Starting April 29, 2025, Gemini 1.5 Pro and Gemini 1.5 Flash models are not available in projects that have no prior usage of these models, including new projects. For details, see Model versions and lifecycle .

CountTokens API

The CountTokens API calculates the number of input tokens before sending a request to the Gemini API.

Use the CountTokens API to prevent requests from exceeding the model context window, and estimate potential costs based on billable characters or tokens.

The CountTokens API can use the same contents parameter as Gemini API inference requests.

Supported models

Gemini 2.5 Flash Image Preview (Preview)
Gemini 2.5 Flash-Lite
Gemini 2.0 Flash with image generation (Preview)
Vertex AI Model Optimizer (Experimental)
Gemini 2.5 Pro
Gemini 2.5 Flash
Gemini 2.0 Flash
Gemini 2.0 Flash-Lite

Parameter list

This class consists of two main properties: role and parts . The role property denotes the individual producing the content, while the parts property contains multiple elements, each representing a segment of data within a message.

Parameters

role

Optional: string

The identity of the entity that creates the message. Set the string to one of the following:

user : This indicates that the message is sent by a real person. For example, a user-generated message.
model : This indicates that the message is generated by the model.

The model value is used to insert messages from the model into the conversation during multi-turn conversations.

For non-multi-turn conversations, this field can be left blank or unset.

parts

part

A list of ordered parts that make up a single message. Different parts may have different IANA MIME types .

`Part`

A data type containing media that is part of a multi-part Content message.

Parameters

text

Optional: string

A text prompt or code snippet.

inline_data

Optional: Blob

Inline data in raw bytes.

file_data

Optional: FileData

Data stored in a file.

`Blob`

Content blob. If possible this send as text rather than raw bytes.

Parameters

mime_type

string

IANA MIME type of the data.

data

bytes

Raw bytes.

`FileData`

URI based data.

Parameters

mime_type

string

IANA MIME type of the data.

file_uri

string

The Cloud Storage URI to the file storing the data.

`system_instruction`

This field is for user provided system_instructions . It is the same as contents but with a limited support of the content types.

Parameters

role

string

IANA MIME type of the data. This field is ignored internally.

parts

Part

Text only. Instructions that users want to pass to the model.

`FunctionDeclaration`

A structured representation of a function declaration as defined by the OpenAPI 3.0 specification that represents a function the model may generate JSON inputs for.

Parameters

name

string

The name of the function to call.

description

Optional: string

Description and purpose of the function.

parameters

Optional: Schema

Describes the parameters of the function in the OpenAPI JSON Schema Object format: OpenAPI 3.0 specification .

response

Optional: Schema

Describes the output from the function in the OpenAPI JSON Schema Object format: OpenAPI 3.0 specification .

Examples

Get token count from text prompt

This example counts the tokens of a single text prompt:

REST

To get the token count and the number of billable characters for a prompt by using the Vertex AI API, send a POST request to the publisher model endpoint.

Before using any of the request data, make the following replacements:

LOCATION : The region to process the request. Available options include the following:
Click to expand a partial list of available regions
- us-central1
- us-west4
- northamerica-northeast1
- us-east4
- us-west1
- asia-northeast3
- asia-southeast1
- asia-northeast1
PROJECT_ID : Your project ID .
MODEL_ID : The model ID of the multimodal model that you want to use.
ROLE : The role in a conversation associated with the content. Specifying a role is required even in singleturn use cases. Acceptable values include the following:
- USER : Specifies content that's sent by you.
TEXT : The text instructions to include in the prompt.

HTTP method and URL:

POST https:// LOCATION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION 
/publishers/google/models/ MODEL_ID 
:countTokens

Request JSON body:

{
  "contents": [{
    "role": " ROLE 
",
    "parts": [{
      "text": " TEXT 
"
    }]
  }]
}

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell , which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list .

Save the request body in a file named request.json , and execute the following command:

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https:// LOCATION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION 
/publishers/google/models/ MODEL_ID 
:countTokens"

PowerShell

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list .

Save the request body in a file named request.json , and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https:// LOCATION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION 
/publishers/google/models/ MODEL_ID 
:countTokens" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Response

{
  "totalTokens": 31,
  "totalBillableCharacters": 96,
  "promptTokensDetails": [
    {
      "modality": "TEXT",
      "tokenCount": 31
    }
  ]
}

Python

Install

pip install --upgrade google-genai

To learn more, see the SDK reference documentation .

Set environment variables to use the Gen AI SDK with Vertex AI:

 # Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values 
 # with appropriate values for your project. 
 export 
  
 GOOGLE_CLOUD_PROJECT 
 = 
 GOOGLE_CLOUD_PROJECT 
 export 
  
 GOOGLE_CLOUD_LOCATION 
 = 
 global 
 export 
  
 GOOGLE_GENAI_USE_VERTEXAI 
 = 
True

  from 
  
 google 
  
 import 
 genai 
 from 
  
 google.genai.types 
  
 import 
 HttpOptions 
 client 
 = 
 genai 
 . 
 Client 
 ( 
 http_options 
 = 
 HttpOptions 
 ( 
 api_version 
 = 
 "v1" 
 )) 
 response 
 = 
 client 
 . 
 models 
 . 
 count_tokens 
 ( 
 model 
 = 
 "gemini-2.5-flash" 
 , 
 contents 
 = 
 "What's the highest mountain in Africa?" 
 , 
 ) 
 print 
 ( 
 response 
 ) 
 # Example output: 
 # total_tokens=10 
 # cached_content_token_count=None

Go

Learn how to install or update the Go .

To learn more, see the SDK reference documentation .

Set environment variables to use the Gen AI SDK with Vertex AI:

 # Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values 
 # with appropriate values for your project. 
 export 
  
 GOOGLE_CLOUD_PROJECT 
 = 
 GOOGLE_CLOUD_PROJECT 
 export 
  
 GOOGLE_CLOUD_LOCATION 
 = 
 global 
 export 
  
 GOOGLE_GENAI_USE_VERTEXAI 
 = 
True

  import 
  
 ( 
 "context" 
 "fmt" 
 "io" 
 genai 
 "google.golang.org/genai" 
 ) 
 // 
 countWithTxt 
 shows 
 how 
 to 
 count 
 tokens 
 with 
 text 
 input 
 . 
 func 
 countWithTxt 
 ( 
 w 
 io 
 . 
 Writer 
 ) 
 error 
 { 
 ctx 
 := 
 context 
 . 
 Background 
 () 
 client 
 , 
 err 
 := 
 genai 
 . 
 NewClient 
 ( 
 ctx 
 , 
& genai 
 . 
 ClientConfig 
 { 
 HTTPOptions 
 : 
 genai 
 . 
 HTTPOptions 
 { 
 APIVersion 
 : 
 "v1" 
 }, 
 }) 
 if 
 err 
 != 
 nil 
 { 
 return 
 fmt 
 . 
 Errorf 
 ( 
 "failed to create genai client: %w" 
 , 
 err 
 ) 
 } 
 modelName 
 := 
 "gemini-2.5-flash" 
 contents 
 := 
 [] 
 * 
 genai 
 . 
 Content 
 { 
 { 
 Parts 
 : 
 [] 
 * 
 genai 
 . 
 Part 
 { 
 { 
 Text 
 : 
 "What's the highest mountain in Africa?" 
 }, 
 }}, 
 } 
 resp 
 , 
 err 
 := 
 client 
 . 
 Models 
 . 
 CountTokens 
 ( 
 ctx 
 , 
 modelName 
 , 
 contents 
 , 
 nil 
 ) 
 if 
 err 
 != 
 nil 
 { 
 return 
 fmt 
 . 
 Errorf 
 ( 
 "failed to generate content: %w" 
 , 
 err 
 ) 
 } 
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
 "Total: 
 %d 
 \n 
 Cached: 
 %d 
 \n 
 " 
 , 
 resp 
 . 
 TotalTokens 
 , 
 resp 
 . 
 CachedContentTokenCount 
 ) 
 // 
 Example 
 response 
 : 
 // 
 Total 
 : 
 9 
 // 
 Cached 
 : 
 0 
 return 
 nil 
 }

Node.js

Install

npm install @google/genai

To learn more, see the SDK reference documentation .

Set environment variables to use the Gen AI SDK with Vertex AI:

 # Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values 
 # with appropriate values for your project. 
 export 
  
 GOOGLE_CLOUD_PROJECT 
 = 
 GOOGLE_CLOUD_PROJECT 
 export 
  
 GOOGLE_CLOUD_LOCATION 
 = 
 global 
 export 
  
 GOOGLE_GENAI_USE_VERTEXAI 
 = 
True

  const 
  
 { 
 GoogleGenAI 
 } 
  
 = 
  
 require 
 ( 
 '@google/genai' 
 ); 
 const 
  
 GOOGLE_CLOUD_PROJECT 
  
 = 
  
 process 
 . 
 env 
 . 
 GOOGLE_CLOUD_PROJECT 
 ; 
 const 
  
 GOOGLE_CLOUD_LOCATION 
  
 = 
  
 process 
 . 
 env 
 . 
 GOOGLE_CLOUD_LOCATION 
  
 || 
  
 'global' 
 ; 
 async 
  
 function 
  
 countTokens 
 ( 
  
 projectId 
  
 = 
  
 GOOGLE_CLOUD_PROJECT 
 , 
  
 location 
  
 = 
  
 GOOGLE_CLOUD_LOCATION 
 ) 
  
 { 
  
 const 
  
 ai 
  
 = 
  
 new 
  
 GoogleGenAI 
 ({ 
  
 vertexai 
 : 
  
 true 
 , 
  
 project 
 : 
  
 projectId 
 , 
  
 location 
 : 
  
 location 
 , 
  
 }); 
  
 const 
  
 response 
  
 = 
  
 await 
  
 ai 
 . 
 models 
 . 
 countTokens 
 ({ 
  
 model 
 : 
  
 'gemini-2.5-flash' 
 , 
  
 contents 
 : 
  
 'What is the highest mountain in Africa?' 
 , 
  
 }); 
  
 console 
 . 
 log 
 ( 
 response 
 ); 
  
 return 
  
 response 
 . 
 totalTokens 
 ; 
 }

Java

Learn how to install or update the Java .

To learn more, see the SDK reference documentation .

Set environment variables to use the Gen AI SDK with Vertex AI:

 # Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values 
 # with appropriate values for your project. 
 export 
  
 GOOGLE_CLOUD_PROJECT 
 = 
 GOOGLE_CLOUD_PROJECT 
 export 
  
 GOOGLE_CLOUD_LOCATION 
 = 
 global 
 export 
  
 GOOGLE_GENAI_USE_VERTEXAI 
 = 
True

  import 
  
 com.google.genai.Client 
 ; 
 import 
  
 com.google.genai.types.CountTokensResponse 
 ; 
 import 
  
 com.google.genai.types.HttpOptions 
 ; 
 import 
  
 java.util.Optional 
 ; 
 public 
 class 
  
 CountTokensWithText 
 { 
 public 
 static 
 void 
 main 
 ( 
 String 
 [] 
 args 
 ) 
 { 
 // 
 TODO 
 ( 
 developer 
 ): 
 Replace 
 these 
 variables 
 before 
 running 
 the 
 sample 
 . 
 String 
 modelId 
 = 
 "gemini-2.5-flash" 
 ; 
 countTokens 
 ( 
 modelId 
 ); 
 } 
 // 
 Counts 
 tokens 
 with 
 text 
 input 
 public 
 static 
 Optional<Integer> 
 countTokens 
 ( 
 String 
 modelId 
 ) 
 { 
 // 
 Initialize 
 client 
 that 
 will 
 be 
 used 
 to 
 send 
 requests 
 . 
 This 
 client 
 only 
 needs 
 to 
 be 
 created 
 // 
 once 
 , 
 and 
 can 
 be 
 reused 
 for 
 multiple 
 requests 
 . 
 try 
 ( 
 Client 
 client 
 = 
 Client 
 . 
 builder 
 () 
 . 
 location 
 ( 
 "global" 
 ) 
 . 
 vertexAI 
 ( 
 true 
 ) 
 . 
 httpOptions 
 ( 
 HttpOptions 
 . 
 builder 
 () 
 . 
 apiVersion 
 ( 
 "v1" 
 ) 
 . 
 build 
 ()) 
 . 
 build 
 ()) 
 { 
 CountTokensResponse 
 response 
 = 
 client 
 . 
 models 
 . 
 countTokens 
 ( 
 modelId 
 , 
 "What's the highest mountain in Africa?" 
 , 
 null 
 ); 
 System 
 . 
 out 
 . 
 print 
 ( 
 response 
 ); 
 // 
 Example 
 response 
 : 
 // 
 CountTokensResponse 
 { 
 totalTokens 
 = 
 Optional 
 [ 
 9 
 ], 
 cachedContentTokenCount 
 = 
 Optional 
 . 
 empty 
 } 
 return 
 response 
 . 
 totalTokens 
 (); 
 } 
 } 
 }

Get token count from media prompt

This example counts the tokens of a prompt that uses various media types.

REST

To get the token count and the number of billable characters for a prompt by using the Vertex AI API, send a POST request to the publisher model endpoint.

Before using any of the request data, make the following replacements:

LOCATION : The region to process the request. Available options include the following:
Click to expand a partial list of available regions
- us-central1
- us-west4
- northamerica-northeast1
- us-east4
- us-west1
- asia-northeast3
- asia-southeast1
- asia-northeast1
PROJECT_ID : .
MODEL_ID : The model ID of the multimodal model that you want to use.
ROLE : The role in a conversation associated with the content. Specifying a role is required even in singleturn use cases. Acceptable values include the following:
- USER : Specifies content that's sent by you.
TEXT : The text instructions to include in the prompt.
FILE_URI : The URI or URL of the file to include in the prompt. Acceptable values include the following:
- Cloud Storage bucket URI: The object must either be publicly readable or reside in the same Google Cloud project that's sending the request. For gemini-2.0-flash and gemini-2.0-flash-lite , the size limit is 2 GB.
- HTTP URL: The file URL must be publicly readable. You can specify one video file, one audio file, and up to 10 image files per request. Audio files, video files, and documents can't exceed 15 MB.
- YouTube video URL: The YouTube video must be either owned by the account that you used to sign in to the Google Cloud console or is public. Only one YouTube video URL is supported per request.
When specifying a fileURI , you must also specify the media type ( mimeType ) of the file. If VPC Service Controls is enabled, specifying a media file URL for fileURI is not supported.
MIME_TYPE : The media type of the file specified in the data or fileUri fields. Acceptable values include the following:
Click to expand MIME types
- application/pdf
- audio/mpeg
- audio/mp3
- audio/wav
- image/png
- image/jpeg
- image/webp
- text/plain
- video/mov
- video/mpeg
- video/mp4
- video/mpg
- video/avi
- video/wmv
- video/mpegps
- video/flv

HTTP method and URL:

POST https:// LOCATION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION 
/publishers/google/models/ MODEL_ID 
:countTokens

Request JSON body:

{
  "contents": [{
    "role": " ROLE 
",
    "parts": [
      {
        "file_data": {
          "file_uri": " FILE_URI 
",
          "mime_type": " MIME_TYPE 
"
        }
      },
      {
        "text": " TEXT 
}
    ]
  }]
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json , and execute the following command:

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https:// LOCATION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION 
/publishers/google/models/ MODEL_ID 
:countTokens"

PowerShell

Save the request body in a file named request.json , and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https:// LOCATION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION 
/publishers/google/models/ MODEL_ID 
:countTokens" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Response

{
  "totalTokens": 31,
  "totalBillableCharacters": 96,
  "promptTokensDetails": [
    {
      "modality": "TEXT","IMAGE"
      "tokenCount": 31
    }
  ]
}

Python

Install

pip install --upgrade google-genai

To learn more, see the SDK reference documentation .

Set environment variables to use the Gen AI SDK with Vertex AI:

 # Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values 
 # with appropriate values for your project. 
 export 
  
 GOOGLE_CLOUD_PROJECT 
 = 
 GOOGLE_CLOUD_PROJECT 
 export 
  
 GOOGLE_CLOUD_LOCATION 
 = 
 global 
 export 
  
 GOOGLE_GENAI_USE_VERTEXAI 
 = 
True

  from 
  
 google 
  
 import 
 genai 
 from 
  
 google.genai.types 
  
 import 
 HttpOptions 
 , 
 Part 
 client 
 = 
 genai 
 . 
 Client 
 ( 
 http_options 
 = 
 HttpOptions 
 ( 
 api_version 
 = 
 "v1" 
 )) 
 contents 
 = 
 [ 
 Part 
 . 
 from_uri 
 ( 
 file_uri 
 = 
 "gs://cloud-samples-data/generative-ai/video/pixel8.mp4" 
 , 
 mime_type 
 = 
 "video/mp4" 
 , 
 ), 
 "Provide a description of the video." 
 , 
 ] 
 response 
 = 
 client 
 . 
 models 
 . 
 count_tokens 
 ( 
 model 
 = 
 "gemini-2.5-flash" 
 , 
 contents 
 = 
 contents 
 , 
 ) 
 print 
 ( 
 response 
 ) 
 # Example output: 
 # total_tokens=16252 cached_content_token_count=None

Go

Learn how to install or update the Go .

To learn more, see the SDK reference documentation .

Set environment variables to use the Gen AI SDK with Vertex AI:

 # Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values 
 # with appropriate values for your project. 
 export 
  
 GOOGLE_CLOUD_PROJECT 
 = 
 GOOGLE_CLOUD_PROJECT 
 export 
  
 GOOGLE_CLOUD_LOCATION 
 = 
 global 
 export 
  
 GOOGLE_GENAI_USE_VERTEXAI 
 = 
True

  import 
  
 ( 
 "context" 
 "fmt" 
 "io" 
 genai 
 "google.golang.org/genai" 
 ) 
 // 
 countWithTxtAndVid 
 shows 
 how 
 to 
 count 
 tokens 
 with 
 text 
 and 
 video 
 inputs 
 . 
 func 
 countWithTxtAndVid 
 ( 
 w 
 io 
 . 
 Writer 
 ) 
 error 
 { 
 ctx 
 := 
 context 
 . 
 Background 
 () 
 client 
 , 
 err 
 := 
 genai 
 . 
 NewClient 
 ( 
 ctx 
 , 
& genai 
 . 
 ClientConfig 
 { 
 HTTPOptions 
 : 
 genai 
 . 
 HTTPOptions 
 { 
 APIVersion 
 : 
 "v1" 
 }, 
 }) 
 if 
 err 
 != 
 nil 
 { 
 return 
 fmt 
 . 
 Errorf 
 ( 
 "failed to create genai client: %w" 
 , 
 err 
 ) 
 } 
 modelName 
 := 
 "gemini-2.5-flash" 
 contents 
 := 
 [] 
 * 
 genai 
 . 
 Content 
 { 
 { 
 Parts 
 : 
 [] 
 * 
 genai 
 . 
 Part 
 { 
 { 
 Text 
 : 
 "Provide a description of the video." 
 }, 
 { 
 FileData 
 : 
& genai 
 . 
 FileData 
 { 
 FileURI 
 : 
 "gs://cloud-samples-data/generative-ai/video/pixel8.mp4" 
 , 
 MIMEType 
 : 
 "video/mp4" 
 , 
 }}, 
 }, 
 Role 
 : 
 "user" 
 }, 
 } 
 resp 
 , 
 err 
 := 
 client 
 . 
 Models 
 . 
 CountTokens 
 ( 
 ctx 
 , 
 modelName 
 , 
 contents 
 , 
 nil 
 ) 
 if 
 err 
 != 
 nil 
 { 
 return 
 fmt 
 . 
 Errorf 
 ( 
 "failed to generate content: %w" 
 , 
 err 
 ) 
 } 
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
 "Total: 
 %d 
 \n 
 Cached: 
 %d 
 \n 
 " 
 , 
 resp 
 . 
 TotalTokens 
 , 
 resp 
 . 
 CachedContentTokenCount 
 ) 
 // 
 Example 
 response 
 : 
 // 
 Total 
 : 
 16252 
 // 
 Cached 
 : 
 0 
 return 
 nil 
 }

Node.js

Install

npm install @google/genai

To learn more, see the SDK reference documentation .

Set environment variables to use the Gen AI SDK with Vertex AI:

 # Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values 
 # with appropriate values for your project. 
 export 
  
 GOOGLE_CLOUD_PROJECT 
 = 
 GOOGLE_CLOUD_PROJECT 
 export 
  
 GOOGLE_CLOUD_LOCATION 
 = 
 global 
 export 
  
 GOOGLE_GENAI_USE_VERTEXAI 
 = 
True

  const 
  
 { 
 GoogleGenAI 
 } 
  
 = 
  
 require 
 ( 
 '@google/genai' 
 ); 
 const 
  
 GOOGLE_CLOUD_PROJECT 
  
 = 
  
 process 
 . 
 env 
 . 
 GOOGLE_CLOUD_PROJECT 
 ; 
 const 
  
 GOOGLE_CLOUD_LOCATION 
  
 = 
  
 process 
 . 
 env 
 . 
 GOOGLE_CLOUD_LOCATION 
  
 || 
  
 'global' 
 ; 
 async 
  
 function 
  
 countTokens 
 ( 
  
 projectId 
  
 = 
  
 GOOGLE_CLOUD_PROJECT 
 , 
  
 location 
  
 = 
  
 GOOGLE_CLOUD_LOCATION 
 ) 
  
 { 
  
 const 
  
 ai 
  
 = 
  
 new 
  
 GoogleGenAI 
 ({ 
  
 vertexai 
 : 
  
 true 
 , 
  
 project 
 : 
  
 projectId 
 , 
  
 location 
 : 
  
 location 
 , 
  
 }); 
  
 const 
  
 video 
  
 = 
  
 { 
  
 fileData 
 : 
  
 { 
  
 fileUri 
 : 
  
 'gs://cloud-samples-data/generative-ai/video/pixel8.mp4' 
 , 
  
 mimeType 
 : 
  
 'video/mp4' 
 , 
  
 }, 
  
 }; 
  
 const 
  
 response 
  
 = 
  
 await 
  
 ai 
 . 
 models 
 . 
 countTokens 
 ({ 
  
 model 
 : 
  
 'gemini-2.5-flash' 
 , 
  
 contents 
 : 
  
 [ 
 video 
 , 
  
 'Provide a description of the video.' 
 ], 
  
 }); 
  
 console 
 . 
 log 
 ( 
 response 
 ); 
  
 return 
  
 response 
 . 
 totalTokens 
 ; 
 }

Java

Learn how to install or update the Java .

To learn more, see the SDK reference documentation .

Set environment variables to use the Gen AI SDK with Vertex AI:

 # Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values 
 # with appropriate values for your project. 
 export 
  
 GOOGLE_CLOUD_PROJECT 
 = 
 GOOGLE_CLOUD_PROJECT 
 export 
  
 GOOGLE_CLOUD_LOCATION 
 = 
 global 
 export 
  
 GOOGLE_GENAI_USE_VERTEXAI 
 = 
True

  import 
  
 com.google.genai.Client 
 ; 
 import 
  
 com.google.genai.types.Content 
 ; 
 import 
  
 com.google.genai.types.CountTokensResponse 
 ; 
 import 
  
 com.google.genai.types.HttpOptions 
 ; 
 import 
  
 com.google.genai.types.Part 
 ; 
 import 
  
 java.util.List 
 ; 
 import 
  
 java.util.Optional 
 ; 
 public 
 class 
  
 CountTokensWithTextAndVideo 
 { 
 public 
 static 
 void 
 main 
 ( 
 String 
 [] 
 args 
 ) 
 { 
 // 
 TODO 
 ( 
 developer 
 ): 
 Replace 
 these 
 variables 
 before 
 running 
 the 
 sample 
 . 
 String 
 modelId 
 = 
 "gemini-2.5-flash" 
 ; 
 countTokens 
 ( 
 modelId 
 ); 
 } 
 // 
 Counts 
 tokens 
 with 
 text 
 and 
 video 
 inputs 
 public 
 static 
 Optional<Integer> 
 countTokens 
 ( 
 String 
 modelId 
 ) 
 { 
 // 
 Initialize 
 client 
 that 
 will 
 be 
 used 
 to 
 send 
 requests 
 . 
 This 
 client 
 only 
 needs 
 to 
 be 
 created 
 // 
 once 
 , 
 and 
 can 
 be 
 reused 
 for 
 multiple 
 requests 
 . 
 try 
 ( 
 Client 
 client 
 = 
 Client 
 . 
 builder 
 () 
 . 
 location 
 ( 
 "global" 
 ) 
 . 
 vertexAI 
 ( 
 true 
 ) 
 . 
 httpOptions 
 ( 
 HttpOptions 
 . 
 builder 
 () 
 . 
 apiVersion 
 ( 
 "v1" 
 ) 
 . 
 build 
 ()) 
 . 
 build 
 ()) 
 { 
 Content 
 content 
 = 
 Content 
 . 
 fromParts 
 ( 
 Part 
 . 
 fromText 
 ( 
 "Provide a description of this video" 
 ), 
 Part 
 . 
 fromUri 
 ( 
 "gs://cloud-samples-data/generative-ai/video/pixel8.mp4" 
 , 
 "video/mp4" 
 )); 
 CountTokensResponse 
 response 
 = 
 client 
 . 
 models 
 . 
 countTokens 
 ( 
 modelId 
 , 
 List 
 . 
 of 
 ( 
 content 
 ), 
 null 
 ); 
 System 
 . 
 out 
 . 
 print 
 ( 
 response 
 ); 
 // 
 Example 
 response 
 : 
 // 
 CountTokensResponse 
 { 
 totalTokens 
 = 
 Optional 
 [ 
 16707 
 ], 
 cachedContentTokenCount 
 = 
 Optional 
 . 
 empty 
 } 
 return 
 response 
 . 
 totalTokens 
 (); 
 } 
 } 
 }

What's next

Learn more about the Gemini API .

CountTokens API Stay organized with collections Save and categorize content based on your preferences.

Supported models

Parameter list

Part

Blob

FileData

system_instruction

FunctionDeclaration

Examples

Get token count from text prompt

REST

curl

PowerShell

Response

Python

Install

Go

Node.js

Install

Java

Get token count from media prompt

REST

curl

PowerShell

Response

Python

Install

Go

Node.js

Install

Java

What's next

CountTokens API

`Part`

`Blob`

`FileData`

`system_instruction`

`FunctionDeclaration`