GPT-5.5 vs Mythos vs Gemini 3.1 Pro Legal

GPT-5.5 vs Claude Mythos vs Gemini 3.1 Pro for legal work is the three-way frontier comparison most firms haven't run. OpenAI shipped GPT-5.5 on April 23, 2026. Anthropic's Mythos research preview surfaced earlier in the year as the experimental model behind Opus 4.7's xhigh effort tier. Google's Gemini 3.1 Pro shipped in Q1 2026 as the flagship multimodal reasoning model. All three target the high-end of complex legal reasoning. Pricing, deployment surfaces, and behavioral profiles differ substantially. For firms evaluating which frontier model fits which legal workload, this is the operator read on the three-way comparison — neutral on vendor character, opinionated on operational fit.

What Mythos actually is and how it relates to Opus 4.7

Anthropic's Mythos surfaced as the codename behind Opus 4.7's xhigh effort level — the highest-compute reasoning tier on the Claude product line. Per Anthropic's Opus 4.7 release notes and the platform docs, xhigh sits between high and max effort settings and ships as the default on all paid Claude Code plans.

For legal users, the operational read: "Claude Mythos" isn't a separate model from Opus 4.7 — it's the high-effort variant. When legal-tech coverage refers to Mythos as if it's a distinct model, that's typically shorthand for Opus 4.7 on xhigh effort. Pricing follows Opus 4.7's standard rates ($5/M input + $25/M output per Claude pricing) — there's no separate Mythos price tier. xhigh consumes more output tokens per query, so the bill is higher even at the same per-token rate.

The practical implication: when comparing GPT-5.5 vs Mythos for legal research, you're comparing GPT-5.5 against Opus 4.7's compound-reasoning behavior. The detailed apples-to-apples comparison lives in the GPT-5.5 vs Claude Opus 4.7 spoke. This spoke focuses on the three-way layer that adds Gemini 3.1 Pro to the picture.

Gemini 3.1 Pro: what Google ships for legal frontier reasoning

Gemini 3.1 Pro is Google's flagship reasoning model on the Gemini line. Multimodal native (text, image, audio, video input), 2M-token context window, and tightly integrated with Google Workspace and Vertex AI. Per Google's published pricing, Gemini 3.1 Pro lists at $3.50/M input + $21/M output for standard use, with caching discounts available for repeated context.

For legal workloads, Gemini 3.1 Pro's strengths are three. First, the 2M-token context window — twice GPT-5.5's 1M and ten times Opus 4.7's 200K. For mega-cases (5,000+ page regulatory records, full M&A data rooms with 15,000+ pages), Gemini 3.1 Pro is the only frontier model that fits the load natively. Second, multimodal handling — for evidence review involving images, audio depositions, or video exhibits, Gemini's native multimodal pipeline beats text-only models. Third, Workspace integration — for firms running Google Workspace (a minority of law firms but a meaningful one in the startup-counsel and tech-practice segments), the procurement velocity is fastest.

The operational caveats: Gemini 3.1 Pro's calibration on niche legal questions is less predictable than GPT-5.5 or Opus 4.7. The model performs strongly on benchmarks but legal-specific reliability varies more by query type. Most BigLaw firms haven't standardized on Google Workspace, which limits Gemini 3.1 Pro's procurement advantage outside startup-tech practice.

The second-order angle: Vertex AI ships Anthropic's Opus 4.7 alongside Google's own Gemini models. For firms standardized on Google Cloud infrastructure, the surface offers both vendors on the same procurement paper — a meaningful flexibility that ChatGPT and claude.ai consumer surfaces don't match.

Three-way comparison: context, calibration, cost

Context window: Gemini 3.1 Pro at 2M tokens (winner) > GPT-5.5 at 1M tokens > Opus 4.7 at 200K tokens. For megadoc workloads, Gemini wins. For standard discovery and contract work, all three are sufficient. Opus 4.7 compensates with multi-session memory.

Calibration on legal queries: Mixed by query type. Opus 4.7 (Mythos at xhigh) shows the strongest calibration on niche legal questions (state-bar variations, statute renumberings, recent Supreme Court holdings) in our internal observation. GPT-5.5 follows closely with structurally similar improvements per OpenAI's system card. Gemini 3.1 Pro is variable — strong on multi-jurisdictional regulatory reasoning, weaker on US-state-specific case law. The GPT-5.5 calibration improvement and AI hallucination sanctions spoke covers the calibration metric in detail.

Pricing on standard tiers: Gemini 3.1 Pro at $3.50/M input + $21/M output (cheapest). Opus 4.7 at $5/M input + $25/M output. GPT-5.5 at $5/M input + $30/M output (most expensive on output). For 50,000 queries/month at typical 70/30 input/output split: Gemini ~$4,375 < Opus 4.7 ~$5,500 < GPT-5.5 ~$6,250. The output gap matters most on output-heavy workloads (memo drafting, contract review).

Pro/elevated tiers: GPT-5.5 Pro at $30/M input + $180/M output (six-times standard). Opus 4.7 xhigh at standard rates but higher token usage (effective 1.5-2x bill increase). Gemini 3.1 Pro doesn't ship a separate "Pro Pro" tier — same rates regardless of effort level.

The second-order economics: Gemini 3.1 Pro's pricing advantage is meaningful at scale but offset by Google Workspace's smaller install base in the legal industry. For firms not on Workspace, Gemini's procurement is OpenAI-API-equivalent friction. The API pricing firm cost analysis covers GPT-5.5's economics in depth.

Practice-area routing for the three-way comparison

Five practice areas where Gemini 3.1 Pro wins:

- Mega-case regulatory practice — FERC, FCC, FDA records that exceed 1M tokens benefit from the 2M context. - Multi-jurisdictional compliance — Gemini's training data appears stronger on cross-border regulatory frameworks. - Multimodal evidence review — when matters involve image, audio, or video evidence alongside documents. - Tech-practice startups using Workspace — procurement velocity on Workspace-native firms. - High-volume cost-sensitive workloads — pricing advantage matters at 100K+ queries/month scale.

Five where GPT-5.5 wins:

- Standard megadoc analysis — 1M context fits most workloads; broad ChatGPT integration. - Microsoft 365 Copilot deployments — firms on M365 (90%+ of law firms) get GPT-5.5 via $30/user/month add-on. - Brittle tool integrations — error recovery improvements per the tool calls and legal research coherence spoke. - Codex CLI legal-tech engineering — see the Codex CLI for legal-tech engineering spoke. - Best Lawyers ChatGPT app users — vertical legal directory living inside ChatGPT.

Five where Opus 4.7 (Mythos at xhigh) wins:

- Long-horizon multi-session matters — multi-session memory persistence beats per-session reset. - Calibration-critical niche research — fewer fabrications on state-bar variations. - Discovery with task budgets — deterministic per-matter spend via task budgets. - Microsoft Foundry deployments — Anthropic models on M365 procurement paper. - Firms with active Anthropic deals — Freshfields and the Anthropic eating the legal stack analysis network.

Procurement pattern: when to run all three

BigLaw and AmLaw 100 firms increasingly run all three frontier models simultaneously, with practice groups specializing. The procurement pattern that works:

Microsoft 365 Copilot at $30/user/month covers GPT-5.5 across the firm's M365 install base. Default for routine research, drafting, and inline document work.

Microsoft Foundry for Opus 4.7 access on the same M365 procurement paper. Default for litigation discovery (task budgets) and M&A diligence (multi-session memory).

Vertex AI for Gemini 3.1 Pro access on Google Cloud infrastructure. Default for mega-case regulatory practice and multimodal evidence review.

Mid-market firms (10-100 attorneys) typically can't justify the procurement overhead of three vendor relationships. The right pattern: pick two — usually GPT-5.5 (via Microsoft Copilot) and Opus 4.7 (via claude.ai Team or Microsoft Foundry) — and let practice groups gravitate. Add Gemini 3.1 Pro only if a specific practice has mega-case workloads that need the 2M context.

Solo and small firms standardize on one. ChatGPT Business ($25/user/month per OpenAI Business pricing) or Claude Team ($25/user/month per Claude pricing) cover most workloads. Don't try to run three at solo scale.

The Bottom Line: My take: Three frontier models, three different operational fits. Gemini 3.1 Pro wins on context size and price; GPT-5.5 wins on M365-native deployment and Codex tooling; Opus 4.7 (Mythos at xhigh) wins on calibration and multi-session memory. BigLaw runs all three. Mid-market runs two. Solos pick one based on existing tooling. Vendor lock-in is the procurement risk to watch — every additional model deepens dependency that's hard to unwind once practice groups settle in.