List and count tokens

This page shows you how to list the tokens and their token IDs of a prompt and how to get a total token count of a prompt by using the Google Gen AI SDK.

Tokens and the importance of token listing and counting

Generative AI models break down text and other data in a prompt into units called tokens for processing. The way that data is converted into tokens depends on the tokenizer used. A token can be characters, words, or phrases.

Each model has a maximum number of tokens that it can handle in a prompt and response. Knowing the token count of your prompt lets you know whether you've exceeded this limit or not.

Listing tokens returns a list of the tokens that your prompt is broken down into. Each listed token is associated with a token ID, which helps you perform troubleshooting and analyze model behavior.

Supported models

The following table shows you the models that support token listing and token counting:

Gemini 2.5 Flash (Preview)
Gemini 2.5 Flash-Lite (Preview)
Gemini 2.5 Flash Image
Gemini 2.5 Flash-Lite
Gemini 2.0 Flash with image generation (Preview)
Gemini 2.5 Pro
Gemini 2.5 Flash
Gemini 2.0 Flash
Gemini 2.0 Flash-Lite

Get a list of tokens and token IDs for a prompt

The following code sample shows you how to get a list of tokens and token IDs for a prompt. The prompt must contain only text. Multimodal prompts are not supported.

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 
 . 
 compute_tokens 
 ( 
 model 
 = 
 "gemini-2.5-flash" 
 , 
 contents 
 = 
 "What's the longest word in the English language?" 
 , 
 ) 
 print 
 ( 
 response 
 ) 
 # Example output: 
 # tokens_info=[TokensInfo( 
 #    role='user', 
 #    token_ids=[1841, 235303, 235256, 573, 32514, 2204, 575, 573, 4645, 5255, 235336], 
 #    tokens=[b'What', b"'", b's', b' the', b' longest', b' word', b' in', b' the', b' English', b' language', b'?'] 
 #  )]

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" 
 "encoding/json" 
 "fmt" 
 "io" 
 genai 
 "google.golang.org/genai" 
 ) 
 // 
 computeWithTxt 
 shows 
 how 
 to 
 compute 
 tokens 
 with 
 text 
 input 
 . 
 func 
 computeWithTxt 
 ( 
 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 longest word in the English language?" 
 }, 
 }, 
 Role 
 : 
 "user" 
 }, 
 } 
 resp 
 , 
 err 
 := 
 client 
 . 
 Models 
 . 
 ComputeTokens 
 ( 
 ctx 
 , 
 modelName 
 , 
 contents 
 , 
 nil 
 ) 
 if 
 err 
 != 
 nil 
 { 
 return 
 fmt 
 . 
 Errorf 
 ( 
 "failed to generate content: %w" 
 , 
 err 
 ) 
 } 
 type 
 tokenInfoDisplay 
 struct 
 { 
 IDs 
 [] 
 int64 
 ` 
 json 
 : 
 "token_ids" 
 ` 
 Tokens 
 [] 
 string 
 ` 
 json 
 : 
 "tokens" 
 ` 
 } 
 // 
 See 
 the 
 documentation 
 : 
 https 
 : 
 // 
 pkg 
 . 
 go 
 . 
 dev 
 / 
 google 
 . 
 golang 
 . 
 org 
 / 
 genai 
 #ComputeTokensResponse 
 for 
 _ 
 , 
 instance 
 := 
 range 
 resp 
 . 
 TokensInfo 
 { 
 display 
 := 
 tokenInfoDisplay 
 { 
 IDs 
 : 
 instance 
 . 
 TokenIDs 
 , 
 Tokens 
 : 
 make 
 ([] 
 string 
 , 
 len 
 ( 
 instance 
 . 
 Tokens 
 )), 
 } 
 for 
 i 
 , 
 t 
 := 
 range 
 instance 
 . 
 Tokens 
 { 
 display 
 . 
 Tokens 
 [ 
 i 
 ] 
 = 
 string 
 ( 
 t 
 ) 
 } 
 data 
 , 
 err 
 := 
 json 
 . 
 MarshalIndent 
 ( 
 display 
 , 
 "" 
 , 
 "  " 
 ) 
 if 
 err 
 != 
 nil 
 { 
 return 
 fmt 
 . 
 Errorf 
 ( 
 "failed to marshal token info: %w" 
 , 
 err 
 ) 
 } 
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
 string 
 ( 
 data 
 )) 
 } 
 // 
 Example 
 response 
 : 
 // 
 { 
 // 
 "ids" 
 : 
 [ 
 // 
 1841 
 , 
 // 
 235303 
 , 
 // 
 235256 
 , 
 // 
 ... 
 // 
 ], 
 // 
 "values" 
 : 
 [ 
 // 
 "What" 
 , 
 // 
 "'" 
 , 
 // 
 "s" 
 , 
 // 
 ... 
 // 
 ] 
 // 
 } 
 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 
  
 client 
  
 = 
  
 new 
  
 GoogleGenAI 
 ({ 
  
 vertexai 
 : 
  
 true 
 , 
  
 project 
 : 
  
 projectId 
 , 
  
 location 
 : 
  
 location 
 , 
  
 httpOptions 
 : 
  
 { 
 apiVersion 
 : 
  
 'v1' 
 }, 
  
 }); 
  
 const 
  
 response 
  
 = 
  
 await 
  
 client 
 . 
 models 
 . 
 computeTokens 
 ({ 
  
 model 
 : 
  
 'gemini-2.5-flash' 
 , 
  
 contents 
 : 
  
 "What's the longest word in the English language?" 
 , 
  
 }); 
  
 console 
 . 
 log 
 ( 
 response 
 ); 
  
 return 
  
 response 
 . 
 tokensInfo 
 ; 
 }

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.ComputeTokensResponse 
 ; 
 import 
  
 com.google.genai.types.HttpOptions 
 ; 
 import 
  
 com.google.genai.types.TokensInfo 
 ; 
 import 
  
 java.nio.charset.StandardCharsets 
 ; 
 import 
  
 java.util.List 
 ; 
 import 
  
 java.util.Optional 
 ; 
 public 
 class 
  
 CountTokensComputeWithText 
 { 
 public 
 static 
 void 
 main 
 ( 
 String 
 [] 
 args 
 ) 
 { 
 // 
 TODO 
 ( 
 developer 
 ): 
 Replace 
 these 
 variables 
 before 
 running 
 the 
 sample 
 . 
 String 
 modelId 
 = 
 "gemini-2.5-flash" 
 ; 
 computeTokens 
 ( 
 modelId 
 ); 
 } 
 // 
 Computes 
 tokens 
 with 
 text 
 input 
 public 
 static 
 Optional<List<TokensInfo> 
> computeTokens 
 ( 
 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 
 ()) 
 { 
 ComputeTokensResponse 
 response 
 = 
 client 
 . 
 models 
 . 
 computeTokens 
 ( 
 modelId 
 , 
 "What's the longest word in the English language?" 
 , 
 null 
 ); 
 // 
 Print 
 TokensInfo 
 response 
 . 
 tokensInfo 
 () 
 . 
 ifPresent 
 ( 
 tokensInfoList 
 - 
> { 
 for 
 ( 
 TokensInfo 
 info 
 : 
 tokensInfoList 
 ) 
 { 
 info 
 . 
 role 
 () 
 . 
 ifPresent 
 ( 
 role 
 - 
> System 
 . 
 out 
 . 
 println 
 ( 
 "role: " 
 + 
 role 
 )); 
 info 
 . 
 tokenIds 
 () 
 . 
 ifPresent 
 ( 
 tokenIds 
 - 
> System 
 . 
 out 
 . 
 println 
 ( 
 "tokenIds: " 
 + 
 tokenIds 
 )); 
 // 
 print 
 tokens 
 input 
 as 
 strings 
 since 
 they 
 are 
 in 
 a 
 form 
 of 
 byte 
 array 
 System 
 . 
 out 
 . 
 println 
 ( 
 "tokens: " 
 ); 
 info 
 . 
 tokens 
 () 
 . 
 ifPresent 
 ( 
 tokens 
 - 
> tokens 
 . 
 forEach 
 ( 
 token 
 - 
> System 
 . 
 out 
 . 
 println 
 ( 
 new 
 String 
 ( 
 token 
 , 
 StandardCharsets 
 . 
 UTF_8 
 )) 
 ) 
 ); 
 } 
 }); 
 // 
 Example 
 response 
 . 
 tokensInfo 
 () 
 // 
 role 
 : 
 user 
 // 
 tokenIds 
 : 
 [ 
 1841 
 , 
 235303 
 , 
 235256 
 , 
 573 
 , 
 32514 
 , 
 2204 
 , 
 575 
 , 
 573 
 , 
 4645 
 , 
 5255 
 , 
 235336 
 ] 
 // 
 tokens 
 : 
 // 
 What 
 // 
 ' 
 // 
 s 
 // 
 the 
 return 
 response 
 . 
 tokensInfo 
 (); 
 } 
 } 
 }

Get the token count of a prompt

The following code sample shows you how to Get the token count of a prompt. Both text-only and multimodal prompts are supported.

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" 
 )) 
 prompt 
 = 
 "Why is the sky blue?" 
 # Send text to Gemini 
 response 
 = 
 client 
 . 
 models 
 . 
 generate_content 
 ( 
 model 
 = 
 "gemini-2.5-flash" 
 , 
 contents 
 = 
 prompt 
 ) 
 # Prompt and response tokens count 
 print 
 ( 
 response 
 . 
 usage_metadata 
 ) 
 # Example output: 
 #  cached_content_token_count=None 
 #  candidates_token_count=311 
 #  prompt_token_count=6 
 #  total_token_count=317

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" 
 "encoding/json" 
 "fmt" 
 "io" 
 genai 
 "google.golang.org/genai" 
 ) 
 // 
 generateTextAndCount 
 shows 
 how 
 to 
 generate 
 text 
 and 
 obtain 
 token 
 count 
 metadata 
 from 
  
 the 
 model 
 response 
 . 
 func 
 generateTextAndCount 
 ( 
 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 
 : 
 "Why is the sky blue?" 
 }, 
 }, 
 Role 
 : 
 "user" 
 }, 
 } 
 resp 
 , 
 err 
 := 
 client 
 . 
 Models 
 . 
 GenerateContent 
 ( 
 ctx 
 , 
 modelName 
 , 
 contents 
 , 
 nil 
 ) 
 if 
 err 
 != 
 nil 
 { 
 return 
 fmt 
 . 
 Errorf 
 ( 
 "failed to generate content: %w" 
 , 
 err 
 ) 
 } 
 usage 
 , 
 err 
 := 
 json 
 . 
 MarshalIndent 
 ( 
 resp 
 . 
 UsageMetadata 
 , 
 "" 
 , 
 "  " 
 ) 
 if 
 err 
 != 
 nil 
 { 
 return 
 fmt 
 . 
 Errorf 
 ( 
 "failed to convert usage metadata to JSON: %w" 
 , 
 err 
 ) 
 } 
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
 string 
 ( 
 usage 
 )) 
 // 
 Example 
 response 
 : 
 // 
 { 
 // 
 "candidatesTokenCount" 
 : 
 339 
 , 
 // 
 "promptTokenCount" 
 : 
 6 
 , 
 // 
 "totalTokenCount" 
 : 
 345 
 // 
 } 
 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 
  
 client 
  
 = 
  
 new 
  
 GoogleGenAI 
 ({ 
  
 vertexai 
 : 
  
 true 
 , 
  
 project 
 : 
  
 projectId 
 , 
  
 location 
 : 
  
 location 
 , 
  
 httpOptions 
 : 
  
 { 
 apiVersion 
 : 
  
 'v1' 
 }, 
  
 }); 
  
 const 
  
 response 
  
 = 
  
 await 
  
 client 
 . 
 models 
 . 
 generateContent 
 ({ 
  
 model 
 : 
  
 'gemini-2.5-flash' 
 , 
  
 contents 
 : 
  
 'Why is the sky blue?' 
 , 
  
 }); 
  
 console 
 . 
 log 
 ( 
 response 
 . 
 usageMetadata 
 ); 
  
 return 
  
 response 
 . 
 usageMetadata 
 ; 
 }

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.GenerateContentResponse 
 ; 
 import 
  
 com.google.genai.types.GenerateContentResponseUsageMetadata 
 ; 
 import 
  
 com.google.genai.types.HttpOptions 
 ; 
 import 
  
 java.util.Optional 
 ; 
 public 
 class 
  
 CountTokensResponseWithText 
 { 
 public 
 static 
 void 
 main 
 ( 
 String 
 [] 
 args 
 ) 
 { 
 // 
 TODO 
 ( 
 developer 
 ): 
 Replace 
 these 
 variables 
 before 
 running 
 the 
 sample 
 . 
 String 
 modelId 
 = 
 "gemini-2.5-flash" 
 ; 
 countTokens 
 ( 
 modelId 
 ); 
 } 
 // 
 Generates 
 content 
 response 
 usage 
 metadata 
 that 
 contains 
 prompt 
 and 
 response 
 token 
 counts 
 public 
 static 
 Optional<GenerateContentResponseUsageMetadata> 
 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 
 ()) 
 { 
 GenerateContentResponse 
 response 
 = 
 client 
 . 
 models 
 . 
 generateContent 
 ( 
 modelId 
 , 
 "Why is the sky blue?" 
 , 
 null 
 ); 
 response 
 . 
 usageMetadata 
 () 
 . 
 ifPresent 
 ( 
 System 
 . 
 out 
 :: 
 println 
 ); 
 // 
 Example 
 response 
 : 
 // 
 GenerateContentResponseUsageMetadata 
 { 
 cacheTokensDetails 
 = 
 Optional 
 . 
 empty 
 , 
 // 
 cachedContentTokenCount 
 = 
 Optional 
 . 
 empty 
 , 
 candidatesTokenCount 
 = 
 Optional 
 [ 
 569 
 ], 
 // 
 candidatesTokensDetails 
 = 
 Optional 
 [[ 
 ModalityTokenCount 
 { 
 modality 
 = 
 Optional 
 [ 
 TEXT 
 ], 
 // 
 tokenCount 
 = 
 Optional 
 [ 
 569 
 ]}]], 
 promptTokenCount 
 = 
 Optional 
 [ 
 6 
 ], 
 // 
 promptTokensDetails 
 = 
 Optional 
 [[ 
 ModalityTokenCount 
 { 
 modality 
 = 
 Optional 
 [ 
 TEXT 
 ], 
 // 
 tokenCount 
 = 
 Optional 
 [ 
 6 
 ]}]], 
 thoughtsTokenCount 
 = 
 Optional 
 [ 
 1132 
 ], 
 // 
 toolUsePromptTokenCount 
 = 
 Optional 
 . 
 empty 
 , 
 toolUsePromptTokensDetails 
 = 
 Optional 
 . 
 empty 
 , 
 // 
 totalTokenCount 
 = 
 Optional 
 [ 
 1707 
 ], 
 trafficType 
 = 
 Optional 
 [ 
 ON_DEMAND 
 ]} 
 return 
 response 
 . 
 usageMetadata 
 (); 
 } 
 } 
 }