Multimodal large language models are transforming how AI interprets visual content, but their capabilities remain difficult to measure consistently. This challenge creates a significant gap between promising research advancements and practical implementation in production environments. Understanding how to properly evaluate these models is crucial for anyone building AI products that integrate text and visual understanding.

This article examines comprehensive benchmarks specifically designed to assess how well multimodal LLMs understand visual information. We'll explore specialized evaluation frameworks like ChartQA for data visualization interpretation, DocVQA for document comprehension, and advanced benchmarks measuring multi-image reasoning capabilities.

These insights will help you implement more reliable evaluation protocols, identify capability gaps in current models, and make informed decisions about which multimodal systems best suit your specific product requirements. The frameworks discussed provide actionable approaches to quantifying model performance beyond simple accuracy metrics.

Key Topics Covered:

1. 1
   Core architectural approaches for multimodal LLMs
2. 2
   Key benchmarks: ChartQA, DocVQA, TextVQA, and MMMU
3. 3
   Performance measurement with specialized metrics (ANLS, F1-score, hallucination rates)
4. 4
   Domain-specific evaluation frameworks
5. 5
   Balancing automated evaluation with human assessment

What are multimodal LLMs for image understanding

Let's begin by exploring the fundamental architecture and capabilities that enable multimodal LLMs to process and understand visual information.

Multimodal large language models (LLMs) employ two primary architectural approaches for integrating vision and language capabilities. Vision-language models combine separate visual and textual encoders, while multimodal transformers incorporate both modalities within a unified architecture. These design choices significantly impact how models process and understand visual information.

The implementation of visual-language integration typically leverages CLIP embeddings, which map images into the same semantic space as text. This approach enables models to establish connections between visual elements and linguistic concepts. Alternatively, some models use visual token projection, transforming image patches into tokens that can be processed alongside text tokens in the model's architecture.

Cross-attention mechanisms play a crucial role in image understanding by allowing the model to focus on relevant parts of an image while processing text. 

These mechanisms enable bidirectional information flow between modalities, helping models interpret visual content in context with textual queries or instructions. These architectural foundations are essential for enabling the sophisticated visual processing that modern multimodal systems require.

Core capabilities for effective image understanding

Now, let's examine the specific capabilities that multimodal LLMs must develop to effectively process and understand visual information.

Successful multimodal LLMs must master several key technical capabilities. Optical character recognition (OCR) allows models to extract and interpret text embedded within images, such as labels, captions, or document content. This capability is essential for tasks like document understanding and chart interpretation.

Spatial reasoning enables models to comprehend the relative positions and relationships between objects in an image. This capability helps models answer questions about location, proximity, and spatial arrangements.

Context integration represents another critical skill, allowing models to combine visual information with broader knowledge and understanding. Models must connect what they "see" with what they "know" to provide meaningful interpretations of visual content. Mastering these core capabilities is what ultimately enables multimodal LLMs to perform sophisticated visual reasoning tasks.

Recent advancements in benchmarks

The field has seen significant progress in benchmark development aimed at measuring these multimodal capabilities more effectively.

Image understanding benchmarks have evolved significantly through 2024-2025. ChartQA evaluates an LLM’s ability to interpret data visualizations by combining visual perception with numerical reasoning. DocVQA tests document understanding by requiring models to answer questions based on both textual and visual elements.

MMBench introduces progressively challenging tasks, from basic visual recognition to complex data fusion problems requiring integrated understanding of text and images. This benchmark focuses on dynamic, real-time interactions, such as describing evolving visual trends.

Recent research shows multimodal LLMs have improved significantly but still struggle with complex visual reasoning compared to humans. Even top-performing models like GPT-4V occasionally falter when faced with context-heavy visual reasoning tasks, highlighting areas where multimodal alignment remains incomplete. These benchmark advancements help identify both the progress made and the remaining gaps in multimodal LLM capabilities.

Key image understanding benchmarks and evaluation metrics

With the foundational concepts established, let’s examine the specific benchmarks that have become standard tools for evaluating multimodal LLMs' visual understanding capabilities.

ChartQA, DocVQA, and TextVQA benchmarks

Image understanding benchmarks evaluate multimodal LLMs' ability to comprehend and reason about visual content. ChartQA focuses on interpreting charts and graphs, requiring both visual perception and numerical reasoning skills. DocVQA evaluates document understanding by requiring models to answer questions about text and visual elements in documents. TextVQA assesses how well models can answer questions that require reading text within images.

These benchmarks have evolved significantly through 2024-2025, with new metrics focusing on both accuracy and reasoning patterns. Each benchmark provides specific datasets with unique characteristics:

Benchmark Comparison:

These specialized benchmarks provide targeted evaluation of different aspects of visual understanding, allowing researchers and developers to pinpoint specific strengths and weaknesses in multimodal models.

Specialized evaluation metrics

Beyond the benchmarks themselves, a range of sophisticated metrics has emerged to provide more nuanced evaluation of model performance.

Evaluation frameworks now incorporate diverse metrics to assess model performance thoroughly:

• ANLS (Average Normalized Levenshtein Similarity): Primary metric for DocVQA that measures text similarity while accounting for minor variations
• Accuracy: Standard measure of correct responses across all benchmarks
• F1-score: Balances precision and recall for answer evaluation
• Reasoning quality: Assesses the logical steps models take to reach conclusions
• Hallucination rates: Measures when models generate incorrect information not present in images
• Consistency: Evaluates whether models provide stable answers across similar queries

Human evaluation remains crucial for assessing nuanced understanding despite the development of automated metrics. This multifaceted approach to evaluation provides a more comprehensive assessment of model capabilities than simple accuracy metrics alone.

Technical implementation requirements

Implementing visual understanding benchmarks requires careful consideration of several technical factors.

Implementing these benchmarks requires specific technical considerations:

1. 1
   Integration with multimodal model architectures to process both text and visual inputs
2. 2
   Preprocessing pipelines for different image types (charts, documents, natural scenes)
3. 3
   Custom evaluation protocols for each metric type
4. 4
   Support for both end-to-end evaluation and component-level assessment

The evaluation frameworks must accommodate various image formats, text extraction methods, and reasoning approaches. These technical requirements highlight the complexity involved in properly evaluating multimodal LLMs.

Real-world applications

The benchmarks we've discussed have direct relevance to practical applications of multimodal AI.

Current benchmarks increasingly focus on real-world applications, demonstrating a correlation between benchmark design and practical requirements:

• Educational tools using chart understanding for learning assessment
• Document processing systems for business workflows
• Visual search applications requiring text recognition

Models that perform well on these benchmarks show improved capabilities in practical document and chart understanding tasks, though they still struggle with complex visual reasoning compared to humans.

Recent research shows multimodal LLMs demonstrating improved performance on these benchmarks, particularly in understanding diagrams, infographics, and specialized visual domains like scientific figures.

Human language model evaluation

The image understanding field continues to evolve through human-AI collaboration, with expert evaluators providing insights that complement automated metrics, ensuring models develop capabilities that truly serve user needs.

Human language model evaluation remains a critical component in the assessment process, especially for nuanced tasks involving charts and documents where context and domain knowledge significantly impact performance. This combination of benchmark performance and real-world application testing provides the most comprehensive assessment of multimodal capabilities.

Multi-image and complex reasoning benchmarks

As multimodal models advance, evaluation has expanded to more complex tasks involving multiple images and sophisticated reasoning requirements.

Recent benchmarks have evolved to evaluate multimodal LLMs on more complex visual reasoning tasks requiring temporal and multiview relationships. MuirBench offers a comprehensive 12-task evaluation framework designed to assess how models interpret relationships across multiple images.

MIBench/MIRB implements a four-tier reasoning evaluation framework that systematically tests models' abilities to:

1. 1
   Process temporal sequences
2. 2
   Compare visual elements across multiple images
3. 3
   Identify patterns within image sets
4. 4
   Integrate information from diverse visual contexts

These advanced benchmarks push beyond single-image understanding to evaluate more sophisticated multimodal reasoning capabilities.

Performance gaps in multi-chart reasoning

Despite significant progress, current models still face substantial challenges with multi-image reasoning tasks.

Current models demonstrate significant limitations when handling multi-image reasoning tasks. Quantitative assessments reveal substantial performance gaps between human-level understanding and even the most advanced multimodal LLMs.

Key Limitations:

• Models struggle with temporal reasoning across image sequences
• Information integration from multiple charts remains challenging
• Correlation and causation relationships between visuals are poorly understood
• Complex visual narratives often lead to inconsistent responses

These limitations highlight the significant work still needed to achieve human-like capabilities in complex visual reasoning scenarios.

Technical evaluation challenges

Evaluating multi-image reasoning capabilities presents unique technical challenges.

Assessing sequential reasoning across multiple images presents unique evaluation difficulties. MultiChartQA exposes these challenges by requiring models to:

• Track information across diverse visual elements
• Maintain contextual awareness between related images
• Draw conclusions based on distributed visual information
• Generate coherent responses that integrate multi-image context

These benchmarks are critical for advancing multimodal systems that can reason effectively across complex visual relationships rather than just interpreting single images in isolation. Addressing these technical challenges is essential for developing more comprehensive evaluation methodologies.

Domain-specific visual evaluation frameworks

Beyond general visual understanding, specialized benchmarks have emerged to evaluate performance in specific domains and professional contexts.

Domain-specific visual evaluation frameworks rigorously assess multimodal LLMs across specialized fields. MMMU, a comprehensive benchmark, comprises 11,500 multimodal questions from college-level materials spanning 30 subjects and 183 subfields. It features diverse image types including charts, diagrams, and chemical structures, challenging models to perform expert-level visual reasoning tasks.

The newest models from early 2025 show much better scores on MMMU than previous generations. Models such as GPT-4V and Gemini Ultra achieve only 56-59% in mid-2024. Llama 4 Behemoth leads at 76.1%, showing that size still matters for complex reasoning tasks.

MMBench provides another approach, evaluating vision-language integration across progressively challenging tasks. It tests everything from basic visual recognition to complex data fusion problems requiring coordinated text-visual processing. These domain-specific benchmarks reveal how models perform in specialized knowledge contexts requiring visual understanding and domain expertise.

Specialized scientific assessment approaches

Scientific and technical domains present particularly challenging visual understanding requirements.

CharXiv offers a targeted methodology for evaluating scientific understanding through technical document comprehension. It requires models to process complex diagrams, equations, and specialized notation common in research papers.

InfoVQA specifically tests infographic comprehension, measuring how well models extract structured information from visual data representations. This capability is crucial for business analytics and data interpretation tasks.

A single evaluation framework cannot fully capture visual reasoning abilities. These specialized scientific assessment approaches provide targeted evaluation of the sophisticated visual reasoning required in technical and scientific contexts.

Performance correlations with domain expertise

Research has revealed important correlations between domain knowledge and visual understanding performance.

Research also shows a strong correlation between domain expertise and performance on specialized visual tasks. Models trained with domain-specific knowledge consistently outperform general-purpose models on visual reasoning tasks requiring specialized expertise.

The most effective evaluation approaches combine domain-expert assessment with automated metrics to provide comprehensive measurement of visual understanding capabilities.

These frameworks reveal that even top-performing models struggle with complex, context-heavy visual reasoning tasks, highlighting areas where multimodal alignment remains incomplete. This connection between domain knowledge and visual understanding performance has important implications for model training and application development.

Recent Model Performance Comparison

Understanding the Multimodal LLM Leaderboard

The table above shows how different AI models perform on visual understanding tasks. Let's break down what this means in simple terms.

These scores tell us how well each AI model can "see" and understand images. Higher numbers mean better performance. Think of it like test scores in school - 90% is better than 70%.

Key Performance Insights

Recent models show impressive improvements in visual understanding capabilities:

• Gemini 2.5 Pro leads in OCRBench with a score of 862, showing exceptional text recognition in images
• InternVL 2.5-78B (MPO variant) achieves the highest OCRBench score at 909
• Step-1o reaches 926 on OCRBench, setting a new standard for text recognition in images
• Gemini 2.5 Pro excels at math problems with images, scoring 80.9% on MathVista
• GPT-4.5 and Llama 4 Behemoth show strong performance on college-level questions in MMMU

What These Scores Mean

Each benchmark tests different visual skills:

• OCRBench: How well models read text in images (like reading signs in photos)
• MathVista: Solving math problems presented visually (like interpreting graphs)
• MMMU: Answering college-level questions about images across many subjects
• MMBench: General visual understanding across various tasks

Models with high scores on MMBench (like Gemini 2.5 Pro at 88.3% and Qwen2.5-VL-72B at 88.0%) show strong overall visual abilities. High MMMU scores (like o1-preview at 78.2%) indicate better reasoning about complex visual information.

The Visual AI Race

We're seeing a competitive race between major AI labs:

• Google's Gemini 2.5 Pro excels in math and general visual tasks
• OpenAI's models (GPT-4o, GPT-4.5, o1-preview) show strong performance across different benchmarks
• Meta's Llama 4 Behemoth demonstrates impressive reasoning on MMMU
• Specialized models from labs like Shanghai AI Lab and StepFun are setting new records in specific areas

These improvements happen quickly. Models released just months apart show significant performance jumps. For example, newer models score 10-20% higher on MMMU than models from early 2024.

What's Missing

The table has many blank spots (shown as N/A). This means:

1. 1
   Not all models are tested on all benchmarks
2. 2
   Companies might only report their best scores
3. 3
   Some tests are newer or less commonly used

This makes direct comparisons tricky. A complete picture would require testing all models on the same benchmarks under identical conditions.

Despite these limitations, the data clearly shows rapid progress in visual AI capabilities. Models continue to improve their understanding of images, charts, and documents. This improvement will lead to more helpful AI tools for education, research, and business.

Conclusion

Evaluating multimodal LLMs requires a strategic blend of specialized benchmarks and thoughtful methodology. ChartQA, DocVQA, and newer frameworks like MMMU provide critical insights into how models comprehend visual content across diverse contexts. The substantial performance gaps identified between current models and human-level understanding highlight specific areas requiring focused development.

For product teams, these benchmarks translate directly to practical decision-making, helping identify which models excel at specific visual tasks relevant to your product requirements. Engineering teams should note the correlation between benchmark performance and real-world application success, particularly in document processing, visualization interpretation, and multi-image reasoning scenarios. By systematically applying these evaluation methodologies, you can build more reliable multimodal features while maintaining a clear understanding of their capabilities and limitations.

References for the table

1. 1
   OpenVLM Leaderboard
2. 2
   Evaluating Math Reasoning in Visual Contexts
3. 3
   A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
4. 4
   SEED-Bench-2-Plus: Benchmarking Multimodal Large Language Models with Text-Rich Visual Comprehension
5. 5
   ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering