peterindia.netVision Language Models
Models that read images, charts, documents and screens alongside text, from proprietary multimodal assistants to open-weight and on-device vision-language models.
OpenAI's API guide to sending images to GPT models for description, question answering and data extraction.
Best for: Adding image understanding through the OpenAI API.
Visit OpenAI image and vision input →Anthropic's guide to giving Claude images and PDFs to analyse, transcribe and reason about alongside text.
Best for: Documents, charts and screenshots.
Visit Claude vision →Gemini API documentation for captioning, visual question answering, object detection and segmentation on images.
Best for: Multimodal prompts in the Gemini API.
Visit Google Gemini image understanding →Alibaba's Qwen vision-language model series, released as open weights with image, video and document understanding.
Best for: Strong open multimodal models in many sizes.
Visit Qwen3-VL →Open-source multimodal model family from OpenGVLab, with models in a range of sizes for images, documents and video.
Best for: Research and self-hosted multimodal assistants.
Visit InternVL →Large Language and Vision Assistant, an influential open project that connects a vision encoder to a language model through visual instruction tuning.
Best for: Understanding how VLMs are built.
Visit LLaVA →Open multimodal models from the Allen Institute for AI, released with their training data and able to point at things in images.
Best for: Fully open models and data.
Visit Molmo (Ai2) →DeepSeek's open mixture-of-experts vision-language models for visual question answering, OCR and grounding.
Best for: Efficient open multimodal inference.
Visit DeepSeek-VL2 →Meta's Llama developer site; recent Llama models accept images as well as text and are available with open weights.
Best for: Open models from a major lab with broad tooling.
Visit Meta Llama →Google's open vision-language model built on Gemma, designed to be fine-tuned for captioning, detection and segmentation.
Best for: Fine-tuning for a specific vision task.
Visit PaliGemma →Hugging Face's small vision-language models designed to run with a low memory footprint.
Best for: Running VLMs on modest hardware.
Visit SmolVLM →Small, efficient vision-language model that runs locally and in the cloud for captioning, detection and visual questions.
Best for: Lightweight local image understanding.
Visit Moondream →Microsoft's compact open model that handles text, images and speech in a single model.
Best for: Small multimodal model for apps.
Visit Phi-4-multimodal →Microsoft's compact vision foundation model that handles captioning, detection and segmentation through text prompts.
Best for: Prompt-driven vision tasks at low cost.
Visit Florence-2 →Benchmark of college-level multimodal questions across many subjects for evaluating vision-language models.
Best for: Comparing models on expert-level reasoning over images.
Visit MMMU →OpenCompass leaderboard that ranks open and proprietary vision-language models across multimodal benchmarks.
Best for: Shortlisting models by benchmark.
Visit OpenVLM Leaderboard →Introductory article on how VLMs work, how to pick one, and how to fine-tune and use them.
Best for: Learning the fundamentals.
Visit Hugging Face: Vision Language Models Explained →Open-source implementation of CLIP-style contrastive image-text training, the idea behind many vision encoders.
Best for: Image-text embeddings and search.
Visit OpenCLIP →Match your requirement to a starting point, then compare vendors.
| Understand screenshots, charts and PDFs | A proprietary multimodal model through its API |
|---|---|
| Keep images on your own infrastructure | Open-weight VLMs such as Qwen3-VL, InternVL or Llama |
| Run on a laptop, phone or edge device | Compact models such as SmolVLM, Moondream or Phi-4-multimodal |
| Adapt a model to your own images | Fine-tune an open model such as PaliGemma |
| Image search by meaning | CLIP-style embeddings such as OpenCLIP |
| Compare models | MMMU and the OpenVLM Leaderboard, then test on your own data |
Neighbouring directories and hubs.
Quick answers.
A VLM is an AI model that accepts images (and often video) together with text and produces text. It usually pairs a vision encoder with a language model so it can describe, question and reason about visual input.
A classifier outputs a label from a fixed set. A VLM answers open-ended questions in language, so one model can caption, read text, compare images or explain a chart.
Many handle printed text well, but accuracy varies with layout, resolution and language. For high-volume document work, compare them with dedicated OCR models.
Yes. They can describe objects that are not present or misread small details. Verify important outputs and test on your own images.
Start with a proprietary model for a quick prototype, then test open or compact models if cost, privacy or latency matter. Use benchmarks only to shortlist.
Descriptions are summaries of each vendor's own positioning, checked against its website on 4 October 2026. Product names and offerings change often, so confirm details on the vendor site before deciding.