Large Model Multimodal

Multimodal large models allow AI to process multiple forms of information simultaneously, such as text, images, audio, and video, and are one of the most core development directions for large models today.

This article explains the concept, development process, internal principles, and typical capabilities of multimodality from scratch, and provides at the endBuild Your First Multimodal Application in 5 Minutesa runnable example.

1. What is Multimodal?

Ordinary large models usually can only process one type of information—textIf you give a traditional AI an image and ask it, "How many cats are in the image?", the model cannot do it without multimodal support.

But the real world is not composed of text; rather, it is made up of multiple types of sensory information:

Therefore,MultimodalThis concept came into being.

To understand it in one sentence:

Multimodal = one model that simultaneously understands and generates multiple forms of information.

Let's look at two typical examples:

• 1. Input an image plus a question:"Is the food in this picture high in calories?"The model answers:"This is fried chicken and french fries, estimated at about 1,200 calories."

• 2. Input voice:"Help me summarize this meeting"The model outputs a text summary with action suggestions.

This ability to describe images and summarize audio is the most direct manifestation of multimodality.

2. What is a Modal?

Modality isthe representation form of information.。

Different modalities correspond to different inputs and outputs. The table below lists common modalities and their typical applications:

The more modalities a model supports, the closer its capabilities are to humans, which is why all leading labs are upgrading text models to multimodal models.

3. Development Process of Multimodal Large Models

From processing only text to seeing, hearing, and outputting video, multimodal large models have gone through three clear stages.

Phase 1: Language Models (LLM)

In this stage, models take text as both input and output. Representative products include the OpenAI GPT series, Anthropic Claude, and Google Gemini.

They can write code, write articles, translate, and answer questions. They are highly capable, but have a fundamental limitation:They cannot see.。

Phase 2: Vision Language Models (VLM)

VLM adds the ability to 'see images' on top of an LLM. A typical use case is uploading a screenshot of a webpage and asking 'why is the layout wrong?' The model replies 'CSS conflict'.

Models at this stage begin to possess capabilities such as image understanding, OCR, and chart understanding.

Phase 3: Native Multimodal Models

Native multimodal models no longer treat 'image' and 'text' as two concatenated modules. Instead, from the very start of training, theyprocess images, audio, video, and text in a unified way.。

They possess complete capabilities: seeing, hearing, speaking, reasoning, and acting. This is also the mainstream direction in the industry today.

4. How Do Multimodal Models Work Internally?

The internal workings of multimodal models can be summarized in one sentence:Convert all data into a unified vector space.。

For example:

The three expressions "one只cat", "cat", and cat.png, although from different modalities, will land in nearby regions in the vector space after encoding.

The overall process is shown in the figure below:

The figure above shows the complete pipeline from input to output; let's break down each step below.

Step 1: Encoding

Different modalities must first be converted into numbers before the model can process them.

Step 2: Unified Semantic Space

The key step after encoding is to make "cat", "cat", and cat.png in the vector spaceclose to each other。

This is the essence of "cross-modal understanding": projecting the features of different modalities into the same space.

Step 3: Generating Output

After reasoning in the unified space, the model "restores" the results into different modalities on demand.

The output can be any one of: text, image, audio, and video.

For example: upload an image → describe it → then generate an introductory video; this is a complete multimodal generation chain.

5. Why Can Transformer Unify Multimodality?

There is only one core reason:Attention mechanism。

Transformer's core formula is Attention(Q, K, V), which means "the model decides for itself what to focus on."

For example: when a user asks "Who is playing basketball in the image?", the model will:

1. First look at the image and identify all the people in it
2. Identify the basketball in the image
3. Determine the relationship between "who" and "basketball"
4. Combine the context to output the answer

This ability to "autonomously decide where to focus" makes Transformer naturally suited for handling multimodal input:

This is also why Attention has become a "standard feature" of almost all modern large models — it was inherently designed for cross-modality.

6. Typical Capabilities of Multimodal Models

Multimodal models have five typical capabilities; the figure below shows the relationships among them.

1. Image Understanding

Input a screenshot, and the model outputs page issues, UI analysis, or OCR results.

The most common scenario for programmers is: screenshot the error, let AI locate the problem.

2. Image Generation

Enter a text description like "Generate a futuristic city", and the model returns an AI image.

Typical applications: posters, covers, game assets, product sketches.

3. Speech Interaction

Speech as input, the model responds in real time, with capabilities covering ASR (speech-to-text), TTS (text-to-speech), and conversation.

4. Video Understanding

Upload an instructional video, and the model outputs a summary, timeline, and answerable key segments.

5. Agent

Enter "Help me make a PPT", and the model starts executing a complete action chain: search materials → generate outline → create slides → revise → export.

Agent is the ultimate form combining "see, hear, speak" with "hands-on execution".

7. Five Terms Beginners Must Understand

When reading multimodal-related documentation, the following terms will appear repeatedly; memorize them first:

These five terms run through all large model documentation; build intuition first, then revisit details when needed.

8. Hands-on Experience: Build Your First Multimodal Application in 5 Minutes

Goal of this section: upload an image, AI outputs a text description.

The entire process requires no deep learning background; just knowing Python is enough.

8.1 Prepare the Project

First create a project multimodal-demo, and use the openai library to test:

Inmultimodal-demoUpload an image in the directorycat-cartoon.webp, the image is as follows (you can right-click to save it for testing):

8.2 Write the Calling Code

This example usesArk's Coding Plan, and their doubao-seed-2.0-pro model supports multimodal processing.

Save the following code as test.py：

Run Pyhton, the output is as follows:

在发送请求至火山引擎方舟平台...

========== 识别结果 ==========
这是一张**Q版萌系卡通风格的数字角色插画**，整体背景为纯净的纯白色，没有任何环境元素，所有视觉重心都集中在中心的小猫主体上，是典型的独立角色立绘/吉祥物设计。
---
### 一、画面主体详细描述
主体是一只端正坐姿的卡通幼橘猫，是经过萌化设计的家养橘白虎斑猫形象，整体气质乖巧天真、亲人友好：
1.  **比例与姿态**：采用萌系设计特有的大头短身比例，头部占整体高度的60%左右，
...

Open the browser console, and you'll see the model's textual description of the image. The whole pipeline is:

User uploads an image → Browser passes the image URL to the OpenAI SDK → Multimodal model understands the image → Returns a textual description → Prints to console.

8.3 Common Extensions

After completing the minimal runnable code above, you can continue to extend:

9. Learning Path (Recommended)

If you're new to large language models, the diagram below is a proven 6-week learning path.

10. What Will Happen in the Future?

Looking back on the evolution of software forms, a clear trend can be seen:

In the past: software = functionality.

Now: software = model.

Future: Software = Multimodal Intelligent Agents.

This means that in the future, users will only need to say one sentence, such as "make a website," and the model will automatically complete the entire process of design, coding, deployment, and operations.

Multimodality is not as simple as "adding an image-viewing function to the model."

What it really changes is:

Let computers begin to directly understand the real world.

This is exactly the most important paradigm shift in the current AI industry, and also the direction all developers should invest in.

Frequently Asked Questions

Which is better: multimodal models or regular LLMs?

It depends on the scenario: for plain-text writing, code generation, and translation, a regular LLM is more cost-effective; when you need to view images, listen to audio, or understand video, a multimodal model is the only choice.

Can multimodal models run locally?

Yes, but the cost is relatively high. Small-parameter VLMs (such as Qwen2-VL-7B) can run on consumer-grade GPUs with 16GB of VRAM; larger models usually require cloud GPUs or API calls.

Must the output of a multimodal model be text?

Not necessarily. Native multimodal models can output text, images, audio, and even video; pure VLMs usually output only text.

How to choose a multimodal API?

If you only need image understanding: OpenAI gpt-4o, Gemini 2.5 Flash, and Qwen2-VL are all cost-effective choices; if you need "see + listen + real-time voice", it is recommended to directly use the OpenAI Realtime API or Gemini Live.