail.

Context windows: 1M vs 200K plus memory

GPT-5.5 ships with a 1M-token context window per OpenAI's launch announcement. Opus 4.7 holds a 200K context window but adds multi-session memory persistence via scratchpad/notes file (per Anthropic docs on what's new). Two different solutions to the same long-context 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. Load the whole set, ask the question, model attends to everything. Opus 4.7 at 200K either rejects the load or requires chunking with retrieval. Per the 1M context window for litigation discovery spoke, the 1M unlock is structural for single-shot megadoc analysis.

For a 12-day M&A diligence engagement spanning 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.

Calibration and citation behavior: both improved, both still need verification

Both labs claim improved calibration in their 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 similar behavior. Different language, similar direction.

For legal research, that single behavior matters more than any benchmark. Per Damien Charlotin's hallucination database, 1,227 documented sanctions cases globally had been logged by early 2026. 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. Model brand doesn't matter when verification is missing.

Observational evidence on the 5.5 vs Opus 4.7 calibration delta is mixed. Opus 4.7 surfaces fewer overconfident answers on niche legal questions (state-bar variations, recent Supreme Court holdings, statute renumberings) in our internal testing. GPT-5.5's edge is speed — shorter latency means faster verification cycles, which can functionally compensate for residual hallucination if your 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 Bloomberg Law's 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. The calibration improvement and AI hallucination sanctions spoke covers this exposure path.

Tool use and agentic legal workflows: where each 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. Behavioral differences show up in how each finishes a task.

Opus 4.7 ships with task budgets that 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. Deterministic per-matter spend that partners can put in a budget memo. GPT-5.5 doesn't ship a comparable native task-budget feature — usage logging happens at the API level, not the model level.

GPT-5.5's edge on agentic work is error recovery mid-task. When a Westlaw API call returns an error or a tool call rate-limits, GPT-5.5 retries or pivots more cleanly than 5.4 did. For brittle integrations (older internal systems, third-party legal databases with rate limits), that's meaningful. The tool calls and legal research coherence spoke walks the agentic engineering implications.

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 GPT-5.5 Pro on ChatGPT Pro $200/month carries similar latent-cost dynamics. AI policies that name vendors but not effort levels or tier configurations are 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 $20-25/user/month, Enterprise quote-only per OpenAI Business pricing) and the OpenAI API. Microsoft 365 Copilot at $30/user/month embeds OpenAI models inside Word, Outlook, Teams.

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. For OpenAI access on the same M365 infrastructure, Microsoft Copilot is the standard surface. Different SLAs, different audit trails, different version-update cadence.

For AWS-native firms, Bedrock inherits AWS compliance posture; for GCP-native firms, Vertex AI does the same. OpenAI doesn't ship a Bedrock or Vertex equivalent — direct OpenAI API or Microsoft Copilot are the standard procurement paths.

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 — Team/Business tier minimum on either side. The Anthropic eating the legal stack analysis covers BigLaw deployment patterns post-Freshfields.

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 workflows actually settle. Claude Team at $25/user/month and ChatGPT Business at $25/user/month are within $0/seat. 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; more in negotiation per the Anthropic eating the legal stack analysis) optimize for Claude. Firms with deep Azure/Microsoft tooling optimize for GPT-5.5 via Microsoft Copilot. Most BigLaw firms run both at portfolio scale.

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

The Bottom Line: My take: This isn't a winner-takes-all comparison. GPT-5.5 wins on raw context size, agentic error recovery, and per-token output efficiency on cached workloads. Opus 4.7 wins on calibration, deterministic per-matter spend via task budgets, and long-horizon multi-session memory. 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.