Topic
Multimodal AI Models
4 posts on multimodal ai models — part of glossary on the n4n AI blog.
What is multimodal AI and how it processes multiple inputs
A technical definition of multimodal AI covering architecture patterns, cross-modal attention, training objectives, and practical deployment considerations for engineers.
How multimodal models combine text, image, and audio
A practical guide to how multimodal models process and combine text, image, and audio inputs — covering architectures, tokenization strategies, and integration patterns for production systems.
How GPT-5 handles text, image, and audio in one model
A practical guide to GPT-5 multimodal capabilities covering text, image, and audio handling with code examples, routing strategies, and common pitfalls for production systems.
How Gemini 3 processes video input natively
A practical guide to Gemini 3's native video processing — tokenization, context limits, sampling strategies, and production patterns for engineers building multimodal applications.
More topics in glossary
- Structured Outputs & JSON Mode19
- AI Agents Fundamentals12
- Hallucination in LLMs11
- Sampling Parameters: Top-p, Top-k & Penalties11
- Context Window & Context Length10
- Fine-Tuning Fundamentals9
- Foundation Models: Base vs Instruct vs Chat9
- Model Families & Naming Conventions: GPT-5, Claude, Gemini 3, Llama 4, Mistral, DeepSeek, Qwen, Grok9
- Grounding & Fact-Checking in AI8
- LLM Benchmarks: MMLU, HumanEval, SWE-bench & GPQA8
- Max Tokens, Stop Sequences & Output Truncation8
- Quantization Formats: GGUF, GPTQ, AWQ & INT4/INT88