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Gemini - Google AI Studio

PropertyDetails
DescriptionGoogle AI Studio is a fully-managed AI development platform for building and using generative AI.
Provider Route on LiteLLMgemini/
Provider DocGoogle AI Studio ↗
API Endpoint for Providerhttps://generativelanguage.googleapis.com
Supported OpenAI Endpoints/chat/completions, /embeddings, /completions
Pass-through EndpointSupported

API Keys​

import os
os.environ["GEMINI_API_KEY"] = "your-api-key"

Sample Usage​

from litellm import completion
import os

os.environ['GEMINI_API_KEY'] = ""
response = completion(
    model="gemini/gemini-pro", 
    messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}]
)

Supported OpenAI Params​

  • temperature
  • top_p
  • max_tokens
  • max_completion_tokens
  • stream
  • tools
  • tool_choice
  • functions
  • response_format
  • n
  • stop
  • logprobs
  • frequency_penalty
  • modalities
  • reasoning_content
  • audio (for TTS models only)

Anthropic Params

  • thinking (used to set max budget tokens across anthropic/gemini models)

See Updated List

Usage - Thinking / reasoning_content​

LiteLLM translates OpenAI's reasoning_effort to Gemini's thinking parameter. Code

Added an additional non-OpenAI standard "disable" value for non-reasoning Gemini requests.

Mapping

reasoning_effortthinking
"disable""budget_tokens": 0
"low""budget_tokens": 1024
"medium""budget_tokens": 2048
"high""budget_tokens": 4096
from litellm import completion

resp = completion(
    model="gemini/gemini-2.5-flash-preview-04-17",
    messages=[{"role": "user", "content": "What is the capital of France?"}],
    reasoning_effort="low",
)

Expected Response

ModelResponse(
    id='chatcmpl-c542d76d-f675-4e87-8e5f-05855f5d0f5e',
    created=1740470510,
    model='claude-3-7-sonnet-20250219',
    object='chat.completion',
    system_fingerprint=None,
    choices=[
        Choices(
            finish_reason='stop',
            index=0,
            message=Message(
                content="The capital of France is Paris.",
                role='assistant',
                tool_calls=None,
                function_call=None,
                reasoning_content='The capital of France is Paris. This is a very straightforward factual question.'
            ),
        )
    ],
    usage=Usage(
        completion_tokens=68,
        prompt_tokens=42,
        total_tokens=110,
        completion_tokens_details=None,
        prompt_tokens_details=PromptTokensDetailsWrapper(
            audio_tokens=None,
            cached_tokens=0,
            text_tokens=None,
            image_tokens=None
        ),
        cache_creation_input_tokens=0,
        cache_read_input_tokens=0
    )
)

Pass thinking to Gemini models​

You can also pass the thinking parameter to Gemini models.

This is translated to Gemini's thinkingConfig parameter.

response = litellm.completion(
  model="gemini/gemini-2.5-flash-preview-04-17",
  messages=[{"role": "user", "content": "What is the capital of France?"}],
  thinking={"type": "enabled", "budget_tokens": 1024},
)

Text-to-Speech (TTS) Audio Output​

info

LiteLLM supports Gemini TTS models that can generate audio responses using the OpenAI-compatible audio parameter format.

Supported Models​

LiteLLM supports Gemini TTS models with audio capabilities (e.g. gemini-2.5-flash-preview-tts and gemini-2.5-pro-preview-tts). For the complete list of available TTS models and voices, see the official Gemini TTS documentation.

Limitations​

warning

Important Limitations:

  • Gemini TTS models only support the pcm16 audio format
  • Streaming support has not been added to TTS models yet
  • The modalities parameter must be set to ['audio'] for TTS requests

Quick Start​

from litellm import completion
import os

os.environ['GEMINI_API_KEY'] = "your-api-key"

response = completion(
    model="gemini/gemini-2.5-flash-preview-tts",
    messages=[{"role": "user", "content": "Say hello in a friendly voice"}],
    modalities=["audio"],  # Required for TTS models
    audio={
        "voice": "Kore",
        "format": "pcm16"  # Required: must be "pcm16"
    }
)

print(response)

Advanced Usage​

You can combine TTS with other Gemini features:

response = completion(
    model="gemini/gemini-2.5-pro-preview-tts",
    messages=[
        {"role": "system", "content": "You are a helpful assistant that speaks clearly."},
        {"role": "user", "content": "Explain quantum computing in simple terms"}
    ],
    modalities=["audio"],
    audio={
        "voice": "Charon",
        "format": "pcm16"
    },
    temperature=0.7,
    max_tokens=150
)

For more information about Gemini's TTS capabilities and available voices, see the official Gemini TTS documentation.

Passing Gemini Specific Params​

Response schema​

LiteLLM supports sending response_schema as a param for Gemini-1.5-Pro on Google AI Studio.

Response Schema

from litellm import completion 
import json 
import os 

os.environ['GEMINI_API_KEY'] = ""

messages = [
    {
        "role": "user",
        "content": "List 5 popular cookie recipes."
    }
]

response_schema = {
        "type": "array",
        "items": {
            "type": "object",
            "properties": {
                "recipe_name": {
                    "type": "string",
                },
            },
            "required": ["recipe_name"],
        },
    }


completion(
    model="gemini/gemini-1.5-pro", 
    messages=messages, 
    response_format={"type": "json_object", "response_schema": response_schema} # 👈 KEY CHANGE
    )

print(json.loads(completion.choices[0].message.content))

Validate Schema

To validate the response_schema, set enforce_validation: true.

from litellm import completion, JSONSchemaValidationError
try: 
	completion(
    model="gemini/gemini-1.5-pro", 
    messages=messages, 
    response_format={
        "type": "json_object", 
        "response_schema": response_schema,
        "enforce_validation": true # 👈 KEY CHANGE
    }
	)
except JSONSchemaValidationError as e: 
	print("Raw Response: {}".format(e.raw_response))
	raise e

LiteLLM will validate the response against the schema, and raise a JSONSchemaValidationError if the response does not match the schema.

JSONSchemaValidationError inherits from openai.APIError

Access the raw response with e.raw_response

GenerationConfig Params​

To pass additional GenerationConfig params - e.g. topK, just pass it in the request body of the call, and LiteLLM will pass it straight through as a key-value pair in the request body.

See Gemini GenerationConfigParams

from litellm import completion 
import json 
import os 

os.environ['GEMINI_API_KEY'] = ""

messages = [
    {
        "role": "user",
        "content": "List 5 popular cookie recipes."
    }
]

completion(
    model="gemini/gemini-1.5-pro", 
    messages=messages, 
    topK=1 # 👈 KEY CHANGE
)

print(json.loads(completion.choices[0].message.content))

Validate Schema

To validate the response_schema, set enforce_validation: true.

from litellm import completion, JSONSchemaValidationError
try: 
	completion(
    model="gemini/gemini-1.5-pro", 
    messages=messages, 
    response_format={
        "type": "json_object", 
        "response_schema": response_schema,
        "enforce_validation": true # 👈 KEY CHANGE
    }
	)
except JSONSchemaValidationError as e: 
	print("Raw Response: {}".format(e.raw_response))
	raise e

Specifying Safety Settings​

In certain use-cases you may need to make calls to the models and pass safety settings different from the defaults. To do so, simple pass the safety_settings argument to completion or acompletion. For example:

response = completion(
    model="gemini/gemini-pro", 
    messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}],
    safety_settings=[
        {
            "category": "HARM_CATEGORY_HARASSMENT",
            "threshold": "BLOCK_NONE",
        },
        {
            "category": "HARM_CATEGORY_HATE_SPEECH",
            "threshold": "BLOCK_NONE",
        },
        {
            "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
            "threshold": "BLOCK_NONE",
        },
        {
            "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
            "threshold": "BLOCK_NONE",
        },
    ]
)

Tool Calling​

from litellm import completion
import os
# set env
os.environ["GEMINI_API_KEY"] = ".."

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["location"],
            },
        },
    }
]
messages = [{"role": "user", "content": "What's the weather like in Boston today?"}]

response = completion(
    model="gemini/gemini-1.5-flash",
    messages=messages,
    tools=tools,
)
# Add any assertions, here to check response args
print(response)
assert isinstance(response.choices[0].message.tool_calls[0].function.name, str)
assert isinstance(
    response.choices[0].message.tool_calls[0].function.arguments, str
)


Google Search Tool​

from litellm import completion
import os

os.environ["GEMINI_API_KEY"] = ".."

tools = [{"googleSearch": {}}] # 👈 ADD GOOGLE SEARCH

response = completion(
    model="gemini/gemini-2.0-flash",
    messages=[{"role": "user", "content": "What is the weather in San Francisco?"}],
    tools=tools,
)

print(response)

URL Context​

from litellm import completion
import os

os.environ["GEMINI_API_KEY"] = ".."

# 👇 ADD URL CONTEXT
tools = [{"urlContext": {}}]

response = completion(
    model="gemini/gemini-2.0-flash",
    messages=[{"role": "user", "content": "Summarize this document: https://ai.google.dev/gemini-api/docs/models"}],
    tools=tools,
)

print(response)

# Access URL context metadata
url_context_metadata = response.model_extra['vertex_ai_url_context_metadata']
urlMetadata = url_context_metadata[0]['urlMetadata'][0]
print(f"Retrieved URL: {urlMetadata['retrievedUrl']}")
print(f"Retrieval Status: {urlMetadata['urlRetrievalStatus']}")

Google Search Retrieval​

from litellm import completion
import os

os.environ["GEMINI_API_KEY"] = ".."

tools = [{"googleSearch": {}}] # 👈 ADD GOOGLE SEARCH

response = completion(
    model="gemini/gemini-2.0-flash",
    messages=[{"role": "user", "content": "What is the weather in San Francisco?"}],
    tools=tools,
)

print(response)

Code Execution Tool​

from litellm import completion
import os

os.environ["GEMINI_API_KEY"] = ".."

tools = [{"codeExecution": {}}] # 👈 ADD GOOGLE SEARCH

response = completion(
    model="gemini/gemini-2.0-flash",
    messages=[{"role": "user", "content": "What is the weather in San Francisco?"}],
    tools=tools,
)

print(response)

JSON Mode​

from litellm import completion 
import json 
import os 

os.environ['GEMINI_API_KEY'] = ""

messages = [
    {
        "role": "user",
        "content": "List 5 popular cookie recipes."
    }
]



completion(
    model="gemini/gemini-1.5-pro", 
    messages=messages, 
    response_format={"type": "json_object"} # 👈 KEY CHANGE
)

print(json.loads(completion.choices[0].message.content))

Gemini-Pro-Vision

LiteLLM Supports the following image types passed in url

Sample Usage​

import os
import litellm
from dotenv import load_dotenv

# Load the environment variables from .env file
load_dotenv()
os.environ["GEMINI_API_KEY"] = os.getenv('GEMINI_API_KEY')

prompt = 'Describe the image in a few sentences.'
# Note: You can pass here the URL or Path of image directly.
image_url = 'https://storage.googleapis.com/github-repo/img/gemini/intro/landmark3.jpg'

# Create the messages payload according to the documentation
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": prompt
            },
            {
                "type": "image_url",
                "image_url": {"url": image_url}
            }
        ]
    }
]

# Make the API call to Gemini model
response = litellm.completion(
    model="gemini/gemini-pro-vision",
    messages=messages,
)

# Extract the response content
content = response.get('choices', [{}])[0].get('message', {}).get('content')

# Print the result
print(content)

Usage - PDF / Videos / etc. Files​

Inline Data (e.g. audio stream)​

LiteLLM follows the OpenAI format and accepts sending inline data as an encoded base64 string.

The format to follow is

data:<mime_type>;base64,<encoded_data>

** LITELLM CALL **

import litellm
from pathlib import Path
import base64
import os

os.environ["GEMINI_API_KEY"] = "" 

litellm.set_verbose = True # 👈 See Raw call 

audio_bytes = Path("speech_vertex.mp3").read_bytes()
encoded_data = base64.b64encode(audio_bytes).decode("utf-8")
print("Audio Bytes = {}".format(audio_bytes))
model = "gemini/gemini-1.5-flash"
response = litellm.completion(
    model=model,
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Please summarize the audio."},
                {
                    "type": "file",
                    "file": {
                        "file_data": "data:audio/mp3;base64,{}".format(encoded_data), # 👈 SET MIME_TYPE + DATA
                    }
                },
            ],
        }
    ],
)

** Equivalent GOOGLE API CALL **

# Initialize a Gemini model appropriate for your use case.
model = genai.GenerativeModel('models/gemini-1.5-flash')

# Create the prompt.
prompt = "Please summarize the audio."

# Load the samplesmall.mp3 file into a Python Blob object containing the audio
# file's bytes and then pass the prompt and the audio to Gemini.
response = model.generate_content([
    prompt,
    {
        "mime_type": "audio/mp3",
        "data": pathlib.Path('samplesmall.mp3').read_bytes()
    }
])

# Output Gemini's response to the prompt and the inline audio.
print(response.text)

https:// file​

import litellm
import os

os.environ["GEMINI_API_KEY"] = "" 

litellm.set_verbose = True # 👈 See Raw call 

model = "gemini/gemini-1.5-flash"
response = litellm.completion(
    model=model,
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Please summarize the file."},
                {
                    "type": "file",
                    "file": {
                        "file_id": "https://storage...", # 👈 SET THE IMG URL
                        "format": "application/pdf" # OPTIONAL
                    }
                },
            ],
        }
    ],
)

gs:// file​

import litellm
import os

os.environ["GEMINI_API_KEY"] = "" 

litellm.set_verbose = True # 👈 See Raw call 

model = "gemini/gemini-1.5-flash"
response = litellm.completion(
    model=model,
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Please summarize the file."},
                {
                    "type": "file",
                    "file": {
                        "file_id": "gs://storage...", # 👈 SET THE IMG URL
                        "format": "application/pdf" # OPTIONAL
                    }
                },
            ],
        }
    ],
)

Chat Models​

tip

We support ALL Gemini models, just set model=gemini/<any-model-on-gemini> as a prefix when sending litellm requests

Model NameFunction CallRequired OS Variables
gemini-procompletion(model='gemini/gemini-pro', messages)os.environ['GEMINI_API_KEY']
gemini-1.5-pro-latestcompletion(model='gemini/gemini-1.5-pro-latest', messages)os.environ['GEMINI_API_KEY']
gemini-2.0-flashcompletion(model='gemini/gemini-2.0-flash', messages)os.environ['GEMINI_API_KEY']
gemini-2.0-flash-expcompletion(model='gemini/gemini-2.0-flash-exp', messages)os.environ['GEMINI_API_KEY']
gemini-2.0-flash-lite-preview-02-05completion(model='gemini/gemini-2.0-flash-lite-preview-02-05', messages)os.environ['GEMINI_API_KEY']
gemini-2.5-flash-preview-09-2025completion(model='gemini/gemini-2.5-flash-preview-09-2025', messages)os.environ['GEMINI_API_KEY']
gemini-2.5-flash-lite-preview-09-2025completion(model='gemini/gemini-2.5-flash-lite-preview-09-2025', messages)os.environ['GEMINI_API_KEY']
gemini-flash-latestcompletion(model='gemini/gemini-flash-latest', messages)os.environ['GEMINI_API_KEY']
gemini-flash-lite-latestcompletion(model='gemini/gemini-flash-lite-latest', messages)os.environ['GEMINI_API_KEY']

Context Caching​

Use Google AI Studio context caching is supported by

{
    {
        "role": "system",
        "content": ...,
        "cache_control": {"type": "ephemeral"} # 👈 KEY CHANGE
    },
    ...
}

in your message content block.

Custom TTL Support​

You can now specify a custom Time-To-Live (TTL) for your cached content using the ttl parameter:

{
    {
        "role": "system",
        "content": ...,
        "cache_control": {
            "type": "ephemeral",
            "ttl": "3600s"  # 👈 Cache for 1 hour
        }
    },
    ...
}

TTL Format Requirements:

  • Must be a string ending with 's' for seconds
  • Must contain a positive number (can be decimal)
  • Examples: "3600s" (1 hour), "7200s" (2 hours), "1800s" (30 minutes), "1.5s" (1.5 seconds)

TTL Behavior:

  • If multiple cached messages have different TTLs, the first valid TTL encountered will be used
  • Invalid TTL formats are ignored and the cache will use Google's default expiration time
  • If no TTL is specified, Google's default cache expiration (approximately 1 hour) applies

Architecture Diagram​

Notes:

  • Relevant code

  • Gemini Context Caching only allows 1 block of continuous messages to be cached.

  • If multiple non-continuous blocks contain cache_control - the first continuous block will be used. (sent to /cachedContent in the Gemini format)

  • The raw request to Gemini's /generateContent endpoint looks like this:

curl -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash-001:generateContent?key=$GOOGLE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
      "contents": [
        {
          "parts":[{
            "text": "Please summarize this transcript"
          }],
          "role": "user"
        },
      ],
      "cachedContent": "'$CACHE_NAME'"
    }'

Example Usage​

from litellm import completion 

for _ in range(2): 
    resp = completion(
        model="gemini/gemini-1.5-pro",
        messages=[
        # System Message
            {
                "role": "system",
                "content": [
                    {
                        "type": "text",
                        "text": "Here is the full text of a complex legal agreement" * 4000,
                        "cache_control": {"type": "ephemeral"}, # 👈 KEY CHANGE
                    }
                ],
            },
            # marked for caching with the cache_control parameter, so that this checkpoint can read from the previous cache.
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": "What are the key terms and conditions in this agreement?",
                        "cache_control": {"type": "ephemeral"},
                    }
                ],
            }]
    )

    print(resp.usage) # 👈 2nd usage block will be less, since cached tokens used

Image Generation​

from litellm import completion 

response = completion(
    model="gemini/gemini-2.0-flash-exp-image-generation",
    messages=[{"role": "user", "content": "Generate an image of a cat"}],
    modalities=["image", "text"],
)
assert response.choices[0].message.content is not None # "data:image/png;base64,e4rr.."