Counting tokens

Untuk panduan mendetail tentang cara menghitung token menggunakan Gemini API, termasuk cara menghitung gambar, audio, dan video, lihat panduan Penghitungan token dan resep Cookbook yang menyertainya.

Metode: models.countTokens

Menjalankan tokenizer model pada Content input dan menampilkan jumlah token. Lihat panduan token untuk mempelajari token lebih lanjut.

Endpoint

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

Parameter jalur

model string

Wajib. Nama resource model. Nama ini berfungsi sebagai ID untuk Model yang akan digunakan.

Nama ini harus cocok dengan nama model yang ditampilkan oleh metode models.list.

Format: models/{model} Formatnya adalah models/{model}.

Isi permintaan

Isi permintaan memuat data dengan struktur berikut:

Kolom
contents[] object (Content)

Opsional. Input yang diberikan ke model sebagai perintah. Kolom ini diabaikan saat generateContentRequest ditetapkan.

generateContentRequest object (GenerateContentRequest)

Opsional. Input keseluruhan yang diberikan ke Model. Hal ini mencakup perintah serta informasi pengarah model lainnya seperti petunjuk sistem, dan/atau deklarasi fungsi untuk panggilan fungsi. Models/Contents dan generateContentRequests bersifat eksklusif. Anda dapat mengirim Model + Contents atau generateContentRequest, tetapi tidak keduanya.

Contoh permintaan

Teks

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);

Go

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))

Shell

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,
);

Go

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)

Shell

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"}],
        },
      ],
      }'

Media inline

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);

Go

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 )

Shell

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);

Go

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))

Shell


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,
  };
}

Go

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{})

Petunjuk Sistem

Go

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)

Alat

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);

Isi respons

Respons dari models.countTokens.

Metode ini menampilkan tokenCount model untuk prompt.

Jika berhasil, isi respons memuat data dengan struktur berikut:

Kolom
totalTokens integer

Jumlah token yang digunakan Model untuk membuat token prompt. Selalu non-negatif.

cachedContentTokenCount integer

Jumlah token di bagian perintah yang di-cache (konten yang di-cache).

promptTokensDetails[] object (ModalityTokenCount)

Hanya output. Daftar modalitas yang diproses dalam input permintaan.

cacheTokensDetails[] object (ModalityTokenCount)

Hanya output. Daftar modalitas yang diproses dalam konten yang di-cache.

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