Counting tokens

Eine detaillierte Anleitung zum Zählen von Tokens mit der Gemini API, einschließlich der Zählweise von Bildern, Audio und Video, finden Sie im Leitfaden zum Zählen von Tokens und im zugehörigen Cookbook-Rezept.

Methode: models.countTokens

Führt den Tokenizer eines Modells für die Eingabe Content aus und gibt die Anzahl der Tokens zurück. Weitere Informationen zu Tokens finden Sie im Leitfaden zu Tokens.

Endpunkt

post https://generativelanguage.googleapis.com/v1beta/{model=models/*}:countTokens

Pfadparameter

model string

Erforderlich. Der Ressourcenname des Modells. Dient als ID für das zu verwendende Modell.

Dieser Name muss mit einem Modellnamen übereinstimmen, der von der Methode models.list zurückgegeben wird.

Format: models/{model}. Hat die Form models/{model}.

Anfragetext

Der Anfragetext enthält Daten mit folgender Struktur:

Felder
contents[] object (Content)

Optional. Die Eingabe, die dem Modell als Prompt gegeben wird. Dieses Feld wird ignoriert, wenn generateContentRequest festgelegt ist.

generateContentRequest object (GenerateContentRequest)

Optional. Die Gesamteingabe für das Model. Dazu gehören der Prompt sowie andere Informationen zur Modellsteuerung wie Systemanweisungen und/oder Funktionsdeklarationen für Funktionsaufrufe. Models/Contents und generateContentRequests schließen sich gegenseitig aus. Sie können entweder Model + Contents oder eine generateContentRequest senden, aber nicht beides.

Beispielanfrage

Text

Python

from google import genai

client = genai.Client()
prompt = "The quick brown fox jumps over the lazy dog."

# Count tokens using the new client method.
total_tokens = client.models.count_tokens(
    model="gemini-3.7-flash", contents=prompt
)
print("total_tokens: ", total_tokens)
# ( e.g., total_tokens: 10 )

response = client.models.generate_content(
    model="gemini-3.7-flash", contents=prompt
)

# The usage_metadata provides detailed token counts.
print(response.usage_metadata)
# ( e.g., prompt_token_count: 11, candidates_token_count: 73, total_token_count: 84 )

Node.js

// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const prompt = "The quick brown fox jumps over the lazy dog.";
const countTokensResponse = await ai.models.countTokens({
  model: "gemini-3.7-flash",
  contents: prompt,
});
console.log(countTokensResponse.totalTokens);

const generateResponse = await ai.models.generateContent({
  model: "gemini-3.7-flash",
  contents: prompt,
});
console.log(generateResponse.usageMetadata);

Ok

ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}
prompt := "The quick brown fox jumps over the lazy dog."

// Convert prompt to a slice of *genai.Content using the helper.
contents := []*genai.Content{
	genai.NewContentFromText(prompt, genai.RoleUser),
}
countResp, err := client.Models.CountTokens(ctx, "gemini-3.7-flash", contents, nil)
if err != nil {
	return err
}
fmt.Println("total_tokens:", countResp.TotalTokens)

response, err := client.Models.GenerateContent(ctx, "gemini-3.7-flash", contents, nil)
if err != nil {
	log.Fatal(err)
}
usageMetadata, err := json.MarshalIndent(response.UsageMetadata, "", "  ")
if err != nil {
	log.Fatal(err)
}
fmt.Println(string(usageMetadata))

Muschel

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:countTokens?key=$GEMINI_API_KEY \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[{
          "text": "The quick brown fox jumps over the lazy dog."
          }],
        }],
      }'

Chat

Python

from google import genai
from google.genai import types

client = genai.Client()

chat = client.chats.create(
    model="gemini-3.7-flash",
    history=[
        types.Content(
            role="user", parts=[types.Part(text="Hi my name is Bob")]
        ),
        types.Content(role="model", parts=[types.Part(text="Hi Bob!")]),
    ],
)
# Count tokens for the chat history.
print(
    client.models.count_tokens(
        model="gemini-3.7-flash", contents=chat.get_history()
    )
)
# ( e.g., total_tokens: 10 )

response = chat.send_message(
    message="In one sentence, explain how a computer works to a young child."
)
print(response.usage_metadata)
# ( e.g., prompt_token_count: 25, candidates_token_count: 21, total_token_count: 46 )

# You can count tokens for the combined history and a new message.
extra = types.UserContent(
    parts=[
        types.Part(
            text="What is the meaning of life?",
        )
    ]
)
history = chat.get_history()
history.append(extra)
print(client.models.count_tokens(model="gemini-3.7-flash", contents=history))
# ( e.g., total_tokens: 56 )

Node.js

// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
// Initial chat history.
const history = [
  { role: "user", parts: [{ text: "Hi my name is Bob" }] },
  { role: "model", parts: [{ text: "Hi Bob!" }] },
];
const chat = ai.chats.create({
  model: "gemini-3.7-flash",
  history: history,
});

// Count tokens for the current chat history.
const countTokensResponse = await ai.models.countTokens({
  model: "gemini-3.7-flash",
  contents: chat.getHistory(),
});
console.log(countTokensResponse.totalTokens);

const chatResponse = await chat.sendMessage({
  message: "In one sentence, explain how a computer works to a young child.",
});
console.log(chatResponse.usageMetadata);

// Add an extra user message to the history.
const extraMessage = {
  role: "user",
  parts: [{ text: "What is the meaning of life?" }],
};
const combinedHistory = chat.getHistory();
combinedHistory.push(extraMessage);
const combinedCountTokensResponse = await ai.models.countTokens({
  model: "gemini-3.7-flash",
  contents: combinedHistory,
});
console.log(
  "Combined history token count:",
  combinedCountTokensResponse.totalTokens,
);

Ok

ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}

// Initialize chat with some history.
history := []*genai.Content{
	{Role: genai.RoleUser, Parts: []*genai.Part{{Text: "Hi my name is Bob"}}},
	{Role: genai.RoleModel, Parts: []*genai.Part{{Text: "Hi Bob!"}}},
}
chat, err := client.Chats.Create(ctx, "gemini-3.7-flash", nil, history)
if err != nil {
	log.Fatal(err)
}

firstTokenResp, err := client.Models.CountTokens(ctx, "gemini-3.7-flash", chat.History(false), nil)
if err != nil {
	log.Fatal(err)
}
fmt.Println(firstTokenResp.TotalTokens)

resp, err := chat.SendMessage(ctx, genai.Part{
	Text: "In one sentence, explain how a computer works to a young child."},
)
if err != nil {
	log.Fatal(err)
}
fmt.Printf("%#v\n", resp.UsageMetadata)

// Append an extra user message and recount.
extra := genai.NewContentFromText("What is the meaning of life?", genai.RoleUser)
hist := chat.History(false)
hist = append(hist, extra)

secondTokenResp, err := client.Models.CountTokens(ctx, "gemini-3.7-flash", hist, nil)
if err != nil {
	log.Fatal(err)
}
fmt.Println(secondTokenResp.TotalTokens)

Muschel

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:countTokens?key=$GEMINI_API_KEY \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [
        {"role": "user",
        "parts": [{"text": "Hi, my name is Bob."}],
        },
        {"role": "model",
         "parts":[{"text": "Hi Bob"}],
        },
      ],
      }'

Inline-Medien

Python

from google import genai
import PIL.Image

client = genai.Client()
prompt = "Tell me about this image"
your_image_file = PIL.Image.open(media / "organ.jpg")

# Count tokens for combined text and inline image.
print(
    client.models.count_tokens(
        model="gemini-3.7-flash", contents=[prompt, your_image_file]
    )
)
# ( e.g., total_tokens: 263 )

response = client.models.generate_content(
    model="gemini-3.7-flash", contents=[prompt, your_image_file]
)
print(response.usage_metadata)
# ( e.g., prompt_token_count: 264, candidates_token_count: 80, total_token_count: 345 )

Node.js

// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const prompt = "Tell me about this image";
const imageBuffer = fs.readFileSync(path.join(media, "organ.jpg"));

// Convert buffer to base64 string.
const imageBase64 = imageBuffer.toString("base64");

// Build contents using createUserContent and createPartFromBase64.
const contents = createUserContent([
  prompt,
  createPartFromBase64(imageBase64, "image/jpeg"),
]);

const countTokensResponse = await ai.models.countTokens({
  model: "gemini-3.7-flash",
  contents: contents,
});
console.log(countTokensResponse.totalTokens);

const generateResponse = await ai.models.generateContent({
  model: "gemini-3.7-flash",
  contents: contents,
});
console.log(generateResponse.usageMetadata);

Ok

model := client.GenerativeModel("gemini-1.5-flash")
prompt := "Tell me about this image"
imageFile, err := os.ReadFile(filepath.Join(testDataDir, "personWorkingOnComputer.jpg"))
if err != nil {
	log.Fatal(err)
}
// Call `CountTokens` to get the input token count
// of the combined text and file (`total_tokens`).
// An image's display or file size does not affect its token count.
// Optionally, you can call `count_tokens` for the text and file separately.
tokResp, err := model.CountTokens(ctx, genai.Text(prompt), genai.ImageData("jpeg", imageFile))
if err != nil {
	log.Fatal(err)
}
fmt.Println("total_tokens:", tokResp.TotalTokens)
// ( total_tokens: 264 )

resp, err := model.GenerateContent(ctx, genai.Text(prompt), genai.ImageData("jpeg", imageFile))
if err != nil {
	log.Fatal(err)
}

fmt.Println("prompt_token_count:", resp.UsageMetadata.PromptTokenCount)
fmt.Println("candidates_token_count:", resp.UsageMetadata.CandidatesTokenCount)
fmt.Println("total_token_count:", resp.UsageMetadata.TotalTokenCount)
// ( prompt_token_count: 264, candidates_token_count: 100, total_token_count: 364 )

Muschel

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:countTokens?key=$GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
            {"text": "Tell me about this instrument"},
            {
              "inline_data": {
                "mime_type":"image/jpeg",
                "data": "'$(base64 $B64FLAGS $IMG_PATH)'"
              }
            }
        ]
        }]
       }' 2> /dev/null

Video

Python

from google import genai
import time

client = genai.Client()
prompt = "Tell me about this video"
your_file = client.files.upload(file=media / "Big_Buck_Bunny.mp4")

# Poll until the video file is completely processed (state becomes ACTIVE).
while not your_file.state or your_file.state.name != "ACTIVE":
    print("Processing video...")
    print("File state:", your_file.state)
    time.sleep(5)
    your_file = client.files.get(name=your_file.name)

print(
    client.models.count_tokens(
        model="gemini-3.7-flash", contents=[prompt, your_file]
    )
)
# ( e.g., total_tokens: 300 )

response = client.models.generate_content(
    model="gemini-3.7-flash", contents=[prompt, your_file]
)
print(response.usage_metadata)
# ( e.g., prompt_token_count: 301, candidates_token_count: 60, total_token_count: 361 )

Node.js

// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const prompt = "Tell me about this video";
let videoFile = await ai.files.upload({
  file: path.join(media, "Big_Buck_Bunny.mp4"),
  config: { mimeType: "video/mp4" },
});

// Poll until the video file is completely processed (state becomes ACTIVE).
while (!videoFile.state || videoFile.state.toString() !== "ACTIVE") {
  console.log("Processing video...");
  console.log("File state: ", videoFile.state);
  await sleep(5000);
  videoFile = await ai.files.get({ name: videoFile.name });
}

const countTokensResponse = await ai.models.countTokens({
  model: "gemini-3.7-flash",
  contents: createUserContent([
    prompt,
    createPartFromUri(videoFile.uri, videoFile.mimeType),
  ]),
});
console.log(countTokensResponse.totalTokens);

const generateResponse = await ai.models.generateContent({
  model: "gemini-3.7-flash",
  contents: createUserContent([
    prompt,
    createPartFromUri(videoFile.uri, videoFile.mimeType),
  ]),
});
console.log(generateResponse.usageMetadata);

Ok

ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}

file, err := client.Files.UploadFromPath(
	ctx, 
	filepath.Join(getMedia(), "Big_Buck_Bunny.mp4"), 
	&genai.UploadFileConfig{
		MIMEType : "video/mp4",
	},
)
if err != nil {
	log.Fatal(err)
}

// Poll until the video file is completely processed (state becomes ACTIVE).
for file.State == genai.FileStateUnspecified || file.State != genai.FileStateActive {
	fmt.Println("Processing video...")
	fmt.Println("File state:", file.State)
	time.Sleep(5 * time.Second)

	file, err = client.Files.Get(ctx, file.Name, nil)
	if err != nil {
		log.Fatal(err)
	}
}

parts := []*genai.Part{
	genai.NewPartFromText("Tell me about this video"),
	genai.NewPartFromURI(file.URI, file.MIMEType),
}
contents := []*genai.Content{
	genai.NewContentFromParts(parts, genai.RoleUser),
}

tokenResp, err := client.Models.CountTokens(ctx, "gemini-3.7-flash", contents, nil)
if err != nil {
	log.Fatal(err)
}
fmt.Println("Multimodal video/audio token count:", tokenResp.TotalTokens)
response, err := client.Models.GenerateContent(ctx, "gemini-3.7-flash", contents, nil)
if err != nil {
	log.Fatal(err)
}
usageMetadata, err := json.MarshalIndent(response.UsageMetadata, "", "  ")
if err != nil {
	log.Fatal(err)
}
fmt.Println(string(usageMetadata))

Muschel


MIME_TYPE=$(file -b --mime-type "${VIDEO_PATH}")
NUM_BYTES=$(wc -c < "${VIDEO_PATH}")
DISPLAY_NAME=VIDEO_PATH

# Initial resumable request defining metadata.
# The upload url is in the response headers dump them to a file.
curl "${BASE_URL}/upload/v1beta/files?key=${GOOGLE_API_KEY}" \
  -D upload-header.tmp \
  -H "X-Goog-Upload-Protocol: resumable" \
  -H "X-Goog-Upload-Command: start" \
  -H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Header-Content-Type: ${MIME_TYPE}" \
  -H "Content-Type: application/json" \
  -d "{'file': {'display_name': '${DISPLAY_NAME}'}}" 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r")
rm "${tmp_header_file}"

# Upload the actual bytes.
curl "${upload_url}" \
  -H "Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${VIDEO_PATH}" 2> /dev/null > file_info.json

file_uri=$(jq ".file.uri" file_info.json)

state=$(jq ".file.state" file_info.json)

name=$(jq ".file.name" file_info.json)

while [[ "($state)" = *"PROCESSING"* ]];
do
  echo "Processing video..."
  sleep 5
  # Get the file of interest to check state
  curl https://generativelanguage.googleapis.com/v1beta/files/$name > file_info.json
  state=$(jq ".file.state" file_info.json)
done

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:countTokens?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"text": "Describe this video clip"},
          {"file_data":{"mime_type": "video/mp4", "file_uri": '$file_uri'}}]
        }]
       }'

PDF

Python

from google import genai

client = genai.Client()
sample_pdf = client.files.upload(file=media / "test.pdf")
token_count = client.models.count_tokens(
    model="gemini-3.7-flash",
    contents=["Give me a summary of this document.", sample_pdf],
)
print(f"{token_count=}")

response = client.models.generate_content(
    model="gemini-3.7-flash",
    contents=["Give me a summary of this document.", sample_pdf],
)
print(response.usage_metadata)

Cache

Python

from google import genai
from google.genai import types
import time

client = genai.Client()
your_file = client.files.upload(file=media / "a11.txt")

cache = client.caches.create(
    model="gemini-3.7-flash",
    config={
        "contents": ["Here the Apollo 11 transcript:", your_file],
        "system_instruction": None,
        "tools": None,
    },
)

# Create a prompt.
prompt = "Please give a short summary of this file."

# Count tokens for the prompt (the cached content is not passed here).
print(client.models.count_tokens(model="gemini-3.7-flash", contents=prompt))
# ( e.g., total_tokens: 9 )

response = client.models.generate_content(
    model="gemini-3.7-flash",
    contents=prompt,
    config=types.GenerateContentConfig(
        cached_content=cache.name,
    ),
)
print(response.usage_metadata)
# ( e.g., prompt_token_count: ..., cached_content_token_count: ..., candidates_token_count: ... )
client.caches.delete(name=cache.name)

Node.js

  // Make sure to include the following import:
  // import {GoogleGenAI} from '@google/genai';
  const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
  const textFile = await ai.files.upload({
    file: path.join(media, "a11.txt"),
    config: { mimeType: "text/plain" },
  });

  const cache = await ai.caches.create({
    model: "gemini-3.7-flash",
    config: {
      contents: createUserContent([
        "Here the Apollo 11 transcript:",
        createPartFromUri(textFile.uri, textFile.mimeType),
      ]),
      system_instruction: null,
      tools: null,
    },
  });

  const prompt = "Please give a short summary of this file.";
  const countTokensResponse = await ai.models.countTokens({
    model: "gemini-3.7-flash",
    contents: prompt,
  });
  console.log(countTokensResponse.totalTokens);

  const generateResponse = await ai.models.generateContent({
    model: "gemini-3.7-flash",
    contents: prompt,
    config: { cachedContent: cache.name },
  });
  console.log(generateResponse.usageMetadata);

  await ai.caches.delete({ name: cache.name });
  return {
    totalTokens: countTokensResponse.totalTokens,
    usage: generateResponse.usageMetadata,
  };
}

Ok

ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}

file, err := client.Files.UploadFromPath(
	ctx, 
	filepath.Join(getMedia(), "a11.txt"), 
	&genai.UploadFileConfig{
		MIMEType : "text/plain",
	},
)
if err != nil {
	log.Fatal(err)
}
parts := []*genai.Part{
	genai.NewPartFromText("Here the Apollo 11 transcript:"),
	genai.NewPartFromURI(file.URI, file.MIMEType),
}
contents := []*genai.Content{
	genai.NewContentFromParts(parts, genai.RoleUser),
}

// Create cached content using a simple slice with text and a file.
cache, err := client.Caches.Create(ctx, "gemini-3.7-flash", &genai.CreateCachedContentConfig{
	Contents: contents,
})
if err != nil {
	log.Fatal(err)
}

prompt := "Please give a short summary of this file."
countResp, err := client.Models.CountTokens(ctx, "gemini-3.7-flash", []*genai.Content{
	genai.NewContentFromText(prompt, genai.RoleUser),
}, nil)
if err != nil {
	log.Fatal(err)
}
fmt.Printf("%d", countResp.TotalTokens)
response, err := client.Models.GenerateContent(ctx, "gemini-3.7-flash", []*genai.Content{
	genai.NewContentFromText(prompt, genai.RoleUser),
}, &genai.GenerateContentConfig{
	CachedContent: cache.Name,
})
if err != nil {
	log.Fatal(err)
}

usageMetadata, err := json.MarshalIndent(response.UsageMetadata, "", "  ")
if err != nil {
	log.Fatal(err)
}
// Returns `nil` for some reason
fmt.Println(string(usageMetadata))
_, err = client.Caches.Delete(ctx, cache.Name, &genai.DeleteCachedContentConfig{})

Systemanweisung

Ok

ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}

// Construct the user message contents.
contents := []*genai.Content{
	genai.NewContentFromText("Good morning! How are you?", genai.RoleUser),
}

// Set the system instruction as a *genai.Content.
config := &genai.GenerateContentConfig{
	SystemInstruction: genai.NewContentFromText("You are a cat. Your name is Neko.", genai.RoleUser),
}

response, err := client.Models.GenerateContent(ctx, "gemini-3.7-flash", contents, config)
if err != nil {
	log.Fatal(err)
}
printResponse(response)

Tools

Java

FunctionDeclaration multiplyDefinition =
    defineFunction(
        /* name  */ "multiply",
        /* description */ "returns a * b.",
        /* parameters */ Arrays.asList(
            Schema.numDouble("a", "First parameter"),
            Schema.numDouble("b", "Second parameter")),
        /* required */ Arrays.asList("a", "b"));

Tool tool = new Tool(Arrays.asList(multiplyDefinition), null);
;

// Specify a Gemini model appropriate for your use case
GenerativeModel gm =
    new GenerativeModel(
        /* modelName */ "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key"
        // above)
        /* apiKey */ BuildConfig.apiKey,
        /* generationConfig (optional) */ null,
        /* safetySettings (optional) */ null,
        /* requestOptions (optional) */ new RequestOptions(),
        /* tools (optional) */ Arrays.asList(tool));
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

Content inputContent = new Content.Builder().addText("What's your name?.").build();

// For illustrative purposes only. You should use an executor that fits your needs.
Executor executor = Executors.newSingleThreadExecutor();

// For text-only input
ListenableFuture<CountTokensResponse> countTokensResponse = model.countTokens(inputContent);

Futures.addCallback(
    countTokensResponse,
    new FutureCallback<CountTokensResponse>() {
      @Override
      public void onSuccess(CountTokensResponse result) {
        int totalTokens = result.getTotalTokens();
        System.out.println("TotalTokens = " + totalTokens);
      }

      @Override
      public void onFailure(Throwable t) {
        t.printStackTrace();
      }
    },
    executor);

Antworttext

Eine Antwort von models.countTokens.

Gibt die tokenCount des Modells für den prompt zurück.

Bei Erfolg enthält der Antworttext Daten mit der folgenden Struktur:

Felder
totalTokens integer

Die Anzahl der Tokens, in die das Model den prompt tokenisiert. Immer nicht negativ.

cachedContentTokenCount integer

Anzahl der Tokens im im Cache gespeicherten Teil des Prompts (im Cache gespeicherter Inhalt).

promptTokensDetails[] object (ModalityTokenCount)

Nur Ausgabe. Liste der Modalitäten, die in der Anfrageneingabe verarbeitet wurden.

cacheTokensDetails[] object (ModalityTokenCount)

Nur Ausgabe. Liste der Modalitäten, die im im Cache gespeicherten Inhalt verarbeitet wurden.

JSON-Darstellung 
{
  "totalTokens": integer,
  "cachedContentTokenCount": integer,
  "promptTokensDetails": [
    {
      object (ModalityTokenCount)
    }
  ],
  "cacheTokensDetails": [
    {
      object (ModalityTokenCount)
    }
  ]
}