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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, sorted by what you actually want to do: coding, chat, the small-model crowd, structured output, vision, embeddings, and audio.

  • Quantization, distillation, pruning: how a 140GB model fits on your laptop

    Three ways to shrink an LLM, and why one of them does almost all the work. What Q4_K_M actually means and what each shortcut costs you.

  • The local-LLM vocabulary

    Parameters, B, dense vs MoE, base vs instruct, tokens, context windows, chat templates, GGUF, and quant suffixes. Read it once and any HuggingFace model card stops being scary.

  • What leaves your machine when you use AI

    What providers actually see, log, and keep when you call an LLM API in 2026. What "we don't train on your data" really means, how free and paid tiers differ, and when local is the only safe choice.

  • LLM API bills, and why a token costs what it costs

    How input and output tokens get priced, why output runs 5-6x more, and how prompt caching cuts the input bill by 10x. Plus the hidden costs that ambush people.

  • The major LLMs in 2026

    A field guide to the closed frontier models and the open weights you can actually run. What the "B" numbers mean, and which size fits your machine.

  • Where AI actually runs: cloud, local, edge

    When you use AI, a model file is sitting on a real machine. There are only three places it can be, and which one decides almost everything else.

  • 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.