Best AI models for Big Law associates (June 2026)

APEX: Big Law Associate measures how well frontier AI models perform on real Big Law associate tasks. Across sample tasks graded by expert law practitioners, GPT 5 leads at 76.6% Mean Score, ahead of Opus 4.6 (76.4%) and Opus 4.6 Max (76.2%).

The table below shows the top 10 AI models across 100 Big Law Associate tasks. Scores are captured as of June 2026.

View full APEX: Big Law Associate leaderboard →

Who should use APEX: Big Law Associate?

AI model productivity APEX leaderboard for Big Law Associate tasks serves 3 types of users:

• Individual Big Law associates evaluating which AI model handles complex legal drafting, contract analysis, and regulatory compliance tasks most reliably.
• Law firm technology and knowledge management teams standardizing AI model deployments across practice groups. The benchmark gives them expert-graded performance data on the drafting and analysis tasks associates actually do.
• Legal AI companies building products for Big Law and corporate legal work that need expert-graded performance data on real legal drafting tasks.

How are AI model productivity rankings evaluated for Big Law associates?

These rankings come from APEX: Big Law Associate, Mercor's benchmark for how frontier AI models perform on real Big Law associate work. Unlike general legal AI benchmarks that test isolated research queries, APEX: Big Law Associate evaluates models on the multi-issue drafting and analysis tasks that define associate work at elite law firms, including contract drafting with regulatory and cross-jurisdictional considerations.

What does the APEX: Big Law Associate benchmark measure?

APEX: Big Law Associate measures how well frontier AI models perform on Big Law associate tasks across 100 held-out prompts. Each task is passed to models with relevant source documents, mirroring how associates actually work. Tasks are graded by human-authored rubrics and scored as Mean Score, which awards partial credit for each criterion a model satisfies.

The benchmark was built by practitioners from Latham & Watkins, Skadden, and Cravath, and is advised by Cass Sunstein, a Harvard law professor, former White House Office of Information and Regulatory Affairs Administrator, and one of the most-cited legal scholars in the US.

How does APEX: Big Law Associate scoring work?

Every rubric is written by an experienced Big Law practitioner who defines what a correct or complete legal output looks like for that task. Unlike a binary pass/fail, Mean Score awards credit for each individual rubric criterion a model satisfies. A model that correctly handles 8 of 12 contract provisions scores 66.7% on that task, reflecting the partial value of getting most provisions right even while missing others.

This scoring method is appropriate for legal drafting tasks where partial compliance still has value: a contract that correctly addresses export controls, liability, and dangerous-goods regulations but misses insurance and notice provisions is more useful than a contract that addresses none of them.

What type of task is evaluated by the APEX: Big Law Associate benchmark?

Here’s one of the sample tasks that was evaluated within this domain:

Sample task: contract drafting for an international shipment of a university prototype

A Virginia logistics company has been asked by a public university to design, build, and ship a protective crate for a student-engineered prototype headed to Dubai for an academic exhibition. The prototype contains defense contractor components fabricated under a Department of Energy grant. The model must draft an agreement that protects the logistics company against regulatory, operational, and liability exposure, while keeping the arrangement commercially workable and legally enforceable under applicable federal and state law.

The rubric has 12 criteria covering export controls, government procurement conditions, liability, insurance, dangerous-goods regulations, and wood-packaging standards. On this sample task, the top model (GPT 5) satisfied 8 of the 12 criteria: it correctly addressed University's role as exporter of record, export documentation requirements, identification of export-controlled technical data, dangerous-goods certification, ISPM-15 wood-packaging compliance, Company's negligence-limited liability, trade-compliance suspension rights, and reimbursement for concealed hazards.

GPT 5 missed 4 criteria: conditioning University's payment on appropriated funds availability (a requirement for public university contracts), limiting Company's scope to packaging services only, requiring University to provide written claim notice within a defined period, and requiring University to obtain cargo insurance with at least 3 specified protections.

What are the hardest Big Law associate tasks for AI models?

Complex contract drafting with multi-jurisdictional regulatory exposure remains the hardest category of task on APEX: Big Law Associate. GPT 5 leads at 76.6% Mean Score. The bottom of the ranked models, GLM 4.7 at 51.6% and GLM 5 Thinking at 50.0%, shows that a 26-point spread exists between the strongest and weakest models currently evaluated.

4 types of provisions consistently separate stronger models from weaker ones, based on what the top model missed on the sample task:

• Government procurement conditions. Public university contracts require appropriated funds clauses that condition payment on availability of legislative appropriation. Models unfamiliar with government contracting doctrine omit this entirely.
• Scope limitation clauses. Clearly limiting a service provider to its specific scope of work (packaging only, not the goods themselves) requires precise drafting that models often broaden inadvertently when describing the parties' arrangement.
• Procedural notice requirements. Written claim notice provisions with defined time periods are standard protective clauses in commercial contracts that models frequently omit despite their importance in limiting liability exposure.
• Insurance specification clauses. Requirements for cargo insurance with specific named protections (additional insured status, waiver of subrogation, insurer rating) require technical precision in insurance drafting that tests the limits of current model capability.

Senior Big Law partners still outperform AI models on high-stakes multi-issue contract drafting. The legal judgment required to identify which provisions are essential for a specific fact pattern, and to draft them in a way that is enforceable under applicable law, remains largely the preserve of human expertise.

Do AI models or AI tools for Big Law matter more?

Both matter, but they play different roles. The underlying model like GPT 5, Opus 4.6, or o3 determines the quality of legal reasoning and drafting. The tool like a contract review platform, a legal research database, or a firm-specific drafting assistant wraps that capability in context, gives the model access to source documents and precedent, and delivers output into the associate's workflow.

A well-integrated tool cannot compensate for a weak underlying model on tasks requiring precise legal drafting across multiple regulatory regimes. Access to better source documents does not fix a model's failure to identify that a public university contract requires an appropriated funds clause. However, a good tool amplifies what the underlying model offers, particularly by providing the relevant statutory authority and precedent agreements that models need to draft correctly.

For legal research summarization and routine contract review, the difference between the top models narrows and your tool and workflow often decide the result.

For the multi-issue drafting work APEX measures, complex commercial agreements with cross-jurisdictional regulatory exposure, the underlying model's legal reasoning capability is the deciding factor. Pick the model for the ceiling you need on your most demanding matters; pick the tool for how effectively it integrates into your firm's review and drafting workflow.

Get the full APEX: Big Law Associate dataset

The public leaderboard shows the scores. The full dataset is what law firm technology teams and legal AI companies use to make real deployment decisions, including task-level breakdowns and rubric details across all 100 tasks.