List and count tokens

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'?'] # )]

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 } 

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; }

 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(); } } }

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

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 } 

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; }

 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(); } } }