Counting tokens

للحصول على دليل تفصيلي حول كيفية احتساب الرموز المميزة باستخدام Gemini API، بما في ذلك كيفية احتساب الصور والمقاطع الصوتية والفيديوهات، يُرجى الاطّلاع على دليل احتساب الرموز المميزة ووصفة كتاب الطبخ المرفقة.

الطريقة: models.countTokens

تنفّذ أداة الترميز الخاصة بنموذج على الإدخال Content وتعرض عدد الرموز المميزة. راجِع دليل الرموز المميّزة لمعرفة المزيد من المعلومات حول الرموز المميّزة.

نقطة نهاية

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

مَعلمات المسار

model string

الحقل مطلوب. اسم مورد النموذج. يُستخدَم هذا المعرّف كمعرّف للنموذج.

يجب أن يتطابق هذا الاسم مع اسم طراز تم عرضه من خلال الطريقة models.list.

التنسيق: models/{model} ويكون بالتنسيق التالي: models/{model}.

نص الطلب

يتضمن نص الطلب بيانات بالبنية التالية:

الحقول
contents[] object (Content)

اختياريّ. المدخلات المقدَّمة إلى النموذج كطلب يتم تجاهل هذا الحقل عند ضبط القيمة على generateContentRequest.

generateContentRequest object (GenerateContentRequest)

اختياريّ. الإدخال العام المقدَّم إلى Model. ويشمل ذلك الطلب بالإضافة إلى معلومات أخرى لتوجيه النموذج، مثل تعليمات النظام و/أو تعريفات الدوال من أجل استدعاء الدوال. Models/Contents وgenerateContentRequests لا يمكن استخدامهما معًا. يمكنك إرسال Model + Content أو generateContentRequest، ولكن ليس كليهما.

مثال على الطلب

نص

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-3.6-flash", contents=prompt
)
print("total_tokens: ", total_tokens)
# ( e.g., total_tokens: 10 )

response = client.models.generate_content(
    model="gemini-3.6-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-3.6-flash",
  contents: prompt,
});
console.log(countTokensResponse.totalTokens);

const generateResponse = await ai.models.generateContent({
  model: "gemini-3.6-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-3.6-flash", contents, nil)
if err != nil {
	return err
}
fmt.Println("total_tokens:", countResp.TotalTokens)

response, err := client.Models.GenerateContent(ctx, "gemini-3.6-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))

محارة

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."
          }],
        }],
      }'

محادثة

Python

from google import genai
from google.genai import types

client = genai.Client()

chat = client.chats.create(
    model="gemini-3.6-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-3.6-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-3.6-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-3.6-flash",
  history: history,
});

// Count tokens for the current chat history.
const countTokensResponse = await ai.models.countTokens({
  model: "gemini-3.6-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-3.6-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-3.6-flash", nil, history)
if err != nil {
	log.Fatal(err)
}

firstTokenResp, err := client.Models.CountTokens(ctx, "gemini-3.6-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-3.6-flash", hist, nil)
if err != nil {
	log.Fatal(err)
}
fmt.Println(secondTokenResp.TotalTokens)

محارة

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"}],
        },
      ],
      }'

الوسائط المضمّنة

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-3.6-flash", contents=[prompt, your_image_file]
    )
)
# ( e.g., total_tokens: 263 )

response = client.models.generate_content(
    model="gemini-3.6-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-3.6-flash",
  contents: contents,
});
console.log(countTokensResponse.totalTokens);

const generateResponse = await ai.models.generateContent({
  model: "gemini-3.6-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 )

محارة

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

فيديو

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-3.6-flash", contents=[prompt, your_file]
    )
)
# ( e.g., total_tokens: 300 )

response = client.models.generate_content(
    model="gemini-3.6-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-3.6-flash",
  contents: createUserContent([
    prompt,
    createPartFromUri(videoFile.uri, videoFile.mimeType),
  ]),
});
console.log(countTokensResponse.totalTokens);

const generateResponse = await ai.models.generateContent({
  model: "gemini-3.6-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-3.6-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-3.6-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))

محارة


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-3.6-flash",
    contents=["Give me a summary of this document.", sample_pdf],
)
print(f"{token_count=}")

response = client.models.generate_content(
    model="gemini-3.6-flash",
    contents=["Give me a summary of this document.", sample_pdf],
)
print(response.usage_metadata)

ذاكرة التخزين المؤقت

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-3.6-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-3.6-flash", contents=prompt))
# ( e.g., total_tokens: 9 )

response = client.models.generate_content(
    model="gemini-3.6-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-3.6-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-3.6-flash",
    contents: prompt,
  });
  console.log(countTokensResponse.totalTokens);

  const generateResponse = await ai.models.generateContent({
    model: "gemini-3.6-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-3.6-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-3.6-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-3.6-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{})

تعليمات النظام

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-3.6-flash", contents, config)
if err != nil {
	log.Fatal(err)
}
printResponse(response)

الأدوات

جافا

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);

نص الاستجابة

ردّ من models.countTokens

تعرض هذه السمة tokenCount النموذج الخاص بـ prompt.

إذا كانت الاستجابة ناجحة، سيحتوي نص الاستجابة على بيانات بالبنية التالية:

الحقول
totalTokens integer

عدد الرموز المميزة التي يقسّم إليها Model النص prompt. يجب أن تكون القيمة غير سالبة دائمًا.

cachedContentTokenCount integer

عدد الرموز المميّزة في الجزء المخزّن مؤقتًا من الطلب (المحتوى المخزّن مؤقتًا)

promptTokensDetails[] object (ModalityTokenCount)

النتائج فقط. قائمة بالوسائط التي تمت معالجتها في بيانات الإدخال الخاصة بالطلب

cacheTokensDetails[] object (ModalityTokenCount)

النتائج فقط. قائمة بالوسائط التي تمت معالجتها في المحتوى المخزَّن مؤقتًا

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