Vision & Image Understanding

Vision Language Models (VLMs), image analysis, OCR, and visual reasoning.

Capability Matrix

Provider	Model	OCR	Scene Understanding	Reasoning	API
OpenAI	GPT-4V	Excellent	Excellent	Excellent	Yes
Anthropic	Claude 3.5	Excellent	Excellent	Excellent	Yes
Google	Gemini 2.0	Very Good	Very Good	Very Good	Yes
Meta	Llama 3.2 Vision	Good	Good	Good	Open

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Use Cases

Application	Capability Needed	Best For
Document processing	OCR + reasoning	Invoices, receipts, forms
Visual QA	Scene understanding	Accessibility, search
Content moderation	Object detection	Safety, compliance
Product recognition	Visual search	E-commerce, inventory
Medical imaging	Specialized analysis	Healthcare

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Tool Selection

Need	1st Choice	2nd Choice	Why
General vision + reasoning	Claude	GPT-4V	Reasoning + OCR
Multimodal research	Gemini 2.0	—	1M context + Google index
Open source	Llama 3.2 Vision	—	Self-hostable

Stack

Layer	Tool
Model	Claude, GPT-4V, Gemini 2.0
Framework	Claude Code — orchestrate vision calls, chain image→text→action
MCP	Claude in Chrome — capture screenshots, verify rendered UIs
CLI	—

For image generation (text→image), see the modality matrix cells in the Image Output column. For the full MCP adoption radar, see MCP Tools.

Context

• AI Modalities — All capability types
• AI Tools — Framework, MCP, and CLI stack for vision pipelines
• LLM Models — Provider comparison

Questions

When vision models can read, reason, and describe any image — what human capability does that replace, and what does it amplify?

• Which vision task in your workflow still requires a human eye that no model handles reliably?
• At what accuracy threshold does automated document processing become trustworthy enough to remove human review?
• How do you evaluate vision model quality when "correct" is subjective — scene understanding vs OCR vs reasoning?