Large Language Model (LLM)
Also known as: LLM.
In plain English
LLMs work by predicting one token (a chunk of a word, often 3-4 characters) at a time, conditioned on every token that came before. Training happens in two main phases: pre-training on internet-scale text, and post-training (instruction tuning + RLHF/RLAIF) to make the model follow human instructions. Modern LLMs in 2026 have parameter counts ranging from billions (small/cheap models like DeepSeek V3) to hundreds of billions (frontier models like GPT-5.2 and Claude Opus 4.5). They run as cloud APIs and increasingly as on-device models too.
Example
When you type 'What's the capital of France?' into ChatGPT, the underlying LLM (GPT-5.2) predicts the most-likely next tokens given that prompt — which happen to spell out 'The capital of France is Paris.' No lookup table is involved; the answer emerges from learned statistical patterns.
Large Language Model (LLM) in Vincony
Vincony aggregates 750+ LLMs from 15+ providers behind a single account. You can query GPT-5.2, Claude Opus 4.5, Gemini 3 Pro, Llama 4, DeepSeek V3, and many others through one interface.
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