Google LLM Models: How to Choose for Your Work

Google offers two main large language model families for most users: Gemini, its main assistant and API model line, and Gemma, its open-source model family for developers who want to run models locally or build their own products. Understanding which family your chosen tool draws from helps you know what to expect and where its limits are.

Switching between Google models or moving from a Google model to another provider does not have to mean rebuilding your project context. Tools like fenbs keep that context outside the model, so it is available to whichever AI app you connect next. See what an LLM is if you want a grounding in the underlying technology first.

The Gemini model family

Gemini is Google's main model family for consumer and enterprise use. It powers Google AI tools including the Gemini web app and the Gemini API, and it integrates with Google Workspace products such as Docs, Sheets and Gmail. Models within the Gemini family vary in size and speed, with a smaller, faster option and a larger, more capable option for complex tasks.

Gemini models are multimodal: they can process text, images, audio and code in the same session. The Gemini web app (gemini.google.com) offers a consumer-facing interface; the Gemini API and Google AI Studio give developers access to the underlying models. Vertex AI on Google Cloud gives enterprises access with additional data controls.

Gemini Notebook (previously NotebookLM, renamed July 2026) is a separate product built on Gemini that focuses on reasoning over documents you upload. It is useful for research and summarization tasks where you want to ask questions about a specific set of sources.

Gemma: Google's open-source models

Gemma is a family of open-source models that Google releases for developers to download, modify and run on their own infrastructure. If you want to run a language model locally without sending data to Google, Gemma is the option to look at. It is smaller than Gemini's hosted models and suited for tasks where low latency, offline use or data privacy on-premise matters more than raw capability.

Gemma models come in several sizes. The larger variants perform closer to Gemini on standard benchmarks; the smaller ones run on a laptop or a lower-cost server. Developers compare Gemma to similar open-source options like open-source LLMs from Meta, Mistral and others when choosing a base model.

How to access Google models

The access route you choose affects cost, control and what you can customize.

• Gemini web app (gemini.google.com). The easiest starting point for personal and workplace questions. Sign in with a Google account. Integrated with Google Drive, Docs and Gmail when you connect Workspace.
• Google AI Studio (aistudio.google.com). A free developer interface for testing Gemini models, writing prompts and experimenting with the API before building anything.
• Gemini API via Google Cloud or AI Studio. For developers building applications or running the model at scale. Check the current pricing page on the Google AI site.
• Vertex AI. Google Cloud's enterprise AI platform, with stricter data handling, audit logs and compliance controls. Best for teams with specific privacy or regulatory requirements.
• Gemma download. Available on Hugging Face and Google's own site for developers who need to run a model locally or in a private environment.

Choosing a Google model by task

For most workplace questions, the Gemini web app or the Gemini API is the right starting point. Use the larger, more capable Gemini model when the task is complex — long document analysis, multi-step reasoning, code generation — and a faster, smaller model when you need quick responses or are running the model at volume in an application.

If you need to keep data out of Google's infrastructure, or need to run the model in a network-restricted environment, a Gemma model gives you a Google-trained option without the hosted dependency. For a comparison of Google's broader AI product range, see Google AI tools. For a side-by-side look at Claude, which is an alternative many teams use alongside Gemini, see Claude models.

Keeping project context when the model changes

A common issue when experimenting with Google model options is that switching models resets the context. The project background, the decisions you made, the current task and where it stands — all of that needs to be re-explained to a new session.

Keeping that context outside the model is the practical solution. A shared record that any connected assistant can read at the start of a session means the model choice becomes reversible without a cost in context. Gemini CLI is one of the supported Integrations in fenbs for teams that use Google's tools from the command line.

How to do this with fenbs

You can connect Gemini CLI to fenbs over MCP. The setup takes a few minutes and works with the same Google account you already use.

1. Create a free account. Free for one person, no credit card needed.
2. Add a project for the work you are doing with Google models. Write a short description of the goal, the current tasks and any constraints the model should respect.
3. Record key decisions in Decisions and rules: which model version you are using, why you chose it, and any evaluation criteria you want the next session to know.
4. Connect Gemini CLI from Integrations. The connection uses MCP and a browser sign-in; no API key is pasted into a config file.
5. Ask the connected assistant to read the project context at the start of each session. Before stopping, ask it to save progress on any tasks it completed.

fenbs does not host or train a model. If you are using a Google model that is not yet on the supported integrations list, you can still keep the project record in fenbs and reference it manually when opening a new session.