Gemini
AI/dictionary/gemini
Definition
Google's family of LLMs and the consumer chat product at gemini.google.com. Tightly integrated with Google's search index and Workspace apps.
Example
Gemini is Google's answer to ChatGPT, with native access to Search.
Related terms
Posts that use this term
- Picking a local model by task
The 2026 open leaders by task: coding (Qwen 2.5 Coder, DeepSeek-Coder), chat (Llama, Qwen, Mistral), small-model renaissance (Phi-3, Gemma 2), structured output, multimodal, embeddings.
- Quantization, distillation, pruning: making models fit
Three ways to shrink an LLM. Quantization (Q2-Q8 with K-quants in GGUF), distillation (teacher to student), pruning. Why Q4_K_M is the community default and what each lever costs.
- The local-LLM vocabulary
Parameters, B, dense vs MoE, base vs instruct, tokens, context window, chat template, GGUF, quantization suffixes. After this post you can read any HuggingFace model card.
- What leaves your machine when you use AI
What providers actually see, log, and retain when you call an LLM API in 2026. What 'we don't train on your data' really means, free vs paid tier differences, and when local is the only safe option.
- LLM APIs and the economics of tokens
How input vs output tokens are priced, why output is 5-6x more, what prompt caching saves you (10x), and the hidden costs (tokenizer drift, reasoning tokens, tool-call loops) that surprise people.
- The major LLMs in 2026
A tour of the closed frontier models (Claude, GPT, Gemini) and the open weights (Llama, Qwen, DeepSeek, Mistral). What 'B' means, what each is good at, and which size to actually run.
- Where AI actually runs: cloud, local, edge
Where the model file actually sits when you use AI: a datacenter GPU (cloud), your own machine (local), or the device's silicon (edge). The trade-offs and how to pick.
- The context window, and why models hallucinate
An LLM only sees a fixed-size slice of text at a time. When it doesn't know something, it predicts anyway — that's a hallucination, not a bug.
- From models to LLMs
An LLM is one kind of ML model — trained on text, predicts the next token. That single trick at scale gets you ChatGPT, and also explains where it breaks.
- AI, in plain words
What "AI" actually means, where the term came from, and why every product calls itself AI now. Sets up where machine learning and deep learning fit underneath.