Adoption & maturity
Multi-Model Strategy in Legal
How many AI models firms run, and the criteria they weigh when choosing.
A directional estimate suggests firms rarely rely on a single model once they scale. Solo practitioners average an estimated 1.4 models, while Big Law firms (50+ lawyers) run closer to 3.5, with in-house teams in between at 2.6. The pattern points to multi-model stacks as a function of size and risk exposure, not preference: larger firms appear to route different work (drafting, research, review) to different models rather than standardizing on one, likely to balance accuracy, cost, and data-privacy tradeoffs across practice areas. For smaller firms, this scenario implies less redundancy and more dependence on whichever single model they've adopted.
Les données
| Firm type | Models per firm |
|---|---|
| Solo | 1.4 |
| Small (2-10) | 2.1 |
| Mid (11-50) | 2.8 |
| Big Law (50+) | 3.5 |
| In-house | 2.6 |
Estimation illustrative - un chiffre indicatif pour la mise en scène de scénarios, non une référence mesurée. Ne pas lire ces résultats comme des mesures par fournisseur.
Recherches associées
- Cross-Model Legal BenchmarkHAQQ's own measured benchmark scoring legal AI models out of 50 across legal task categories.
- Competitive Landscape MapLegal AI vendor categories, vendor counts and growth rates across the market.
- Data Security & CompliancePublicly documented security certifications across major AI providers.