The AmLaw 100 Math: $400 × 200 Lawyers Is Not the Right Number
Harvey lists around $300–500 per lawyer per month. Thomson Reuters' Co:Counsel runs $200–500. A 200-lawyer firm staring at a $400/seat bill is looking at $80,000 per month — close to $1M per year — for AI that almost certainly doesn't get used by every seat every day.
The pricing model is borrowed from legal-research databases (Westlaw, LexisNexis) — where every lawyer needs occasional access and the per-seat fee is a rounding error on a $1M+ partner. For AI doing the actual work — first-pass contract review, due-diligence summaries, deposition prep — the seat assumption breaks. Most lawyers touch it once a week; a few use it constantly.
Token pricing aligns the bill to the work. Self-hosting puts the privileged data behind your own firewall. The math is the post.
What the Latest Models Actually Cost in 2026
Token pricing across the major providers, approximate as of mid-2026:
| Model | Provider | Input ($/MTok) | Output ($/MTok) | Best for |
|---|---|---|---|---|
| Claude Opus 4.7 | Anthropic | $15 | $75 | Complex contract analysis, brief-writing |
| Claude Sonnet 4.6 | Anthropic | $3 | $15 | First-pass review, summarization |
| Claude Haiku 4.5 | Anthropic | $1 | $5 | Document classification, intake triage |
| GPT-5 | OpenAI | $10 | $30 | Deposition prep, legal research |
| Gemini 3 Pro | $3.50 | $10.50 | Long-context review (multi-MM token files) | |
| Llama 4 (70B, self-hosted) | Meta (open weights) | ~$0 | ~$0 | Privileged work behind firewall |
| DeepSeek-R1 (self-hosted) | DeepSeek (open weights) | ~$0 | ~$0 | Cost-sensitive bulk review |
For self-hosted open-weight models, the marginal cost is GPU time. A reserved H100 instance ($1.50–3/hour) handles tens of thousands of contract pages per day.
A Real Workload: Due-Diligence Contract Review at a 200-Lawyer Firm
Take a mid-market M&A practice running first-pass contract review on a typical deal: 5,000 documents per deal, 6 deals per month = 30,000 contracts per month. Each contract is roughly 3,000 input tokens (the document) and 800 output tokens (the structured summary with flagged clauses and risk classifications). For a deeper per-contract cost breakdown — including a side-by-side against Harvey, Co:Counsel, Spellbook, Ironclad AI, and LinkSquares at three scale tiers (solo / mid-market / AmLaw) — see What AI Contract Review Actually Costs in 2026.
That's 90M input + 24M output tokens per month across the entire firm — front-loaded onto the M&A and corporate teams that actually run diligence.
What it costs by deployment shape
| Deployment | Pricing shape | Monthly cost | Annual | Privilege posture |
|---|---|---|---|---|
| Harvey | Per-lawyer (~$400) | $80,000 | $960,000 | Harvey-hosted (their cloud, their model choice) |
| Thomson Reuters Co:Counsel | Per-lawyer (~$300) | $60,000 | $720,000 | TR-hosted (their cloud, their model choice) |
| ChatGPT Enterprise | Per-seat ($60/user, ~250 staff) | $15,000 | $180,000 | OpenAI cloud (general-purpose, not legal-tuned) |
| Direct API — Claude Sonnet 4.6 | Token-based | ~$630 | ~$7,560 | Anthropic cloud (firm DPA) |
| Direct API — GPT-5 | Token-based | ~$1,620 | ~$19,440 | OpenAI cloud (firm DPA) |
| ibl.ai self-hosted (Llama 4 / DeepSeek-R1) | Flat license + GPU | ~$5,000–8,000 | ~$60,000–96,000 | Inside the firm's network (privilege intact) |
The ibl.ai row covers the GPU instance, the platform license, and ongoing support. There is no third-party vendor in the data path, no managed-cloud DPA to renegotiate, and no question about whether the model provider could be served with a subpoena that reaches your privileged work product.
Why Per-Seat Pricing Fails Harder in Legal
Three reasons the seat model breaks for law firms specifically:
1. Usage is bimodal. M&A, corporate, and litigation eDiscovery teams hit AI constantly. Real estate, T&E, and most non-transactional groups touch it occasionally. Buying the same seat for both means the heavy users subsidize the firm's average — but the firm pays the seat fee for everyone.
2. The "lawyer count" you're billed on includes a lot of non-billable seats. Paralegals, document-review attorneys, and staff are often counted at the same per-seat rate even though their billing rate is a fraction of a partner's. The vendor's "per-lawyer" pricing is really "per-headcount."
3. Privilege is structurally incompatible with managed clouds. ABA Model Rule 1.6 obligates lawyers to make "reasonable efforts to prevent the inadvertent or unauthorized disclosure of" client information. Sending privileged documents to a third-party AI vendor — even one with a DPA and SOC 2 — introduces a custody question that doesn't exist when the model runs on the firm's infrastructure. Several jurisdictions are now writing this into formal opinions.
What Stays the Same, What Changes
Self-hosting the runtime doesn't mean rebuilding the firm's tooling. The chat UI, the matter-scoped workspaces, the citation-checking, the document-management integration (iManage, NetDocuments, SharePoint), the multi-agent orchestration — all of that stays managed by ibl.ai. The compute, the model, and the privileged data move inside the firm's network.
What disappears: the $1M/year per-seat line item. What appears: an internal capability the firm owns and controls, with the same agent-orchestration platform and the freedom to pick the model that fits each workload — Opus for the brief, Sonnet for the contract sweep, an open-weight model for the bulk diligence run.
Run the Numbers for Your Firm
For workload sizing and cost modeling specific to your practice areas, the AI Help Desk Cost Savings Calculator generalizes to most high-volume legal-administrative workloads (intake triage, document classification, first-pass review).
For the deployment comparison side-by-side — including ABA Model Rule 1.6 posture, privilege protection, and air-gapped options for the most sensitive matters — see Self-Hosted AI vs ChatGPT Enterprise for Legal.
For the broader policy framework — what a law-firm AI policy should cover and why owned/air-gapped deployment is the control that makes it enforceable — see AI Policies for Law Firms: A Practical 2026 Guide.
Why Family-Owned and New York Matters Here
A law firm's AI vendor relationship is a long-term commitment — the workflows, the prompts, the integration with the document-management system, the audit logs the firm relies on for malpractice defense. ibl.ai is family-owned and operated from New York, NY — a long-term partner with a perpetual platform license and no investor exit pressure. The runtime is open source. The privileged data stays inside the firm's network. The math works at 50 lawyers or 2,000.