I remember the first time I uploaded a photo to GPT-4V. It was a messy desk cluttered with coffee cups, sticky notes, and half-finished projects. The AI not only identified every object but noticed details I'd missed — like the specific brand of coffee and the fact that one sticky note was curling at the corner.
It felt almost magical. But then I started asking harder questions. "What's the mood of this room?" "Why might someone arrange their desk this way?" "What does this say about their work habits?" The answers became less impressive — sometimes confidently wrong, sometimes missing obvious context.
This is the current state of multimodal AI: incredibly capable at certain tasks, surprisingly limited at others, and fundamentally different from human vision in ways that matter. In this deep dive, I'm going to show you exactly how these models process images, what they're genuinely good at, where they fail spectacularly, and what this means for the future of AI.
If you're curious about comparing different multimodal models, you might also want to check out our guide on how Gemini is different from ChatGPT, which covers their vision capabilities in detail.
- Yes, but differently than humans: Modern multimodal LLMs like GPT-4V, Gemini, and Claude 3 can analyze images with impressive accuracy, but they process visual information as mathematical patterns rather than truly "seeing" in the human sense.
- Vision encoders + language models: These systems use specialized vision models (like CLIP or custom vision transformers) to convert images into numerical tokens, which are then processed alongside text by the language model.
- Strong at: Object recognition, text extraction (OCR), describing scenes, analyzing charts/graphs, identifying relationships between visual elements.
- Weak at: True spatial reasoning, understanding context beyond what's visible, recognizing subtle emotional cues, handling ambiguous or abstract imagery.
- Not consciousness: Despite impressive capabilities, these models lack visual consciousness, embodied experience, and the intuitive understanding that comes from physically existing in the world.
01 What Exactly Are Multimodal LLMs?
Let's start with the basics. A "multimodal" AI is one that can process multiple types of information — typically text and images, though some can also handle audio, video, or even sensor data.
Traditional language models like the original GPT-3 were text-only. You gave them words, they gave you words back. But starting around 2023, we saw the emergence of models that could "see" — GPT-4V (Vision), Gemini, Claude 3, and others that accept images as input alongside or instead of text.
The Evolution: From Text to Vision
The journey from text-only to multimodal wasn't instantaneous. It happened in stages:
Understanding how these models actually work requires diving into the technical architecture — but don't worry, I'll keep it accessible.
02 How Multimodal LLMs Process Images
Here's where things get interesting. When you upload an image to ChatGPT or Gemini, it doesn't "see" the image the way you do. Instead, it goes through a multi-stage process:
(CLIP/ViT)
(Transformer)
The key insight: images are converted into the same "language" (tokens) that the model uses for text, allowing unified processing.
Step 1: Vision Encoding
The image first passes through a vision encoder — typically a Vision Transformer (ViT) or a model like CLIP. This breaks the image into patches (like a grid of small squares) and converts each patch into a numerical vector called an embedding.
These embeddings capture visual features: edges, textures, colors, shapes, and increasingly complex patterns as the data moves through the network. By the end, the model has a mathematical representation of the entire image.
Step 2: Token Conversion
The visual embeddings are then converted into "visual tokens" — essentially, the image is translated into the same format the language model uses for words. This is crucial because it allows the model to process images and text using the same mechanisms.
If you want to understand more about how AI models learn these representations, our guide on how AI models get their training data explains the massive datasets used to train these vision systems.
Step 3: Multimodal Fusion
Here's where the magic happens. The visual tokens are combined with text tokens (your question or prompt) and fed into the language model. The transformer architecture processes both modalities together, allowing it to reason about the relationship between what it "sees" and what you're asking.
This is fundamentally different from older systems that would separately analyze an image and generate text. Modern multimodal LLMs process everything together in a unified way.
The model doesn't have a separate "visual understanding" module and "language understanding" module. It's all one system processing tokens — whether those tokens originated from pixels or letters is ultimately irrelevant to the transformer.
03 What Multimodal LLMs Can Actually Do
After testing dozens of multimodal models across hundreds of tasks, here's what they're genuinely good at:
Real-World Example: Medical Imaging
One of the most promising applications is in healthcare. Multimodal models can analyze X-rays, MRIs, and CT scans, identifying potential issues and providing preliminary assessments. However, they're not replacing radiologists — they're augmenting human expertise by catching details that might be missed and providing second opinions.
If you're interested in how these models are being specialized for specific domains, our article on what is fine-tuning an LLM explains how general models are adapted for specialized tasks like medical imaging.
04 Where Multimodal LLMs Struggle (A Lot)
Now for the uncomfortable truth: despite impressive capabilities, multimodal LLMs have significant limitations that often go unmentioned in marketing materials.
- No true spatial reasoning: Models struggle with tasks requiring 3D mental rotation or understanding spatial relationships beyond what's directly visible. Ask them to imagine what's behind an object, and they'll guess — often wrongly.
- Lack of embodied experience: Humans understand images through the lens of physical experience — we know what things feel like, weigh, sound like. AI has none of this. It sees patterns, not reality.
- Context blindness: Models can't access information outside the image. Show them a photo of a famous landmark from an unusual angle, and they might miss it entirely if the typical features aren't visible.
- Subtle emotional cues: While they can detect basic emotions, nuanced expressions, sarcasm in visual memes, or complex social dynamics in group photos often confuse them.
- Ambiguity handling: When an image could be interpreted multiple ways, models tend to confidently pick one interpretation rather than acknowledging uncertainty — a form of hallucination.
- Abstract & conceptual images: Metaphors, symbolism, abstract art, and conceptual photography often baffle models that excel at concrete object recognition.
- Temporal reasoning: Understanding sequences, cause-and-effect in still images, or predicting what happens next is extremely challenging.
The Hallucination Problem
Just like text-only LLMs, multimodal models hallucinate — they confidently describe things that aren't in the image or miss obvious details. This is particularly problematic in high-stakes applications like medical diagnosis or security screening.
To understand why this happens, check out our deep dive on why LLMs hallucinate facts — the same principles apply to visual information.
Never rely solely on multimodal AI for critical decisions. Always have human oversight, especially in healthcare, legal, security, or safety-critical applications. These tools are assistants, not replacements for human judgment.
05 Comparing Leading Multimodal Models
Not all multimodal LLMs are created equal. Here's how the major players stack up:
| Model | Image Understanding | OCR Quality | Chart Analysis | Speed | Best For |
|---|---|---|---|---|---|
| GPT-4V / GPT-4o | Excellent | Excellent | Excellent | Moderate | General-purpose, detailed analysis |
| Gemini 1.5 Pro | Excellent | Excellent | Excellent | Fast | Native multimodal, large contexts |
| Claude 3 Opus | Excellent | Very Good | Good | Moderate | Nuanced analysis, reasoning |
| Claude 3.5 Sonnet | Very Good | Very Good | Good | Very Fast | Speed + quality balance |
| Llama 3.2 Vision | Good | Good | Moderate | Fast | Open-source, self-hosted |
If you're interested in exploring open-source alternatives, our guide on the best open source LLMs in 2026 covers vision-capable models you can run locally.
06 Real-World Applications Transforming Industries
Multimodal AI isn't just a cool demo — it's actively transforming how work gets done across industries:
🏥 Healthcare
Radiologists use AI to flag potential issues in scans, dermatologists get second opinions on skin lesions, and pathologists analyze tissue samples faster. The key is augmentation, not replacement.
🏭 Manufacturing
Quality control systems detect defects in products, assembly line monitoring identifies issues in real-time, and maintenance teams use visual inspection AI to spot equipment problems before they cause failures.
📚 Education
Students upload photos of homework problems and get step-by-step explanations, teachers automatically grade visual assignments, and accessibility tools describe images for visually impaired students.
🛒 Retail & E-commerce
Visual search lets customers find products by uploading photos, inventory systems automatically count and categorize stock, and quality assurance teams inspect products at scale.
🔍 Research & Development
Scientists analyze microscopy images, astronomers process telescope data, and researchers extract data from historical documents and scientific diagrams automatically.
Retrieval-augmented generation (RAG) systems are now incorporating visual information, allowing AI to search through image databases and retrieve relevant visuals alongside text. Learn more about this in our guide on what is retrieval-augmented generation (RAG).
07 The Future: What's Coming Next
The field is moving fast. Here's what I'm watching:
1. Video Understanding
Moving from static images to video — understanding motion, temporal relationships, and dynamic scenes. This is exponentially harder but incredibly powerful.
2. 3D Scene Understanding
Models that can infer 3D structure from 2D images, understand depth, perspective, and spatial relationships in ways that approach human capability.
3. Embodied AI
Robots and agents that learn vision through physical interaction with the world, gaining the embodied experience that current models lack.
4. Multisensory Integration
Combining vision with audio, touch, and other sensors to create richer, more human-like understanding of the environment.
5. Causal Reasoning
Moving beyond pattern recognition to understanding cause-and-effect in visual scenes — why things are arranged as they are, what actions led to current states.
08 Practical Guide: Getting Started with Multimodal AI
Want to start using multimodal AI? Here's how:
For Beginners
- Start with ChatGPT or Gemini: Both offer free tiers with vision capabilities. Upload images and experiment with different types of questions.
- Be specific: Instead of "What's in this image?" try "Describe the objects in this image and their spatial relationships."
- Verify important information: Always double-check AI descriptions against the actual image, especially for critical tasks.
- Learn prompt engineering: The way you ask questions dramatically affects response quality.
For Developers
- APIs: OpenAI, Google, and Anthropic all offer APIs for integrating vision capabilities into applications.
- Open-source options: Models like LLaVA, BLIP-2, and open-source vision transformers can be self-hosted.
- Fine-tuning: For specialized applications, consider fine-tuning existing models on your domain-specific data.
- Hybrid approaches: Combine traditional computer vision (for tasks like object detection) with LLMs (for reasoning and description) for best results.
When working with multimodal AI, chain your queries. First ask for a general description, then drill down into specifics. This often yields better results than asking one complex question.
09 Conclusion: The Bottom Line
So, can LLMs understand images? The honest answer is: yes, but not like humans do.
Modern multimodal models are incredibly powerful tools that can analyze, describe, and reason about visual information in ways that seemed impossible just a few years ago. They're transforming industries, augmenting human capabilities, and opening up new possibilities we're only beginning to explore.
But they're not conscious observers. They don't truly "see" or "understand" in the way we do. They're sophisticated pattern matchers, converting pixels to tokens and finding statistical relationships learned from massive datasets.
This matters because it means they excel at some tasks (object recognition, OCR, chart analysis) while struggling with others (spatial reasoning, abstract interpretation, understanding context beyond the frame). Knowing these strengths and limitations is crucial for using these tools effectively and responsibly.
The technology is advancing rapidly. What seems impossible today might be routine tomorrow. But for now, the best approach is to use multimodal AI as a powerful assistant — one that can process visual information at superhuman speed and scale, but still requires human oversight, judgment, and understanding of its limitations.
Whether you're a developer building the next generation of AI applications, a professional looking to augment your workflow, or just someone curious about how these systems work, understanding both the capabilities and limitations of multimodal AI is essential. The future isn't about AI replacing human vision — it's about AI and humans working together, each bringing their unique strengths to bear on complex visual problems.
10 Frequently Asked Questions
Can LLMs understand images?
How do multimodal LLMs process images?
What can multimodal AI do with images?
Which LLM is best for image understanding?
Do LLMs truly understand images like humans?
Can multimodal AI replace human visual analysis?
How accurate is multimodal AI at image recognition?
Written by Varun Lalwani
Varun explores the intersection of computer vision and language models, testing multimodal AI systems across real-world applications. Published June 2026. Questions? Contact our team or learn about our mission. Stay updated via our RSS feed.