report.object
AI models for legal work
Expert-reviewed from public records, platform data, primary sources, and private research beyond AI search. Use /api/v1/reports/RPT_01KSHN11BMQVWKQ68GCHYNG1KY/sample before purchase, then download PDF, JSON, or Markdown after checkout.
page.markdown
AI models for legal work.md
# AI models for legal work
Legal AI models should be compared as a work stack: frontier model, legal retrieval, OCR, embeddings, reranking, deployment controls, source permissions and lawyer review.
## Object
| field | value |
| ---------------- | ------------------------------ |
| report_id | RPT_01KSHN11BMQVWKQ68GCHYNG1KY |
| schema_version | 2026-05-28 |
| last_updated_at | 2026-06-19 |
| category | MODELS |
| sector | Legal services |
| region | GLOBAL |
| confidence_score | 0.88 |
| source_count | 196 |
| evidence_count | 223 |
## Endpoints
| action | endpoint |
| ------------ | --------------------------------------------------------- |
| summary | /api/v1/reports/RPT_01KSHN11BMQVWKQ68GCHYNG1KY |
| sample | /api/v1/reports/RPT_01KSHN11BMQVWKQ68GCHYNG1KY/sample |
| schema | /api/v1/reports/RPT_01KSHN11BMQVWKQ68GCHYNG1KY/schema |
| quote | /api/v1/reports/RPT_01KSHN11BMQVWKQ68GCHYNG1KY/quote |
| purchase | /checkout/RPT_01KSHN11BMQVWKQ68GCHYNG1KY?view=agent |
| versions | /api/v1/reports/RPT_01KSHN11BMQVWKQ68GCHYNG1KY/versions |
| changelog | /api/v1/reports/RPT_01KSHN11BMQVWKQ68GCHYNG1KY/changelog |
| monitor | /api/v1/monitors?report_id=RPT_01KSHN11BMQVWKQ68GCHYNG1KY |
| mcp_resource | colophon://reports/RPT_01KSHN11BMQVWKQ68GCHYNG1KY/sample |
## Coverage
| field | value |
| -------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| category | MODELS |
| sector | Legal services |
| region | GLOBAL |
| topics | legal AI, frontier models, legal RAG, document parsing, model economics, legal agents, model governance, cross-border AI, export controls, sanctions, AI regulation, AI litigation, AI politics |
| source_classes | official_model_docs, cloud_docs, privacy_terms, model_card, document_ai_docs, legal_benchmark, retrieval_benchmark, vendor_docs, legal_ethics, regulation, evaluation_framework, expert_commentary, social_discourse, market_signal, multilingual_benchmark, general_benchmark, model_router_catalogue, model_hub, package_telemetry, package_registry, issue_tracker, status_page, regulatory_control, regulatory_guidance, sector_restriction, export_control, sanctions_export_control, sanctions_guidance, data_transfer_control, government_restriction, privacy_enforcement, government_evaluation, pricing_page, archived_web, security_advisory, domain_registry, certificate_transparency, grant_database, court_docket, lobbying_disclosure, expert_profile, model_benchmark, cost_benchmark, inference_infrastructure, case_study, expert_interview, demo_video, court_opinion, regulatory_registry, news_index, election_calendar, policy_framework, party_policy, polling_data, regulatory_timeline, regulatory_consultation, regulator, international_standard, industry_analysis, research_paper, professional_benchmark, dataset, vendor_research |
| update_cadence | Weekly |
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"No public benchmark fully captures end-to-end legal agent work under realistic matter permissions, document conditions and reviewer acceptance.",
"Chinese, Japanese, Korean, Russian, EU, UK, and multilingual legal evals remain less comparable than US/English model evals.",
"Regulatory, sanctions, export-control, and public-sector restrictions are moving faster than public model benchmarks.",
"Published token prices are not enough to compare models; hidden reasoning tokens, retries, latency, review cost, and utilisation can reverse the apparent winner.",
"Vendor-reported legal workflow metrics are not consistently reproducible from public data.",
"Public case studies reveal implementation shape but rarely publish denominators, error rates, matter mix, or control groups.",
"Outside-box operational records are strong monitoring triggers but indirect evidence of legal model quality.",
"Polling, news-index, and party-policy sources can flag legal-political risk but should not decide model routing without primary legal, regulatory, or pollster-methodology support."
]
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"title": "AI models for legal work",
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"category": "MODELS",
"sector": "Legal services",
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"evidence": [
{
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"summary": "OpenAI positions GPT-5.5 for professional work, tool use, online research, data analysis, documents, spreadsheets, and operating software.",
"status": "verified",
"confidence": 0.88,
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"summary": "Anthropic introduced Claude Opus 4.8 on 28 May 2026 as an Opus 4.7 upgrade with stronger coding, agentic, and professional-work performance, effort controls, dynamic workflows, unchanged regular pricing, and API ID claude-opus-4-8.",
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"confidence": 0.88,
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{
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"summary": "Vals AI's LegalBench leaderboard updated 26 May 2026 lists 110 tested models, with source-backed rows across Google, OpenAI, Anthropic, Alibaba, Moonshot AI, xAI, Zhipu AI, Meta, MiniMax, DeepSeek, Mistral, and Llama/Together alternatives.",
"status": "verified",
"confidence": 0.88,
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{
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"sourceId": "S20",
"claimIds": [
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"summary": "Legal RAG Bench reports retrieval as the primary driver of legal RAG performance and quantifies gains from Kanon 2 Embedder.",
"status": "verified",
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"access": "sample"
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],
"claims": [
{
"id": "C1",
"statement": "The best legal model strategy in May 2026 is a routed portfolio, not a single winner: GPT-5.5, Claude Opus 4.8, and Gemini 3.1 Pro all deserve seats for different legal workloads.",
"confidence": 0.88,
"evidenceIds": [
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{
"id": "C3",
"statement": "For legal RAG, context quality now constrains answer quality as much as generator intelligence: embeddings, retrieval, reranking, and context packing decide whether controlling sources reach the model's usable context.",
"confidence": 0.9,
"evidenceIds": [
"E18"
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"colophon": {
"checkedAt": "2026-06-19",
"latestRevisionAt": "2026-06-19T00:00:00Z",
"reviewStandard": "expert_human_reviewed",
"kinetics": {
"speed": "fast",
"impact": 0.88,
"momentum": 0.87,
"businessImpact": "Legal AI has moved from clever research chat to work systems that touch diligence, drafting, spreadsheets, matter knowledge, and client delivery.",
"rateOfChange": "Model releases, retrieval benchmarks, and legal AI product features now change fast enough that a one-model policy ages badly.",
"from": "chatbot trials",
"toward": "approved legal AI stacks with retrieval, document intelligence, tool controls, and lawyer review"
},
"watch": [
"23 June 2026: European Commission high-risk AI consultation closes; scope could change which legal AI uses need AI Act controls [E213].",
"2 August 2026: AI Act Commission enforcement powers for GPAI providers begin; EU model routes may need stronger documentation, copyright-policy or incident-reporting controls [E212].",
"Thomson Reuters v ROSS docket entries or appeal briefing; legal-RAG licensing economics may change [E137].",
"OpenAI, Anthropic or Google model-deprecation notices; approved legal routes or cost per accepted legal answer may change."
],
"gapAnalysis": "Independent end-to-end legal-work benchmarks with multilingual and non-US matters, stale-law checks, bad OCR, spreadsheets, privilege review, realistic matter permissions, regulatory-change events and cost per accepted output."
},
"body": {
"sections": [
{
"id": "verdict",
"title": "Executive summary",
"access": "sample",
"claimIds": [
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"lead": "Legal model selection is stack approval: which model sees which sources, under which permissions, with which document layer, and for which legal task. GPT, Claude, Gemini, open-weight and Chinese models all have roles; none deserves the keys to the whole matter. A good model with the wrong source is still a bad legal answer.",
"body": [
"Use the strongest frontier models where reasoning, judgement and drafting quality matter. Use cheaper or private models where volume, sovereignty or latency matters. Use legal retrieval, reranking and OCR before asking any model to pronounce on a clause, case or filing.",
"The trap is to treat a model leaderboard as a buying decision. Legal work fails less often because a model is weak than because the system sent it the wrong source, a bad scan, a stale authority or a document it was not allowed to read; Stanford HAI found pervasive legal hallucination even in retrieval-augmented legal products [E27, E28]."
],
"detail": [
"For high-risk work, the generator is only the visible part of the system. Parser quality, source rights, jurisdiction metadata, currentness, retrieval recall, reranker precision, citation support and human sign-off decide whether the answer is defensible.",
"Model choice is a lane decision rather than a coronation. A research memo, invoice review, Chinese-language diligence, privilege triage and scanned lease schedule need different routes."
]
},
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"label": "AI agents for legal workflows",
"dek": "Provider diligence for agentic legal work: buy versus build, source access, approval gates, evals, governance, and workflow ROI.",
"relation": "Related report"
},
{
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"dek": "Provider selection for OCR, parsing, extraction, classification, table recovery, redlines, VDR ingestion, pricing, controls and parser evals.",
"relation": "Related report"
},
{
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"lead": "Start with the legal job, then route by risk, source need, document condition and jurisdiction.",
"steps": [
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"prompt": "Will this become advice, filing, privilege, citation or client text?",
"tone": "blocked",
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"label": "Yes",
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"tone": "blocked"
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"next": "Q2 sources",
"tone": "monitor"
}
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},
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"prompt": "Does it need authorities, clauses, matter files or local law?",
"tone": "warning",
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"label": "Yes",
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"tone": "success"
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"label": "No",
"outcome": "Cheaper classifiers or summaries may be enough if evals pass.",
"tone": "info"
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"prompt": "Are files scanned, tabular, redlined, multilingual or schedule-heavy?",
"tone": "warning",
"branches": [
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"label": "Yes",
"outcome": "Test OCR and parsing before comparing models.",
"tone": "warning"
},
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"label": "No",
"outcome": "Choose by task difficulty, cost and data boundary.",
"tone": "success"
}
]
},
{
"prompt": "Is it cross-border, sovereign, public-sector or country-of-concern work?",
"tone": "blocked",
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"label": "Yes",
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"tone": "blocked"
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"label": "No",
"outcome": "Pilot the best-priced route that passes private evals.",
"tone": "success"
}
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"align": "left"
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"label": "Buyer move",
"align": "left"
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"decision": "Premium reasoning",
"status": "Route",
"view": "Use the premium frontier lane where legal reasoning, drafting quality and review minutes matter more than raw unit price. Keep exact model versions in the dated shortlist."
},
{
"decision": "Legal retrieval",
"status": "Prioritize",
"view": "Prioritize legal embeddings, reranking, source hierarchy, currentness, and citation verification before swapping answer models."
},
{
"decision": "Open weights",
"status": "Evaluate locally",
"view": "Use Mistral, Llama, DeepSeek, Qwen, and similar lanes for private, sovereign, or high-volume work after local evals."
},
{
"decision": "Document understanding",
"status": "Test first",
"view": "Treat OCR, layout, tables, redlines, exhibits, and page references as a model layer. Bad parsing makes good models look clever and wrong."
},
{
"decision": "Final legal judgement",
"status": "Human gate",
"view": "Do not let any model own filings, privilege calls, client advice, or citation-bearing work without lawyer verification and an audit trail."
}
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"tone": "neutral",
"body": "Route means the approved path from work to accepted output: the source set, data boundary, model or vendor channel, tools, reviewer, evidence record and reapproval trigger that make an AI result usable."
},
{
"id": "early-governance-rule",
"title": "Approve the work path, not the logo",
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"type": "callout",
"tone": "warning",
"body": "Approve models by use case and data class. A route cleared for public-law summaries is not cleared for privileged exhibits, cross-border investigations, spreadsheet mutation, client advice or outbound email."
}
]
},
{
"id": "what-changed",
"title": "What changed",
"access": "locked",
"dek": "The bottleneck moved from raw fluency to defensible work product.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "what-changed-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "model-economics",
"title": "Model economics",
"access": "sample",
"dek": "Price the finished legal task, not the token.",
"claimIds": [
"C1"
],
"evidenceIds": [],
"blocks": [
{
"id": "model-economics-narrative",
"evidenceIds": [],
"type": "section_narrative",
"lead": "The cheap model is cheap only when it finishes the legal job.",
"body": [
"Per-token price is a sticker. Legal cost is the whole route: prompt length, output length, retries, hidden reasoning tokens, cache hits, tool calls, queue time, reviewer minutes and the approved data boundary. A model that costs twice as much per token can still win if it writes the answer in half the tokens, needs fewer repair prompts and takes less lawyer time to verify.",
"Token mix changes the bill. A tool that reads a large bundle and returns a short answer is mostly an input-token problem. Translation, redlining, drafting and clause-by-clause issue lists can force the model to write almost as much text as it read, so high output-token prices can dominate the matter budget. [E123, E156]",
"Artificial Analysis-style comparisons are useful because they put price beside intelligence, output speed, latency and context. Legal buyers need one more column: accepted work product. The unit is not a million tokens. It is a correct answer with source support, a reviewable redline, a complete issue register or a verified extraction.",
"The same rule applies to agent routes. Harvey's initial LAB snapshot put the then-highest all-pass route at about $50.90 per task and roughly 22 minutes; the later Opus 4.8 note improves the score to 10.4%, but it does not remove the need to price passed legal work rather than only the token stream. [E205, E211]"
],
"detail": [
"Open and Chinese models can be excellent routes, but they do not guarantee savings. Self-hosting only becomes cheap when utilisation is high, serving is efficient, security and patching are paid for, and fallback capacity is planned. A cheap model running through a reseller, a low-utilisation GPU fleet or a review-heavy workflow may not be cheap at all.",
"The most useful procurement metric is cost per accepted output: model spend plus infrastructure, latency, retries, review time, failed-task rework and compliance overhead. Many legal AI pilots discover that cheaper tokens were only cheaper accounting."
]
},
{
"id": "intelligence-price-decline-chart-split",
"title": "The sticker price is not the bill",
"evidenceIds": [],
"type": "chart",
"chartType": "line",
"data": [
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"date": "2026-04-02",
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"date": "2026-04-22",
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"date": "2026-04-24",
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"date": "2026-05-31",
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"notes": "Method: Artificial Analysis price rows grouped by a16z bands; compares cheapest blended 3:1 token price with estimated full-index run cost. Caveat: not a legal-quality score [E122, E158]."
},
{
"id": "model-economics-lane-bridge",
"evidenceIds": [],
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"text": "Sticker price is only the first cost signal. Lane choice determines whether the same workload is interactive, batched, cached, flexed, provisioned or delayed, which changes both the bill and the review burden.",
"variant": "body"
},
{
"id": "model-pricing-mechanics-async-lanes-lead",
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"type": "paragraph",
"text": "The best discount for legal VDR review, litigation production, clause extraction and private eval reruns is often patience. Long extraction packets, eval reruns, classification sweeps and archive enrichment do not need a user waiting in the browser. Move that work to async or lower-priority lanes, keep deadline work on the fast path, and compare cost per accepted legal output.",
"variant": "body"
},
{
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{
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{
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"label": "Good for",
"align": "left"
},
{
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"label": "Watch",
"align": "left"
}
],
"rows": [
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"lane": "OpenAI Batch API",
"how_it_works": "Async jobs use a separate batch lane with a lower price and a 24-hour completion target. [E182]",
"good_for": "Large document extraction, eval reruns, classification, enrichment and non-urgent archive work.",
"watch": "Not an interactive path; queue time, file packaging, partial failures and output reconciliation become part of the route."
},
{
"lane": "OpenAI Flex processing",
"how_it_works": "Eligible calls use lower-priority capacity and are charged at Batch API rates for supported models. [E192]",
"good_for": "Long-document async work where cost matters more than immediate availability.",
"watch": "Treat it as a cheaper lane with lower priority, not as the path for a signing call, filing deadline or live customer answer."
},
{
"lane": "Anthropic Message Batches",
"how_it_works": "Bulk async requests can be priced below the standard synchronous lane and can be combined with prompt caching. [E194, E195]",
"good_for": "Large non-interactive packs: extraction, summarization, evals, source tagging and overnight review queues.",
"watch": "Batch queue behaviour and cache checkpoint design decide whether the savings survive production."
},
{
"lane": "Gemini / Vertex AI batch",
"how_it_works": "Vertex batch prediction is the non-real-time lane; Google pricing separates standard, batch and cached-input treatment. [E196, E198]",
"good_for": "Bulk document analysis, classification and back-office model calls that do not need immediate response.",
"watch": "Batch mode, provisioned throughput and cached context are separate mechanisms; do not assume one discount buys the others."
},
{
"lane": "Amazon Bedrock batch inference",
"how_it_works": "Bedrock batch inference runs async jobs over S3 inputs, with discounted batch pricing for supported models. [E199, E201]",
"good_for": "Cloud-controlled bulk inference where the input/output artefacts can live in the approved AWS path.",
"watch": "Prompt caching and batch inference are not universally combinable; model, region and feature support decide the real lane."
},
{
"lane": "Groq Batch and Flex",
"how_it_works": "Groq's Batch API is a discounted async lane; Flex processing trades immediacy for access to available capacity rather than a lower price. [E203, E204]",
"good_for": "Throughput-sensitive work where the buyer can separate background processing from live assistant use.",
"watch": "Low published developer limits can still make a clever route unusable until the account, plan or enterprise path changes."
},
{
"lane": "Usage tiers and quota requests",
"how_it_works": "Spend tiers, usage tiers, quota requests and account-specific limits can change the ceiling before any model changes. [E181, E183, E184, E185, E187, E189]",
"good_for": "New projects that need to absorb a sudden file drop, launch backlog or review queue.",
"watch": "Tier language is not capacity evidence. Check the live project, account, subscription, region and deployment type."
},
{
"lane": "Provisioned or reserved capacity",
"how_it_works": "Azure PTUs and reservations, Vertex Provisioned Throughput and Bedrock Provisioned Throughput buy predictability rather than a smarter model. [E186, E188, E191]",
"good_for": "Recurring volume, deadline-sensitive work and routes where shared-capacity 429s would be business failures.",
"watch": "Commitments, idle capacity, regional availability, spillover rules and reservation terms belong in the price model."
}
]
},
{
"id": "model-pricing-mechanics-cache-lead",
"evidenceIds": [],
"type": "paragraph",
"text": "Prompt caching is easy to misunderstand. It is not model memory and it does not cache the answer. It reuses the provider's work from the repeated beginning of a prompt: instructions, tool schemas, output schema, playbook text, matter taxonomy or source bundle. If the next request starts the same way, the provider can bill and process that repeated prefix differently. If the route prepends a timestamp, request id or one-off user sentence before the stable material, the cache may miss.",
"variant": "body"
},
{
"id": "model-pricing-mechanics-cache-design-lead",
"evidenceIds": [],
"type": "paragraph",
"text": "Design the prompt like a production artefact. Put the durable, reusable context first; put the unique question last; record cache hits in the legal model route register. Cache savings are real only when the route keeps a stable prefix and still produces accepted legal output.",
"variant": "body"
},
{
"id": "model-pricing-mechanics-cache-design-table",
"title": "How to make caching work",
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"type": "table",
"columns": [
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"label": "Design rule",
"align": "left"
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{
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{
"key": "implementation",
"label": "Implementation",
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],
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"design_rule": "Put stable material first",
"why_it_matters": "Most cache mechanisms reward an identical or explicitly marked prefix.",
"implementation": "Start with system instructions, tool schemas, output schema, jurisdiction rules, playbook text and reusable source packs."
},
{
"design_rule": "Put variable material last",
"why_it_matters": "A unique timestamp, request id, user aside or matter-specific fact at the top can break a prefix match.",
"implementation": "Append the specific question, run id, new facts, reviewer note and requested output after the cacheable prefix."
},
{
"design_rule": "Keep tools and schemas stable",
"why_it_matters": "Tool definitions, JSON schemas and message ordering can be part of the cache key.",
"implementation": "Version schemas deliberately; avoid rebuilding tool lists, whitespace, examples or column order on every call."
},
{
"design_rule": "Warm the cache before fan-out",
"why_it_matters": "Parallel extraction can waste money if every worker pays to prefill the same long context.",
"implementation": "Send one cacheable request for the source pack or playbook, then fan out clause, entity, citation or issue tasks."
},
{
"design_rule": "Cache source packs, not shortcuts",
"why_it_matters": "The cache should preserve approved context, not hide missing retrieval or stale policy.",
"implementation": "Attach source version, route version and expiry; rebuild the prefix when authority, playbook or source set changes."
},
{
"design_rule": "Measure cached-token economics",
"why_it_matters": "A cache discount that never hits is procurement fiction.",
"implementation": "Log cache reads, writes, cached tokens, hit rate, latency and accepted-output rate beside the normal token bill."
},
{
"design_rule": "Respect the data boundary",
"why_it_matters": "Caching is still provider processing under the approved route.",
"implementation": "Cache only context that is allowed inside the approved data boundary. Caching is a billing and latency control, not permission to reuse one client's facts in another client's work."
}
]
},
{
"id": "model-pricing-mechanics-provider-cache-table",
"title": "Provider caching is not one thing",
"evidenceIds": [],
"type": "table",
"columns": [
{
"key": "provider",
"label": "Provider",
"align": "left"
},
{
"key": "mechanic",
"label": "Mechanic",
"align": "left"
},
{
"key": "buyer_read",
"label": "Buyer read",
"align": "left"
}
],
"rows": [
{
"provider": "OpenAI",
"mechanic": "Automatic prompt caching applies to repeated prompt prefixes above the documented threshold; usage surfaces cached tokens, and prompt-cache keys can help route related work. [E193]",
"buyer_read": "Keep the first part of the prompt identical: instructions, tools, schema and reusable source context before matter-specific text."
},
{
"provider": "Anthropic",
"mechanic": "Claude uses explicit cache-control breakpoints over tools, system and message content, with separate cache write and read economics and documented cache durations. [E195]",
"buyer_read": "Design cache checkpoints deliberately. The best target is the long shared context, not the short one-off question."
},
{
"provider": "Gemini / Vertex AI",
"mechanic": "Vertex supports context caching, including explicit cached content and implicit prefix caching; pricing separates cached input from ordinary input and batch treatment. [E196, E197]",
"buyer_read": "Check whether the route uses explicit cache objects, implicit prefix reuse or batch prediction; each has different cost and expiry behaviour."
},
{
"provider": "Amazon Bedrock",
"mechanic": "Bedrock prompt caching uses cache points with time-to-live controls and separate cache pricing, while batch inference has its own feature constraints. [E199, E200, E201]",
"buyer_read": "Do not assume a cached interactive prompt can simply move into batch. Confirm feature support by model and region."
},
{
"provider": "Groq",
"mechanic": "Groq prompt caching is automatic for exact repeated prefixes above the documented threshold, with discounted cached tokens and separate treatment in rate-limit accounting. [E202]",
"buyer_read": "The route must keep a stable prefix. This matters most when rate limits are low and large token packets would otherwise queue."
}
]
},
{
"id": "model-pricing-mechanics-approval-lead",
"evidenceIds": [],
"type": "paragraph",
"text": "The practical question is whether the buyer can deliberately choose the cheap lane without breaking the work. A cheaper async run is useful for overnight extraction. It is dangerous for an answer needed during a signing call. A cache hit is useful when the prefix is stable. It is imaginary when every prompt is rebuilt from scratch.",
"variant": "body"
},
{
"id": "model-pricing-mechanics-approval-table",
"title": "What to prove before calling the route cheap",
"evidenceIds": [],
"type": "table",
"columns": [
{
"key": "test",
"label": "Test",
"align": "left"
},
{
"key": "proof",
"label": "Proof",
"align": "left"
},
{
"key": "pass",
"label": "Pass condition",
"align": "left"
}
],
"rows": [
{
"test": "Interactive versus async split",
"proof": "Tag each workflow step as user-blocking, same-day, overnight or archive work.",
"pass": "Only non-blocking work moves to batch, flex or lower-priority processing."
},
{
"test": "Batch economics",
"proof": "Run the same packet through interactive and batch lanes, including failed files, retries, queue time and output handling.",
"pass": "The cheaper lane still produces accepted legal output inside the work window."
},
{
"test": "Cache hit rate",
"proof": "Log cache reads, writes, cached tokens or provider-equivalent fields by route version.",
"pass": "The actual hit rate is high enough to change the finished-task bill, not just the theoretical token price."
},
{
"test": "Tier and capacity path",
"proof": "Capture account tier, quota, regional capacity, provisioned-capacity option and support escalation.",
"pass": "The buyer knows which spend, quota or reserved-capacity lever unlocks the next workload level."
},
{
"test": "Accepted-output denominator",
"proof": "Combine model spend, queue time, cache hit rate, reviewer minutes, failed runs and rework.",
"pass": "The route is cheaper per accepted legal output, not merely cheaper per million tokens."
}
]
},
{
"id": "accepted-output-cost-table",
"title": "Cost per accepted output",
"evidenceIds": [],
"type": "table",
"columns": [
{
"key": "cost_item",
"label": "Cost item",
"align": "left"
},
{
"key": "token_view",
"label": "Token view",
"align": "left"
},
{
"key": "accepted_output_view",
"label": "Accepted-output view",
"align": "left"
},
{
"key": "measurement",
"label": "Measurement",
"align": "left"
}
],
"rows": [
{
"cost_item": "Visible token price",
"token_view": "Input and output prices.",
"accepted_output_view": "Accepted legal output.",
"measurement": "Cost per accepted memo, issue, redline, extraction or citation-checked answer."
},
{
"cost_item": "Input-output mix",
"token_view": "One blended token rate.",
"accepted_output_view": "Scenario-specific bill.",
"measurement": "Separate read-only Q&A, translation, redlining, drafting and issue-list scenarios."
},
{
"cost_item": "Reasoning tokens",
"token_view": "Simple input/output price.",
"accepted_output_view": "Total billed cost.",
"measurement": "Total billed cost and pass rate on the same private legal tasks."
},
{
"cost_item": "Token efficiency",
"token_view": "Price per 1M tokens.",
"accepted_output_view": "Passed task cost.",
"measurement": "Total tokens and repair prompts per passed task."
},
{
"cost_item": "Speed and latency",
"token_view": "Cheap slow output.",
"accepted_output_view": "Usable lawyer workflow.",
"measurement": "Time to first useful draft, output tokens per second and throughput under expected concurrency."
},
{
"cost_item": "Self-hosting utilisation",
"token_view": "Low marginal inference.",
"accepted_output_view": "Fully loaded route cost.",
"measurement": "Peak, trough and idle load plus serving stack, security, patching, fallback and support."
},
{
"cost_item": "Provider route",
"token_view": "Base model price.",
"accepted_output_view": "Approved route economics.",
"measurement": "Compare direct API, cloud marketplace, router, reseller, platform and self-hosted lanes under the same controls."
},
{
"cost_item": "Review burden",
"token_view": "Model spend.",
"accepted_output_view": "Lawyer verification and rework.",
"measurement": "Reviewer minutes, rejected outputs, corrected citations and escalation rate."
}
]
},
{
"id": "model-pricing-mechanics-rule",
"title": "Pricing mechanics rule",
"evidenceIds": [],
"type": "callout",
"tone": "info",
"body": "Do not compare only the model row. First choose the lane: interactive, batch, lower-priority or flex, cached, provisioned, reserved or self-hosted. The same model can have different cost, latency, quota and review economics depending on the lane."
}
]
},
{
"id": "throughput-rate-limits",
"title": "Throughput and rate limits",
"access": "locked",
"dek": "Rate limits decide whether the legal model can survive a matter drop.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "throughput-rate-limits-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "benchmarks",
"title": "Benchmark evidence",
"access": "locked",
"dek": "Use benchmarks to find questions, not to outsource judgement.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "benchmarks-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "use-case-routing",
"title": "Use-case route selection",
"access": "locked",
"dek": "The right stack changes with the legal risk.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "use-case-routing-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "retrieval-embedding-reranking",
"title": "Retrieval, embeddings and reranking",
"access": "locked",
"dek": "The scarce resource is the legal context the model can actually use.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "retrieval-embedding-reranking-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "document-parsing-ocr",
"title": "OCR and document models",
"access": "locked",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "document-parsing-ocr-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "deployment-governance",
"title": "Deployment choices",
"access": "locked",
"dek": "The channel often matters as much as the model.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "deployment-governance-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "risk-controls",
"title": "Risk controls",
"access": "locked",
"dek": "Fluent and wrong is worse than blunt and uncertain; controls must be testable.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "risk-controls-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "cross-border",
"title": "Cross-border limits",
"access": "locked",
"dek": "A fluent answer can still route through the wrong legal system.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "cross-border-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "regulatory-export-controls",
"title": "Regulatory gates",
"access": "locked",
"dek": "Some model routes require legal clearance before use.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "regulatory-export-controls-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "model-shortlist",
"title": "Model shortlist",
"access": "locked",
"dek": "Route constraints decide which models are eligible.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "model-shortlist-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "legal-political-risk",
"title": "Legal-political risk",
"access": "locked",
"dek": "Court rulings, elections, policy posture, lobbying, and public sentiment can change the acceptable model route.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "legal-political-risk-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "operational-signals",
"title": "Operational signals",
"access": "locked",
"dek": "The dull records show whether a model is usable in production.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "operational-signals-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "providers-in-brief",
"title": "AI agents for legal workflows",
"access": "locked",
"dek": "The provider layer turns models into legal workflows.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "providers-in-brief-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "implementation-cases",
"title": "Implementation cases",
"access": "locked",
"dek": "Implementation stories show the variables a model leaderboard cannot see.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "implementation-cases-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "expert-watchlist",
"title": "Expert watchlist",
"access": "locked",
"dek": "Follow the people producing tests, tools, and failure analysis.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "expert-watchlist-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "known-unknowns",
"title": "Known unknowns",
"access": "locked",
"dek": "What public evidence cannot decide.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "known-unknowns-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "vendor-directory",
"title": "Vendor directory",
"access": "locked",
"dek": "Main model providers and inference platforms represented by source-ledger rows in this report.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "vendor-directory-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "source-ledger",
"title": "Source ledger",
"access": "locked",
"dek": "Primary sources and diligence anchors behind the analysis.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "source-ledger-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "glossary",
"title": "Glossary",
"access": "sample",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "glossary-table",
"title": "Terms used in this report",
"caption": "Definitions are reader aids, not separate evidence claims.",
"evidenceIds": [],
"type": "table",
"columns": [
{
"key": "term",
"label": "Term",
"align": "left"
},
{
"key": "meaning",
"label": "Plain meaning",
"align": "left"
}
],
"rows": [
{
"term": "Route",
"meaning": "The approved path from work to accepted output: source set, data boundary, model or vendor channel, tools, reviewer, evidence record and reapproval trigger."
},
{
"term": "API",
"meaning": "Application programming interface: a governed software connection used by an app or workflow to call a model or service."
},
{
"term": "SDK",
"meaning": "Software development kit: packaged code and tools developers use to connect a product to an API or service."
},
{
"term": "LLM",
"meaning": "Large language model: a model trained to read and generate text-like tokens."
},
{
"term": "Token",
"meaning": "A chunk of text used for billing, context windows and rate limits. It is usually smaller than a word, not a page count."
},
{
"term": "RPM",
"meaning": "Requests per minute: how many separate API calls the route can accept each minute."
},
{
"term": "RPD",
"meaning": "Requests per day: the daily cap on separate API calls."
},
{
"term": "TPM",
"meaning": "Tokens per minute: how much token traffic a route can process each minute, unless the provider splits input and output separately."
},
{
"term": "TPD",
"meaning": "Tokens per day: the daily cap on token traffic."
},
{
"term": "ITPM",
"meaning": "Input tokens per minute: how many prompt and source-document tokens may enter the model each minute."
},
{
"term": "OTPM",
"meaning": "Output tokens per minute: how many generated tokens may leave the model each minute."
},
{
"term": "PTU",
"meaning": "Provisioned throughput unit: reserved Azure OpenAI capacity bought for steadier throughput."
},
{
"term": "GSU",
"meaning": "Google scale unit: provisioned Vertex AI capacity used to reserve Gemini throughput."
},
{
"term": "MU",
"meaning": "Model Unit: Amazon Bedrock provisioned-throughput capacity reserved for a specific model route."
},
{
"term": "PayGo",
"meaning": "Pay as you go: shared cloud capacity billed by usage rather than pre-reserved throughput."
},
{
"term": "RAG",
"meaning": "Retrieval-augmented generation: search approved sources first, then pass selected evidence into the model."
},
{
"term": "OCR",
"meaning": "Optical character recognition: converting scanned or image text into machine-readable text."
},
{
"term": "VDR",
"meaning": "Virtual data room: a permissioned deal, diligence or transaction document repository."
},
{
"term": "SLA",
"meaning": "Service-level agreement: a contractual availability or support commitment, not observed uptime history."
},
{
"term": "JSON",
"meaning": "JavaScript Object Notation: a structured text format commonly used for machine-readable API output."
},
{
"term": "429",
"meaning": "HTTP 429 means too many requests: the route hit a rate limit or shared-capacity throttle."
}
]
}
]
}
]
}
},
"gap_analysis": "Independent end-to-end legal-work benchmarks with multilingual and non-US matters, stale-law checks, bad OCR, spreadsheets, privilege review, realistic matter permissions, regulatory-change events and cost per accepted output.",
"kinetics": {
"speed": "fast",
"impact": 0.88,
"momentum": 0.87,
"businessImpact": "Legal AI has moved from clever research chat to work systems that touch diligence, drafting, spreadsheets, matter knowledge, and client delivery.",
"rateOfChange": "Model releases, retrieval benchmarks, and legal AI product features now change fast enough that a one-model policy ages badly.",
"from": "chatbot trials",
"toward": "approved legal AI stacks with retrieval, document intelligence, tool controls, and lawyer review"
},
"watch_signals": [
"23 June 2026: European Commission high-risk AI consultation closes; scope could change which legal AI uses need AI Act controls [E213].",
"2 August 2026: AI Act Commission enforcement powers for GPAI providers begin; EU model routes may need stronger documentation, copyright-policy or incident-reporting controls [E212].",
"Thomson Reuters v ROSS docket entries or appeal briefing; legal-RAG licensing economics may change [E137].",
"OpenAI, Anthropic or Google model-deprecation notices; approved legal routes or cost per accepted legal answer may change."
],
"sample_findings": [
"The best legal AI strategy is model-stack approval, not a single-model mandate."
]
}
```