Vision & Multimodal AI
Build applications that understand and generate images, documents, and audio. This path covers vision capabilities across 5 providers, document processing, image generation, multimodal embeddings, and audio — the complete multimodal toolkit.
The key cross-provider insight: each provider has different vision strengths. OpenAI offers the broadest multimodal coverage (vision + generation + audio), Anthropic excels at document understanding, Cohere provides multimodal embeddings for search, and Mistral/Together AI offer cost-effective open-source alternatives. Choosing the right provider per modality can dramatically improve both quality and cost.
Steps
- Vision LLMs
together-ai
beginner
Learn how to use the vision models supported by Together AI.
Start with a high-level overview of vision-capable LLMs and what they can do. Together AI's guide covers the landscape of open-source and proprietary vision models, helping you understand what's possible before diving into specific providers.
- Images and vision
openai
intermediate
Learn how to understand or generate images with the OpenAI API.
OpenAI's vision capabilities let GPT-4o analyze images, charts, diagrams, and screenshots. Focus on the image encoding options (URL vs base64), detail levels (low/high), and token costs — high-detail images can consume thousands of tokens.
- Vision
anthropic-platform
intermediate
Send images to Claude for analysis, OCR, diagram interpretation, and multimodal reasoning.
Claude's vision is optimized for document understanding, chart analysis, and multi-image comparison. Compare with OpenAI: Claude excels at detailed document analysis while GPT-4o handles a broader range of visual tasks. Pay attention to the supported formats and size limits.
- Vision
mistral
intermediate
Multimodal AI models analyze images and text for insights, supporting use cases like OCR, chart understanding, and receipt transcription
Mistral's Pixtral model brings vision capabilities to the open-weight ecosystem. Compare capabilities and pricing with GPT-4o and Claude — Mistral offers a cost-effective alternative for vision tasks, especially through Together AI hosting.
- Using Cohere's Models to Work with Image Inputs
cohere
intermediate
This page describes how a Cohere large language model works with image inputs. It covers passing images with the API, limitations, and best practices.
Cohere's image input support is designed for enterprise search and classification workflows. Compare this specialized focus with the general-purpose vision of OpenAI and Anthropic — different tools for different use cases.
- Pdf Support
anthropic-platform
intermediate
PDF processing is a critical production use case for vision models. Claude's native PDF support handles complex layouts, tables, and multi-page documents. This is often more reliable than OCR-based approaches for structured document extraction.
- Image generation
openai
intermediate
Learn how to generate or edit images with the OpenAI API and image generation models.
Image generation (DALL-E and GPT-4o native) enables models to create images, not just understand them. The tool-use pattern lets models decide when to generate images during conversations — a building block for multimodal agents.
- Unlocking the Power of Multimodal Embeddings
cohere
intermediate
Multimodal embeddings convert text and images into embeddings for search and classification (API v2).
Multimodal embeddings let you search across text and images in the same vector space — essential for building visual search, cross-modal retrieval, and multimodal RAG systems. This is a capability unique to Cohere's embedding models.
- Audio and speech
openai
intermediate
Learn how to work with audio and speech in the OpenAI API.
Audio completes the multimodal picture: speech-to-text (Whisper), text-to-speech, and native audio understanding in GPT-4o. Understanding audio capabilities alongside vision lets you build truly multimodal applications.