Vision-language models (VLMs) are advancing rapidly. Top models today can describe images, answer visual questions, and outperform baselines across academic benchmarks. But despite this progress, they consistently fall short on the kind of reasoning required in real business and scientific environments.

That performance gap isn’t just theoretical—it limits how these models can be trusted in workflows that support real decisions.

Most benchmarks don’t reflect real-world tasks

Today’s common benchmarks tend to focus on generic vision-language challenges:

Labeling objects or answering trivia questions about a photo
Matching captions with images
Describing everyday visual scenes
Conceptual understanding in some subjects

While helpful for measuring baseline capabilities, these tasks don’t simulate the questions a researcher or analyst might ask in the field.

Consider what’s missing:

A biologist analyzing patterns in growth curves from a microscope image
A CFO interpreting an outlier in quarterly financial data
An engineer calculating shear stress from a structural diagram

These aren’t recognition tasks—they’re reasoning tasks grounded in domain knowledge and decision-making. Most existing benchmarks never touch them.

What real-world evaluation actually requires

If we want VLMs to support professionals, we need benchmarks that challenge models to:

Extract and synthesize data from charts, diagrams, or tables
Solve open-ended questions that require interpretation, not guessing
Handle specialized terminology and technical imagery
Justify answers clearly and consistently—not just generate fluent text or pick an option among multiple choices for the right answer.

That means moving beyond multiple choice. It means testing the ability to explain, calculate, compare, and predict—often across multiple steps and modalities.

A new direction for evaluating VLMs

Turing’s Research team has created a benchmark built specifically for business and STEM tasks. It focuses on reasoning under domain constraints, using real visual artifacts and practical, open-ended prompts.

“We designed this benchmark to mirror how professionals actually think and solve problems—not how academic datasets quiz models.”
— Mahesh Joshi, Head of Research, Turing

The evaluation pipeline uses LLM-based judging across multiple generated answers, creating a richer signal than exact-match scoring or right/wrong labels. The goal isn’t to create a leaderboard—it’s to drive real-world readiness.

What’s next

If you're working with vision-language models and care about domain-specific performance on real-world tasks and workflows, we're diving deeper into this benchmark design—how it works, what it tests, and what early results tell us about current VLM limits.

Read: Evaluating VLMs On Real Business And STEM Tasks →