The Future of Multimodal AI Benchmarks: Evaluating Agents Beyond Text

The Shift Toward Multimodal Intelligence

Artificial intelligence is undergoing a profound transformation. Once defined by text-based models capable of generating language or summarizing information, today’s systems are expanding into multimodal intelligence — the ability to process text, images, audio, video, and environmental data simultaneously.

This shift enables the rise of AI agents that can perceive, reason, and act — interpreting visual scenes, responding to spoken input, or even controlling digital and physical environments. From autonomous robots to intelligent assistants and simulation agents, these systems represent a more complete form of intelligence.

But one question drives the field forward: how do we measure their performance?

Why Text-Only Benchmarks Fall Short — and How Multimodal Ones Are Rising

For several years, AI benchmarks such as SQuAD, GLUE, and SuperGLUE set the standard for evaluating natural language understanding. These datasets helped researchers measure accuracy, reasoning, and generalization in text-based tasks — but they only captured one dimension of intelligence.

As AI expands into perception, embodiment, and interaction, text-only benchmarks no longer tell the full story. They fail to evaluate:

• Visual reasoning — interpreting images, diagrams, or spatial layouts
• Temporal and sensory understanding — reasoning across video or real-time input
• Action and interaction — taking steps, using tools, navigating interfaces
• Multi-agent strategy — coordination, communication, and adaptation in dynamic environments

To address these gaps, next-generation multimodal AI benchmarks have emerged, combining academic and industry-led innovations.

Innovative Benchmarks from Research Labs

• Agent-X (2025) – Vision-centric, multi-step reasoning and tool use in dynamic visual scenes
• VS-Bench (2025) – Strategic reasoning in multi-agent, multimodal environments
• VisualWebArena (2024) – Web navigation and perception across text, image, and layout inputs

Innovative Benchmarks from 2077AI

• OmniHD-Scenes – Combines high-definition video, LiDAR, and 4D imaging radar data for multimodal environmental perception in autonomous driving
• TaskCraft – Multi-step, multimodal task reasoning with visual cues and controlled tool interactions
• VeriGUI – Evaluates agents interacting with user interfaces, testing perception-action accuracy and long-horizon task execution on desktop and web-based GUIs

Comparison – OmniHD‑Scenes vs. Publicly Available Autonomous Driving Datasets

Together, these frameworks represent the next generation of AI benchmarking, testing perception, reasoning, and action in real-world contexts.

Early results show progress but also highlight persistent challenges. Even top-performing models achieve less than 50% full-chain success in reasoning-intensive environments like Agent-X. In VS-Bench, prediction accuracy remains below 50%, demonstrating that multi-step planning, long-horizon reasoning, and cross-modal integration are still difficult to master.

How Abaka AI Can Help

At Abaka AI, we understand that as AI systems evolve, evaluation must evolve too. True intelligence can’t be measured by a single accuracy score — it must be tested across perception, reasoning, and action.

As a core contributor to 2077AI, the open-source community advancing benchmark datasets, Abaka AI provides:

• Precurated multimodal datasets
• High-quality data collection and annotation
• Model training and evaluation frameworks

Together with our partners, we’re building a future where reliable benchmarking forms the foundation of trust, innovation, and collaboration in AI.

If you’re interested in off-the-shelf datasets, model training, evaluation, or potential collaboration, contact Abaka AI to start the conversation. Together, we can shape how the next era of AI is measured, understood, and trusted.