Industry · Legal
Quantify the real cost of AI-enabled contract review, drafting and research across an entire law firm.
Legal AI workloads are among the most context-heavy in the enterprise — contracts, case law, and discovery sets routinely push 50K–200K input tokens per interaction. This calculator models annual AI OpEx for partners, associates and paralegals across the firm.
Tokens / attorney / yr
25–90M
Document-heavy workflows
Annual cost / attorney
$200–$2,200
Frontier model usage
Input / output split
90% / 10%
Highest in the enterprise
Peak doc context size
100K+ tokens
Per contract review
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Embedded in core workflows. Mixed adoption.
AI-assisted tasks
3 / 3Annual estimate
$23.1K
Total token OpEx for 150 employees · Claude Sonnet 4
Key cost considerations for legal
- Confidentiality requirements push many firms toward private deployments — factor in 1.5–3x cost premium.
- Long-context models (Claude Sonnet, Gemini 2.5 Pro) reduce chunking overhead and total tokens consumed.
- Citation-grounded RAG indexing should be modeled as a persistent annual baseline.
- Senior partner review uses high-end models sparingly but reliably — keep frontier capacity available.
Primary AI workloads in this industry
- AI contract review cost calculator — 30K – 200K tokens / event, $0.30 – $5.00 per contract.
- AI legal research cost calculator — 20K – 80K tokens / event, $0.20 – $2.00 per research task.
- AI drafting and writing cost calculator — 3K – 15K tokens / event, $0.01 – $0.20 per draft.
- RAG cost estimator — 5K – 50K tokens / event, $0.01 – $0.20 per query.
Frequently asked questions
›How much does AI cost per lawyer per year?
Mid-sized firms report $200–$700 per attorney per year on balanced models, rising to $1,500–$2,200 when frontier models like Claude Opus 4 are used for complex matter analysis.
›Which AI model is most cost-effective for legal work?
Claude Sonnet 4 and Gemini 2.5 Pro offer the best balance of long-context reasoning and price for contract review. Reserve Claude Opus 4 or GPT-5 for novel legal questions where accuracy materially affects outcomes.
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