๐ช๐ฒ ๐ป๐ฒ๐ฒ๐ฑ ๐๐ผ ๐บ๐ผ๐๐ฒ ๐ฏ๐ฒ๐๐ผ๐ป๐ฑ ๐ฐ๐ฎ๐น๐น๐ถ๐ป๐ด ๐ฒ๐๐ฒ๐ฟ๐๐๐ต๐ถ๐ป๐ด ๐ฎ๐ป โ๐๐๐ .โ โฌ๏ธ
In 2025, the AI landscape has evolved far beyond just large language models. Knowing which model to use for your specific use case โ and how โ is becoming a strategic advantage.
Letโs break down theย 8 most important model typesย and what theyโre actually built to do: โฌ๏ธ
1. ๐๐๐ โ ๐๐ฎ๐ฟ๐ด๐ฒ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น
โ Your ChatGPT-style model.
Handles text, predicts the next token, and powers 90% of GenAI hype.
๐ Use case: content, code, convos.
2. ๐๐๐ โ ๐๐ฎ๐๐ฒ๐ป๐ ๐๐ผ๐ป๐๐ถ๐๐๐ฒ๐ป๐ฐ๐ ๐ ๐ผ๐ฑ๐ฒ๐น
โ Lightweight, diffusion-style models.
Fast, quantized, and efficient โ perfect for real-time or edge deployment.
๐ Use case: image generation, optimized inference.
3. ๐๐๐ โ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐๐ฐ๐๐ถ๐ผ๐ป ๐ ๐ผ๐ฑ๐ฒ๐น
โ Where LLM meets planning.
Adds memory, task breakdown, and intent recognition.
๐ Use case: AI agents, tool use, step-by-step execution.
4. ๐ ๐ผ๐ โ ๐ ๐ถ๐
๐๐๐ฟ๐ฒ ๐ผ๐ณ ๐๐
๐ฝ๐ฒ๐ฟ๐๐
โ One model, many minds.
Routes input to the right โexpertโ model slice โ dynamic, scalable, efficient.
๐ Use case: high-performance model serving at low compute cost.
5. ๐ฉ๐๐ โ ๐ฉ๐ถ๐๐ถ๐ผ๐ป ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น
โ Multimodal beast.
Combines image + text understanding via shared embeddings.
๐ Use case: Gemini, GPT-4o, search, robotics, assistive tech.
6. ๐ฆ๐๐ โ ๐ฆ๐บ๐ฎ๐น๐น ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น
โ Tiny but mighty.
Designed for edge use, fast inference, low latency, efficient memory.
๐ Use case: on-device AI, chatbots, privacy-first GenAI.
7. ๐ ๐๐ โ ๐ ๐ฎ๐๐ธ๐ฒ๐ฑ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น
โ The OG foundation model.
Predicts masked tokens using bidirectional context.
๐ Use case: search, classification, embeddings, pretraining.
8. ๐ฆ๐๐ โ ๐ฆ๐ฒ๐ด๐บ๐ฒ๐ป๐ ๐๐ป๐๐๐ต๐ถ๐ป๐ด ๐ ๐ผ๐ฑ๐ฒ๐น
โ Vision model for pixel-level understanding.
Highlights, segments, and understands *everything* in an image.
๐ Use case: medical imaging, AR, robotics, visual agents.
Understanding these distinctions is essentialย for selecting the right model architecture for specific applications, enabling more effective, scalable, and contextually appropriate AI interactions.
While these are some of the most prominent specialized AI models, there are many more emerging across language, vision, speech, and robotics โ each optimized for specific tasks and domains.
LLM, VLM, MoE, SLM, LCMย โ GenAI
LAM, MLM, SAMย โ Not classic GenAI, butย critical building blocksย for AI agents, reasoning, and multimodal systems
๐ ๐ฒ๐
๐ฝ๐น๐ผ๐ฟ๐ฒ ๐๐ต๐ฒ๐๐ฒ ๐ฑ๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐๐ โ ๐ฎ๐ป๐ฑ ๐๐ต๐ฎ๐ ๐๐ต๐ฒ๐ ๐บ๐ฒ๐ฎ๐ป ๐ณ๐ผ๐ฟ ๐ฟ๐ฒ๐ฎ๐น-๐๐ผ๐ฟ๐น๐ฑ ๐๐๐ฒ ๐ฐ๐ฎ๐๐ฒ๐ โ ๐ถ๐ป ๐บ๐ ๐๐ฒ๐ฒ๐ธ๐น๐ ๐ป๐ฒ๐๐๐น๐ฒ๐๐๐ฒ๐ฟ. ๐ฌ๐ผ๐ ๐ฐ๐ฎ๐ป ๐๐๐ฏ๐๐ฐ๐ฟ๐ถ๐ฏ๐ฒ ๐ต๐ฒ๐ฟ๐ฒ ๐ณ๐ผ๐ฟ ๐ณ๐ฟ๐ฒ๐ฒ: https://lnkd.in/dbf74Y9E
Kudos for the graphic goes to Generative AI ! | 45 comments on LinkedIn
