Before we read, before we write, we see. The human brain devotes more processing power to vision than to any other sense. We navigate the world through sight first, and a single glance tells us more than paragraphs of description ever could.

For decades, this kind of visual understanding eluded machines. Computer vision could detect edges and match patterns, but couldn’t truly see. Now, vision-capable language models (VLMs) can interpret images, form spatial relations, and reason about what they’re looking at. They don’t just parse pixels; they understand scenes.

Here, we will walk through how these models process visual data, combine it with language, and produce outputs that we can use.

Understanding Visual Perception in AI

Text models learned to write. Vision models are learning to perceive. When machines learn to see, not just parse pixels, but understand what they’re looking at, they move closer to how we experience the world and become genuinely useful tools for solving real-world problems.

To "see," a model must first break the world into parts it can process. Just like an LLM can’t understand entire sentences and needs them broken down into tokens, VLMs can’t understand a whole image. However, we also don’t want to feed it an entire image pixel by pixel.

The first step is then to divide the image into a grid, typically 16x16 pixels, of patches. It is these patches that the model can compare and reason about. The next step is to flatten the patches into a one-dimensional array:

These are then passed through a linear projection layer to become a patch embedding, a dense numerical vector representing the content of that small piece of the image. Instead of analyzing every pixel in isolation, the model learns from the relationships between patches: how edges align, how colors cluster, and how forms repeat.

This structure, learning from relationships rather than raw pixels, is what gives vision models their power. Through self-attention, the model identifies which patches belong together and begins to reason about both spatial structure ("where things are") and semantic meaning ("what they are").

Spatial vs Semantic Features

During patch processing, the VLM moves from recognizing where things are to understanding what they are. Early layers focus on spatial features: oriented edges, corner detectors, texture patterns, and geometric layouts. These low-level features capture the structural skeleton of the image, preserving positional relationships between objects.

Later layers build on this foundation to extract semantic features. Rather than detecting edges or textures, these layers recognize higher-level concepts, such as "cat," "pillar," and "floor." They encode object categories, scene types, and relationships between elements. This is where the model learns that certain patch combinations represent a sleeping animal, not just a black and white blob.

The hierarchical nature of this processing matters. Spatial features alone can locate objects, but can’t identify them. A model might detect four legs and a tail without knowing whether it’s looking at a cat or a dog. Semantic features provide identity but lose precise positioning. The combination allows the model to both detect shapes and understand the scene: Milan is a cat (semantic), the pillar is behind him (spatial), and he’s resting against it (relational understanding from both).

This separation also determines what tasks the model can handle. Object detection relies heavily on spatial features to draw bounding boxes. Image classification depends more on semantic features to categorize the scene. Image captioning requires both maintaining spatial relationships while identifying objects and their interactions.

How Multimodal Integration Works

Seeing isn’t understanding. Real perception means connecting what’s seen with what’s said. To become useful, visual understanding must connect to language.

This is multimodality: a model’s ability to process and relate information across different types of data, such as text, images, audio, or video. For VLMs, the challenge is aligning visual and textual information so that when the model sees a photo of a cat and reads the word "cat," it understands they refer to the same concept.

VLMs achieve this through cross-modal context alignment, which involves projecting visual embeddings and text embeddings into a joint latent space via learned projection layers. In this space, visual feature vectors extracted from patches showing fur, whiskers, and pointed ears achieve high cosine similarity with the token embedding for "cat."

Similarly, visual patches showing a mane, hooves, and tail map near the token "horse," clustering separately but using the same alignment mechanism.

This alignment occurs during training through techniques such as CLIP (Contrastive Language-Image Pretraining). The model processes pairs of images and their associated text (captions, questions, descriptors), learning which visual patterns correspond to which words and concepts. The goal is to pull matching image-text pairs closer together in the embedding space while pushing unrelated pairs apart.

Why Alignment is Challenging

Even with sophisticated training methods, alignment remains imperfect. Several issues get in the way:

Language ambiguity: The phrase "man by the bank" could mean a riverbank or a financial institution. The model can get confused. 1.

Different information densities: Images hold thousands of visual details, while captions summarize them in a few words. Images speak a thousand words, and that can be applied here. 1.

Spatial grounding: Understanding where something is in the image (e.g., "the gray cat on the floor") requires spatial awareness.

Misalignment leads to hallucinations (describing objects that are not present or incorrectly) or missed context (failing to connect related elements).

Working with Vision APIs

Understanding how VLMs process and align visual and textual information explains what happens inside the model. But to actually use these capabilities, you interact with them through APIs that abstract away the complexity. These APIs expose the model’s multimodal reasoning while handling the heavy lifting of image encoding, tokenization, and inference.

Working with vision-capable APIs follows the same principles as working with a text model, with a few extra considerations around image pre-processing and structured output.

Use standard formats such as JPEG, PNG, or WebP.

Ensure your images stay within the API’s payload size limits (for example, OpenAI’s models currently allow up to 50 MB per request)

Encoding an image in Base64 is required as APIs usually work with text-only, not binary data.

Here’s an example using gpt-4o in Python.

import  base64
from  openai  import  OpenAI

client  =  OpenAI(api_key="YOUR_API_KEY")

#  1.  Load  and  encode  image
with  open("input_image.jpg",  "rb")  as  f:
image_bytes  =  f.read()
image_b64  =  base64.b64encode(image_bytes).decode("utf-8")

#  2.  Compose  messages  for  multimodal  chat
messages  =  [
{"role":  "system",  "content":  "You are an image-understanding assistant. Reply in JSON with keys: objects, confidence, bounding_boxes."},
{"role":  "user",  "content":  {"type":  "image_data",  "data":  f"data:image/jpeg;base64,{image_b64}"}},
{"role":  "user",  "content":  "List all the objects you see and their approximate locations."}
]

#  3.  Submit  the  request
response =  client.chat.completions.create(
model="gpt-4o",
messages=messages,
temperature=0.0,
max_tokens=300
)

#  4.  Parse  and  print
output  =  response.choices[0].message.content
print("Model response:",  output)

Some best practices for working with vision models:

Resolution: Downsample images above 2048px on the longest side. Higher resolution doesn’t improve reasoning and increases token usage.

Format: Use JPEG for photographs, PNG for diagrams or screenshots with text. Both compress well while preserving necessary detail.

Quality: Ensure sufficient clarity for human interpretation. Excessive compression artifacts degrade model performance.

Encoding: Always use base64 encoding as shown in the example above.

Prompting: Distinguish between descriptive tasks ("caption this image") and inferential tasks ("what might this person be doing?"). VLMs perform differently on each.

For applications that need to parse model responses programmatically, structured output ensures consistent formatting. Schema-guided prompting provides an explicit JSON schema in your prompt and constrains the model’s output format.

Return JSON with this exact structure:

{
"objects":  [],
"relationships":  [],
"caption":  ""
}

Do not include any text before or after the JSON.

Set temperature below 0.2 to reduce variance in field names and structure. Lower temperature makes the model more deterministic, following your schema more precisely.

If your API supports it, you can use function calling, which allows you to define functions that return typed objects. The model generates structured calls that your code can parse natively, eliminating the need for manual JSON parsing.

Error Handling: Hallucinations and Model Evaluation

Hallucinations stem from three main sources.

Cross-modal misalignment occurs when training data bias causes the model to infer objects from textual associations rather than visual evidence (e.g., inferring a pillar because cats and pillars often co-occur in training data). 1.

Visual ambiguities, such as occlusions, low contrast, or unusual camera angles, produce uncertain embeddings. 1.

During fusion, attention layers may overweight textual priors from the prompt instead of the actual image content, producing confident but visually incorrect responses.

Standardized benchmarks measure both recognition accuracy and reasoning capability across task categories:

Visual QA and reasoning: MMMU (Massive Multi-discipline Multimodal Understanding and Reasoning), MMBench

Specialized domains: MathVista (mathematical reasoning), ChartQA (chart interpretation), DocVQA (document understanding)

Video understanding: Video-MME

Run evaluations using tools like VLMEvalKit to compare model performance on your specific use case before deployment.

From Patches to APIs

Vision models work by learning relationships between visual patterns and language concepts. They transform image patches into embeddings, align them with text through contrastive learning, and reason about both spatial structure and semantic meaning.

Modern APIs make this accessible. Understanding the underlying mechanics helps you debug failures, optimize prompts, and select the most suitable model. Vision capabilities are production-ready. The challenge is knowing when to use them and how to validate their outputs for your specific use case.

Frequently Asked Questions

1. How do vision models encode and interpret pixel data internally?

VLMs don’t "see" pixels directly; they tokenize them. Same as words. A typical encoder (like a ViT, or CLIP) divides the image into small, fixed-size patches (e.g., 16x16 pixels). Each patch is flattened and passed through alinear projection layer, converting it into a vector embedding—a numerical summary of the local visual pattern.

2. What are the most common reasons for hallucination or false object detection?

Hallucinations usually originate from cross-modal misalignment or contextual overgeneralization, which can occur in some forms of training data bias. Visual ambiguity, over-regularization, and compression artifacts can lead to false object detection.

3. How can developers enforce consistent JSON output from visual APIs?

• Most vision APIs (OpenAI, Google, Anthropic), with their respective models, are pretty good at structured constraints so that you can enforce structured outputs via prompt schema and post-validation:

Return JSON with this exact structure:

{
"objects":  [],
"relationships":  [],
"caption":  ""
}

Use function calling or response_format parameters (where available). These enable the model to generate native, structured objects.

Always parse responses and re-ask for correction when schema errors occur ("Your JSON was invalid; please reformat according to ...").

Lowering the temperature (<0.2) reduces the model’s creative variance in field names and JSON structure format.

4. What differences exist between GPT‑4o and Gemini in how they process visual context?

While both are multimodal LLMs, their fusion architectures differ:

GPT-4o ("omni") uses unified early fusion. The input image is encoded into visual tokens that are processed through the same transformer as text tokens. This enables proper joint attention, where the model can simultaneously "look" at an image region while reading a sentence.

Gemini (1.5 Pro) follows hybrid or late fusion. Visual encoders (based on ViT/Perceiver) produce embeddings that are later injected into the text model.

5. How do you measure visual reasoning accuracy effectively in tests?

Evaluation revolves around objective metrics and human-interpretable checks:

For captioning and description tasks: use BLEU, METEOR, ROUGE, or CIDEr.

For grounding / reasoning: Visual Question Answering (VQA-v2, GQA): test factual consistency with visual input; Visual entailment datasets (SNLI-VE, ScienceQA): measure logical reasoning grounded in images; RefCOCO, COCO-Panoptic: for object localization accuracy.

Human or synthetic audits: have the model explain why it made a claim and cross-check for visual justification.

Consistency testing: perturb the same image (e.g., crop, rotate, change caption wording) and check the stability of reasoning — large variance signals weak visual grounding.