Best AI models for consulting associates (June 2026)

APEX: Consulting Associate measures how well frontier AI models perform on real consulting associate tasks.

Across sample tasks graded by expert consulting practitioners, GPT 5.2 Codex leads at 66.9% Mean Score, ahead of Gemini 3.1 Pro (66.7%) and GPT 5.4 (65.9%).

The table below shows the top 10 AI models across 100 Consulting Associate tasks.

Scores are captured as of June 2026.

View full APEX: Consulting Associate leaderboard →

Who should use the AI Model Productivity Index APEX: Consulting Associate benchmark?

The AI Model Productivity Index (APEX) for Consulting Associate tasks serves 3 types of users:

• Individual consulting associates evaluating which AI model handles market analysis, client segmentation, and quantitative modeling tasks most reliably.
• Consulting firm technology and knowledge management teams standardizing AI model deployments across practice areas. The benchmark gives them expert-graded performance data on the analytical tasks associates actually do.
• AI and professional services companies building products for consulting and professional services that need expert-graded performance data on real analytical tasks.

How are AI model productivity rankings evaluated for consulting associate tasks?

These rankings come from APEX: Consulting Associate, Mercor's benchmark for how frontier AI models perform on real consulting associate work. Unlike general reasoning benchmarks that test isolated problems, APEX: Consulting Associate evaluates models on the quantitative, multi-step analytical tasks that define associate work at top strategy and professional services firms, including data segmentation, percentile analysis, and precise numerical outputs under specific rounding rules.

What does the APEX: Consulting Associate benchmark measure?

APEX: Consulting Associate measures how well frontier AI models perform on consulting 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 McKinsey, BCG, Deloitte, Accenture, and EY, and is advised by Dominic Barton, former McKinsey Global Managing Director and former Canadian Ambassador to China.

How does APEX: Consulting Associate scoring work?

Every rubric is written by an experienced consulting practitioner who defines what a correct or complete analytical 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 computes some percentile cutoffs and segment counts but misses others earns partial credit reflecting the value of what it got right.

This scoring method is appropriate for consulting tasks where partial correctness still has value: an analysis that correctly categorizes some merchant segments but miscalculates others is more useful than one that gets everything wrong, though in client-facing work a single wrong cutoff can undermine the entire recommendation.

What type of task is evaluated by the AI Model Productivity Index APEX: Consulting Associate benchmark?

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

Sample task: merchant loyalty and size classification for a food delivery client

A food delivery app is analyzing merchant loyalty and performance in the Mexican food industry. The model must classify merchants by size (small, medium, big) using total delivery revenue percentiles, and by loyalty (low, average, high) using share-of-wallet percentiles. It must state the exact revenue and share-of-wallet cutoffs, count the merchants in each of the resulting groups, identify which loyalty-size pairings are unbalanced under a 10% to 13% threshold, and propose a cutoff change to rebalance them.

The rubric has 14 criteria. On this sample task, the top model satisfied 1 of the 14: it correctly proposed moving the cutoff share of wallet between low and average loyalty to 35% to rebalance the groups, but missed every revenue cutoff, share-of-wallet cutoff, and merchant count leading up to that conclusion.

What are the hardest consulting associate tasks for AI models?

Multi-step quantitative analysis with precise percentile cutoffs and segment counts remains the hardest category of task on APEX: Consulting Associate. GPT 5.2 Codex leads at 66.9% Mean Score. The bottom of the ranked models, GLM 5 Thinking at 45.3% and Sonnet 4.6 at 41.9%, shows that a 25-point spread exists between the strongest and weakest models currently evaluated.

3 types of analytical steps consistently separate stronger models from weaker ones, based on the sample task:

• Precise percentile cutoff calculation. Computing the exact revenue value at the 33rd and 67th percentiles to the nearest cent, from raw source data, requires accurate data handling that models frequently get wrong.
• Consistent segment counting. Once cutoffs are set, correctly counting how many merchants fall into each size and loyalty bucket depends entirely on getting the cutoffs right first, so a single upstream error propagates through every count.
• Cross-constraint rebalancing. Identifying which groups are unbalanced and proposing a single cutoff change that applies consistently across all segments requires holding multiple constraints simultaneously, the step where partial-credit scoring most often applies.

Senior consultants still outperform AI models on integrated quantitative deliverables. The judgment required to structure a segmentation, validate the numbers, and defend the cutoffs to a client remains largely the preserve of human expertise.

Do AI models or AI tools for consulting tasks matter more?

Both matter, but they play different roles. The underlying model like GPT 5.2 Codex, Gemini 3.1 Pro, or GPT 5.4 determines the quality of quantitative reasoning and analysis. The tool like a data visualization platform, a presentation builder, or a firm-specific research assistant wraps that capability in context, gives the model access to client data and templates, and delivers output into the associate's workflow.

A well-integrated tool cannot compensate for a weak underlying model on tasks requiring precise percentile analysis and segment counting. Better chart templates do not fix a model's failure to compute the correct revenue cutoff from raw data. However, a good tool amplifies what the underlying model offers, particularly by giving structured access to client datasets and industry benchmarks that models need to analyze correctly.

For research summarization and narrative drafting, the difference between the top models narrows and your tool and workflow often decide the result.

For the quantitative analysis The AI Model Productivity Index (APEX) measures precise data segmentation with exact cutoffs and counts, the underlying model's analytical capability is the deciding factor. Pick the model for the ceiling you need on your most demanding analyses; pick the tool for how effectively it integrates into your firm's delivery workflow.

Get the full APEX: Consulting Associate dataset

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