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 .

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. Additionally, counting tokens also returns the billable characters for the prompt, which helps you estimate cost.

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 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

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 
  
 ai 
  
 = 
  
 new 
  
 GoogleGenAI 
 ({ 
  
 vertexai 
 : 
  
 true 
 , 
  
 project 
 : 
  
 projectId 
 , 
  
 location 
 : 
  
 location 
 , 
  
 httpOptions 
 : 
  
 { 
 apiVersion 
 : 
  
 'v1' 
 }, 
  
 }); 
  
 const 
  
 response 
  
 = 
  
 await 
  
 ai 
 . 
 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 and billable characters of a prompt

The following code sample shows you how to Get the token count and the number of billable characters 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 
  
 ai 
  
 = 
  
 new 
  
 GoogleGenAI 
 ({ 
  
 vertexai 
 : 
  
 true 
 , 
  
 project 
 : 
  
 projectId 
 , 
  
 location 
 : 
  
 location 
 , 
  
 httpOptions 
 : 
  
 { 
 apiVersion 
 : 
  
 'v1' 
 }, 
  
 }); 
  
 const 
  
 response 
  
 = 
  
 await 
  
 ai 
 . 
 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 
 (); 
 } 
 } 
 }

List and count tokens Stay organized with collections Save and categorize content based on your preferences.

Tokens and the importance of token listing and counting

Supported models

Get a list of tokens and token IDs for a prompt

Python

Install

Go

Node.js

Install

Java

Get the token count and billable characters of a prompt

Python

Install

Go

Node.js

Install

Java

List and count tokens