Modèle Updated 2026-04
Vision-Language Model (VLM)
Vision-Language Model
Definition
A Vision-Language Model (VLM) is an AI model capable of simultaneously understanding and reasoning about images and text, unifying visual perception and language understanding.
See also in the glossary
M
Multimodal
A multimodal model processes and generates multiple data types: text, images, audio and video.
L
LLM (Large Language Model)
An LLM is an AI model trained on billions of texts, capable of understanding and generating human language.
T
Transformer
The Transformer is the neural network architecture powering all modern LLMs, invented by Google in 2017.
D
Deep Learning
Deep Learning is a subset of Machine Learning using multi-layered neural networks to learn complex representations from raw data.
A
Attention Mechanism
The attention mechanism allows a model to weigh the importance of each word relative to all others, capturing global context.
F
Foundation Model
A foundation model is a large AI model pre-trained on massive data, adaptable to multiple tasks.
Tools that use vision-language model
C
ChatGPT
The world's most used conversational AI assistant
4.6/5
C
Claude
The AI that understands nuance, by Anthropic
4.7/5
G
Gemini
Google's AI assistant with 1M token context
4.5/5
M
Meta AI (Llama)
Meta's AI assistant powered by Llama, the leading open source LLM
4.3/5
Q
Qwen
Alibaba's LLM excelling at code and multilingual
4.4/5
Frequently Asked Questions
What's the difference between a VLM and a multimodal model?
A VLM is a specific type of multimodal model focused on vision and language. A multimodal model can include other modalities like audio, video or 3D. In practice, VLMs are the most mature and widely deployed category of multimodal models in 2026.
What is the best VLM in 2026?
Google's Gemini and OpenAI's GPT-4o compete for leadership on visual benchmarks. Anthropic's Claude excels at analyzing complex documents and charts. The choice depends on the use case: OCR, scene understanding, visual reasoning, or diagram analysis.
