Cross-model benchmark
Cross-Model Legal Benchmark
HAQQ's own measured benchmark scoring legal AI models out of 50 across legal task categories.
HAQQ measured 19 models across 11 legal task categories, each scored out of 50. HAQQ's own engine (Justinian) leads most categories, including 49/50 on the top-scoring tasks, but it's not a clean sweep: Spellbook edges it out on contract drafting (46 vs. 44), LexisNexis leads legal research (46 vs. 43), and ChatGPT scores highest on plain-language explanation (45 vs. 42). Practical takeaway: no single model dominates every task, so choice should follow the workflow, not a leaderboard rank.
Les données
| Model | Overall /50 | Général | Rédaction de contrats | Recherche juridique | Explication du droit | Contrat de travail | Rédaction de mémos | Contrat de licence | Pacte d'actionnaires | Contrat de conseil | Contrat commercial | Rédaction de NDA |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| HAQQ (Justinian) | 45.8 | 49 | 44 | 43 | 42 | 48 | 44 | 46 | 47 | 45 | 47 | 49 |
| Claude Fable 5 | 42.7 | 45 | 45 | 41 | 43 | 43 | 41 | 42 | 42 | 42 | 41 | 45 |
| Claude Opus 4.7 | 40.8 | 43 | 43 | 39 | 39 | 39 | 41 | 43 | 40 | 39 | 42 | 41 |
| Mike OS | 39.1 | 42 | 39 | 35 | 33 | 41 | 39 | 38 | 41 | 40 | 40 | 42 |
| Harvey | 37.9 | 38 | 40 | 32 | 27 | 42 | 34 | 39 | 44 | 38 | 43 | 40 |
| DeepSeek v4 Pro | 36.8 | 40 | 36 | 38 | 38 | 35 | 37 | 36 | 36 | 36 | 37 | 36 |
| CoCounsel | 36.6 | 37 | 38 | 40 | 31 | 37 | 42 | 35 | 37 | 34 | 35 | 37 |
| Legora | 35.9 | 33 | 42 | 27 | 26 | 40 | 30 | 40 | 39 | 41 | 38 | 39 |
| ChatGPT 5.5 | 35.3 | 39 | 34 | 34 | 45 | 33 | 35 | 32 | 32 | 35 | 34 | 35 |
| Claude + legal plugins | 33.9 | 35 | 35 | 33 | 35 | 34 | 33 | 34 | 34 | 33 | 33 | 34 |
| Gemini 3.1 Pro | 32.9 | 36 | 32 | 36 | 41 | 30 | 32 | 31 | 30 | 30 | 31 | 33 |
| Spellbook | 32.9 | 27 | 46 | 18 | 20 | 38 | 20 | 41 | 35 | 37 | 36 | 44 |
| LexisNexis +AI | 32.0 | 36 | 29 | 46 | 28 | 30 | 38 | 28 | 31 | 27 | 29 | 30 |
| Grok 4.3 | 30.7 | 33 | 31 | 26 | 36 | 28 | 29 | 29 | 28 | 31 | 35 | 32 |
| Perplexity Sonar | 27.2 | 29 | 22 | 43 | 34 | 24 | 28 | 23 | 23 | 25 | 24 | 24 |
| Clio Duo | 25.6 | 26 | 27 | 24 | 23 | 28 | 23 | 25 | 24 | 29 | 26 | 27 |
| Meta Llama 4 | 23.8 | 24 | 23 | 23 | 29 | 23 | 24 | 22 | 22 | 24 | 23 | 25 |
| Mistral 3 | 22.4 | 22 | 25 | 20 | 25 | 21 | 22 | 24 | 20 | 22 | 22 | 23 |
| Qwen 3 Plus | 18.7 | 19 | 18 | 21 | 22 | 17 | 18 | 18 | 17 | 19 | 18 | 19 |
Données mesurées - prétraitées à partir du jeu de données d'utilisation réel de l'Index Legal AI de HAQQ (plus de 134 000 points de données dans 30 pays) ou du propre banc d'essai juridique inter-modèles /50 de HAQQ.
Recherches associées
- Multi-Model Strategy in LegalHow many AI models firms run, and the criteria they weigh when choosing.
- Risk & Hallucination RatesEstimated error rates across legal tasks, highest for citation and jurisdiction work.
- Competitive Landscape MapLegal AI vendor categories, vendor counts and growth rates across the market.