API access to Google's Gemini models

Install this plugin in the same environment as LLM.

llm install llm-gemini

Configure the model by setting a key called "gemini" to your API key:

llm keys set gemini

<paste key here>

You can also set the API key by assigning it to the environment variable LLM_GEMINI_KEY.

Now run the model using -m gemini-2.0-flash, for example:

llm -m gemini-2.0-flash "A short joke about a pelican and a walrus"

A pelican and a walrus are sitting at a bar. The pelican orders a fishbowl cocktail, and the walrus orders a plate of clams. The bartender asks, "So, what brings you two together?"

The walrus sighs and says, "It's a long story. Let's just say we met through a mutual friend... of the fin."

You can set the default model to avoid the extra -m option:

llm models default gemini-2.0-flash
llm "A joke about a pelican and a walrus"

• gemini/gemini-2.5-flash-lite: Gemini 2.5 Flash Lite
• gemini/gemini-2.5-pro: Gemini 2.5 Pro
• gemini/gemini-2.5-flash: Gemini 2.5 Flash
• gemini/gemini-2.5-pro-preview-06-05
• gemini/gemini-2.5-flash-preview-05-20: Gemini 2.5 Flash preview (priced differently from 2.5 Flash)
• gemini/gemini-2.5-pro-preview-05-06
• gemini/gemini-2.5-flash-preview-04-17
• gemini/gemini-2.5-pro-preview-03-25
• gemini/gemini-2.5-pro-exp-03-25
• gemini/gemini-2.0-flash-lite
• gemini/gemini-2.0-pro-exp-02-05
• gemini/gemini-2.0-flash
• gemini/gemini-2.0-flash-thinking-exp-01-21: Experimental "thinking" model from January 2025
• gemini/gemini-2.0-flash-thinking-exp-1219
• gemini/gemma-3n-e4b-it
• gemini/gemma-3-27b-it
• gemini/gemma-3-12b-it
• gemini/gemma-3-4b-it
• gemini/gemma-3-1b-it
• gemini/learnlm-1.5-pro-experimental
• gemini/gemini-2.0-flash-exp
• gemini/gemini-exp-1206
• gemini/gemini-exp-1121
• gemini/gemini-exp-1114
• gemini/gemini-1.5-flash-8b-001
• gemini/gemini-1.5-flash-8b-latest: The least expensive model
• gemini/gemini-1.5-flash-002
• gemini/gemini-1.5-pro-002
• gemini/gemini-1.5-flash-001
• gemini/gemini-1.5-pro-001
• gemini/gemini-1.5-flash-latest
• gemini/gemini-1.5-pro-latest
• gemini/gemini-pro

All of these models have aliases that omit the gemini/ prefix, for example:

llm -m gemini-1.5-flash-8b-latest --schema 'name,age int,bio' 'invent a dog'

Gemini models are multi-modal. You can provide images, audio or video files as input like this:

llm -m gemini-2.0-flash 'extract text' -a image.jpg

Or with a URL:

llm -m gemini-2.0-flash-lite 'describe image' \
  -a https://static.simonwillison.net/static/2024/pelicans.jpg

Audio works too:

llm -m gemini-2.0-flash 'transcribe audio' -a audio.mp3

And video:

llm -m gemini-2.0-flash 'describe what happens' -a video.mp4

The Gemini prompting guide includes extensive advice on multi-modal prompting.

Use -o json_object 1 to force the output to be JSON:

llm -m gemini-2.0-flash -o json_object 1 \
  '3 largest cities in California, list of {"name": "..."}'

Outputs:

{"cities": [{"name": "Los Angeles"}, {"name": "San Diego"}, {"name": "San Jose"}]}

Gemini models can write and execute code - they can decide to write Python code, execute it in a secure sandbox and use the result as part of their response.

To enable this feature, use -o code_execution 1:

llm -m gemini-2.0-flash -o code_execution 1 \
'use python to calculate (factorial of 13) * 3'

Some Gemini models support Grounding with Google Search, where the model can run a Google search and use the results as part of answering a prompt.

Using this feature may incur additional requirements in terms of how you use the results. Consult Google's documentation for more details.

To run a prompt with Google search enabled, use -o google_search 1:

llm -m gemini-2.0-flash -o google_search 1 \
  'What happened in Ireland today?'

Use llm logs -c --json after running a prompt to see the full JSON response, which includes additional information about grounded results.

Gemini models support a URL context tool which, when enabled, allows the models to fetch additional content from URLs as part of their execution.

You can enable that with the -o url_context 1 option - for example:

llm -m gemini-2.5-flash -o url_context 1 'Latest headline on simonwillison.net'

Extra tokens introduced by this tool will be charged as input tokens. Use --usage to see details of those:

llm -m gemini-2.5-flash -o url_context 1 --usage \
  'Latest headline on simonwillison.net'

Outputs:

The latest headline on simonwillison.net as of August 17, 2025, is "TIL: Running a gpt-oss eval suite against LM Studio on a Mac.".
Token usage: 9,613 input, 87 output, {"candidatesTokenCount": 57, "promptTokensDetails": [{"modality": "TEXT", "tokenCount": 10}], "toolUsePromptTokenCount": 9603, "toolUsePromptTokensDetails": [{"modality": "TEXT", "tokenCount": 9603}], "thoughtsTokenCount": 30}

The "toolUsePromptTokenCount" key shows how many tokens were used for that URL context.

To chat interactively with the model, run llm chat:

llm chat -m gemini-2.0-flash

By default there is no timeout against the Gemini API. You can use the timeout option to protect against API requests that hang indefinitely.

With the CLI tool that looks like this, to set a 1.5 second timeout:

llm -m gemini-2.5-flash-preview-05-20 'epic saga about mice' -o timeout 1.5

In the Python library timeouts are used like this:

import httpx, llm

model = llm.get_model("gemini/gemini-2.5-flash-preview-05-20")

try:
    response = model.prompt(
        "epic saga about mice", timeout=1.5
    )
    print(response.text())
except httpx.TimeoutException:
    print("Timeout exceeded")

An httpx.TimeoutException subclass will be raised if the timeout is exceeded.

The plugin also adds support for the gemini-embedding-exp-03-07 and text-embedding-004 embedding models.

Run that against a single string like this:

llm embed -m text-embedding-004 -c 'hello world'

This returns a JSON array of 768 numbers.

The gemini-embedding-exp-03-07 model is larger, returning 3072 numbers. You can also use variants of it that are truncated down to smaller sizes:

• gemini-embedding-exp-03-07 - 3072 numbers
• gemini-embedding-exp-03-07-2048 - 2048 numbers
• gemini-embedding-exp-03-07-1024 - 1024 numbers
• gemini-embedding-exp-03-07-512 - 512 numbers
• gemini-embedding-exp-03-07-256 - 256 numbers
• gemini-embedding-exp-03-07-128 - 128 numbers

This command will embed every README.md file in child directories of the current directory and store the results in a SQLite database called embed.db in a collection called readmes:

llm embed-multi readmes -d embed.db -m gemini-embedding-exp-03-07-128 \
  --files . '*/README.md'

You can then run similarity searches against that collection like this:

llm similar readmes -c 'upload csvs to stuff' -d embed.db

See the LLM embeddings documentation for further details.

The llm gemini models command lists all of the models that are exposed by the Gemini API, some of which may not be available through this plugin.

llm gemini models

You can add a --key X option to use a different API key.

To filter models by their supported generation methods use --method one or more times:

llm gemini models --method embedContent

If you provide multiple methods you will see models that support any of them.

To set up this plugin locally, first checkout the code. Then create a new virtual environment:

cd llm-gemini
python3 -m venv venv
source venv/bin/activate

Now install the dependencies and test dependencies:

llm install -e '.[test]'

To run the tests:

pytest

This project uses pytest-recording to record Gemini API responses for the tests.

If you add a new test that calls the API you can capture the API response like this:

PYTEST_GEMINI_API_KEY="$(llm keys get gemini)" pytest --record-mode once

You will need to have stored a valid Gemini API key using this command first:

llm keys set gemini
# Paste key here