Starting November 2, 2026, Firebase App Check enforcement will be required to use Firebase AI Logic. Learn more.

Model updates:

Gemini 4 Carbon and Gemini 4 Argon are now available to use with Firebase AI Logic! Learn more.
Gemini 2.5 models are deprecated or shut down. For the Agent Platform Gemini API, all general-use Gemini 2.5 models have shut down. For the Gemini Developer API, starting December 1, 2026, requests to the general-use Gemini 2.5 Flash and Pro models will be automatically routed to newer models (which may cause breaking changes; learn more). To avoid service disruptions, update to a newer model (like gemini-4-argon, gemini-3.8-flash, gemini-3.5-flash-lite, or gemini-nano-banana-2.1). Learn more.

Google 會運用 AI 技術將內容翻譯成你偏好的語言，但可能會出錯。

執行程式碼

執行程式碼工具可讓模型生成及執行 Python 程式碼。模型可根據執行程式碼結果反覆試驗學習，直到生成最終輸出內容。

您可以運用程式碼執行功能，建構根據程式碼進行推論、生成文字輸出內容的功能。舉例來說，您可以使用程式碼執行功能，解開方程式或處理文字。您也可以使用程式碼執行環境中的程式庫，執行更專業的工作。

就像您提供給模型的所有工具一樣，模型會決定何時使用執行程式碼功能。

跳到程式碼導入

執行程式碼與函式呼叫的比較

執行程式碼和呼叫函式是類似的功能。一般來說，如果模型可以處理您的用途，建議您使用執行程式碼功能。執行程式碼也更簡單，只要啟用即可。

以下是執行程式碼和呼叫函式的其他差異：

執行程式碼	函式呼叫
如要讓模型編寫及執行 Python 程式碼並傳回結果，請使用執行程式碼功能。	如果您已有要在本機執行的函式，請使用函式呼叫功能。
執行程式碼功能可讓模型在 API 後端，以固定且隔離的環境執行程式碼。	函式呼叫功能可讓您在任何環境中，執行模型要求的函式。
執行程式碼功能會在單一要求中完成。雖然您可以選擇搭配使用執行程式碼和即時通訊功能，但這並非必要。	呼叫函式時，需要額外傳送要求，才能傳回各個函式呼叫的輸出內容。因此，您必須使用對話功能。

支援的模型

gemini-4-carbon
gemini-4-argon
gemini-3.8-flash
gemini-3.5-flash-lite (以及舊版 gemini-3.1-flash-lite)

舊版一般用途 Gemini 3.x Flash 模型支援這項功能，但由於即將停用和/或自動轉送至新版模型，因此不建議使用。

使用執行程式碼功能

您可以使用執行程式碼功能，同時輸入純文字和多模態內容，但回覆一律只會是文字或程式碼。

事前準備

按一下您的Gemini API供應商，即可在這個頁面查看供應商專屬內容和程式碼。

如果尚未完成入門指南，請先完成。指南說明如何設定 Firebase 專案、將應用程式連結至 Firebase、新增 SDK、為所選 Gemini API 供應商初始化後端服務，以及建立 GenerativeModel 執行個體。

如要測試及反覆調整提示，建議使用 Google AI Studio。

啟用執行程式碼功能

嘗試這個範例前，請先完成本指南的「事前準備」部分，設定專案和應用程式。
在該部分中，您也需要點選所選Gemini API供應商的按鈕，這樣這個頁面才會顯示供應商專屬內容。

建立 GenerativeModel 執行個體時，請提供 CodeExecution 做為模型可用來生成回覆的工具。模型就能生成及執行 Python 程式碼。

Swift


import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
let ai = FirebaseAI.firebaseAI(backend: .googleAI())

// Create a `GenerativeModel` instance with a model that supports your use case
let model = ai.generativeModel(
  modelName: "GEMINI_MODEL_NAME",
  // Provide code execution as a tool that the model can use to generate its response.
  tools: [.codeExecution()]
)

let prompt = """
What is the sum of the first 50 prime numbers?
Generate and run code for the calculation, and make sure you get all 50.
"""

let response = try await model.generateContent(prompt)

guard let candidate = response.candidates.first else {
  print("No candidates in response.")
  return
}
for part in candidate.content.parts {
  if let textPart = part as? TextPart {
    print("Text = \(textPart.text)")
  } else if let executableCode = part as? ExecutableCodePart {
    print("Code = \(executableCode.code), Language = \(executableCode.language)")
  } else if let executionResult = part as? CodeExecutionResultPart {
    print("Outcome = \(executionResult.outcome), Result = \(executionResult.output ?? "no output")")
  }
}

Kotlin


// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
    modelName = "GEMINI_MODEL_NAME",
    // Provide code execution as a tool that the model can use to generate its response.
    tools = listOf(Tool.codeExecution())
)

val prompt =  "What is the sum of the first 50 prime numbers? " +
        "Generate and run code for the calculation, and make sure you get all 50."

val response = model.generateContent(prompt)

response.candidates.first().content.parts.forEach {
    if(it is TextPart) {
        println("Text = ${it.text}")
    }
    if(it is ExecutableCodePart) {
        println("Code = ${it.code}, Language = ${it.language}")
    }
    if(it is CodeExecutionResultPart) {
       println("Outcome = ${it.outcome}, Result = ${it.output}")
    }
}

Java


// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())
                .generativeModel("GEMINI_MODEL_NAME",
                        null,
                        null,
                        // Provide code execution as a tool that the model can use to generate its response.
                        List.of(Tool.codeExecution()));

// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures model = GenerativeModelFutures.from(ai);

String text = "What is the sum of the first 50 prime numbers? " +
        "Generate and run code for the calculation, and make sure you get all 50.";

Content prompt = new Content.Builder()
    .addText(text)
    .build();

ListenableFuture response = model.generateContent(prompt);

Futures.addCallback(response, new FutureCallback() {
   @Override
public void onSuccess(GenerateContentResponse response)   {
    // Access the first candidate's content parts
    List parts = response.getCandidates().get(0).getContent().getParts();
    for (Part part : parts) {
        if (part instanceof TextPart) {
            TextPart textPart = (TextPart) part;
            System.out.println("Text = " + textPart.getText());
        } else if (part instanceof ExecutableCodePart) {
            ExecutableCodePart codePart = (ExecutableCodePart) part;
            System.out.println("Code = " + codePart.getCode() + ", Language = " + codePart.getLanguage());
        } else if (part instanceof CodeExecutionResultPart) {
            CodeExecutionResultPart resultPart = (CodeExecutionResultPart) part;
            System.out.println("Outcome = " + resultPart.getOutcome() + ", Result = " + resultPart.getOutput());
        }
    }
}

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

Web


import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai";

// TODO(developer): Replace the following with your app's Firebase configuration
// See: https://firebase.google.com/docs/web/learn-more#config-object
const firebaseConfig = {
  // ...
};

// Initialize FirebaseApp
const firebaseApp = initializeApp(firebaseConfig);

// Initialize the Gemini Developer API backend service.
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Create a `GenerativeModel` instance with a model that supports your use case.
const model = getGenerativeModel(
  ai,
  {
    model: "GEMINI_MODEL_NAME",
    // Provide code execution as a tool that the model can use to generate its response.
    tools: [{ codeExecution: {} }]
  }
);

const prompt =  "What is the sum of the first 50 prime numbers? " +
        "Generate and run code for the calculation, and make sure you get all 50."

const result = await model.generateContent(prompt);
const response = await result.response;

const parts = response.candidates?.[0].content.parts;

if (parts) {
  parts.forEach((part) => {
    if (part.type === "text") {
      console.log(`Text: ${part.text}`);
    } else if (part.type === "executableCode") {
      console.log(
        `Code: ${part.executableCode.code}, Language: ${part.executableCode.language}`
      );
    } else if (part.type === "codeExecutionResult") {
      console.log(
        `Outcome: ${part.codeExecutionResult.outcome}, Result: ${part.codeExecutionResult.output}`
      );
    }
  });
}

Dart


import 'package:firebase_core/firebase_core.dart';
import 'package:firebase_ai/firebase_ai.dart';
import 'firebase_options.dart';

// Initialize FirebaseApp
await Firebase.initializeApp(
  options: DefaultFirebaseOptions.currentPlatform,
);

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
final model = FirebaseAI.googleAI().generativeModel(
  model: 'GEMINI_MODEL_NAME',
  // Provide code execution as a tool that the model can use to generate its response.
  tools: [
    Tool.codeExecution(),
  ],
);

const prompt = 'What is the sum of the first 50 prime numbers? '
    'Generate and run code for the calculation, and make sure you get all 50.';

final response = await model.generateContent([Content.text(prompt)]);

final buffer = StringBuffer();
    for (final part in response.candidates.first.content.parts) {
      if (part is TextPart) {
        buffer.writeln(part.text);
      } else if (part is ExecutableCodePart) {
        buffer.writeln('Executable Code:');
        buffer.writeln('Language: ${part.language}');
        buffer.writeln('Code:');
        buffer.writeln(part.code);
      } else if (part is CodeExecutionResultPart) {
        buffer.writeln('Code Execution Result:');
        buffer.writeln('Outcome: ${part.outcome}');
        buffer.writeln('Output:');
        buffer.writeln(part.output);
      }
    }

Unity


using Firebase;
using Firebase.AI;

// Initialize the Gemini Developer API backend service.
var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());

// Create a `GenerativeModel` instance with a model that supports your use case.
var model = ai.GetGenerativeModel(
  modelName: "GEMINI_MODEL_NAME",
  // Provide code execution as a tool that the model can use to generate its response.
  tools: new Tool[] { new Tool(new CodeExecution()) }
);

var prompt = "What is the sum of the first 50 prime numbers? " +
        "Generate and run code for the calculation, and make sure you get all 50.";

var response = await model.GenerateContentAsync(prompt);

foreach (var part in response.Candidates.First().Content.Parts) {
  if (part is ModelContent.TextPart tp) {
    UnityEngine.Debug.Log($"Text = {tp.Text}");
  } else if (part is ModelContent.ExecutableCodePart esp) {
    UnityEngine.Debug.Log($"Code = {esp.Code}, Language = {esp.Language}");
  } else if (part is ModelContent.CodeExecutionResultPart cerp) {
    UnityEngine.Debug.Log($"Outcome = {cerp.Outcome}, Output = {cerp.Output}");
  }
}

瞭解如何根據用途和應用程式，選擇合適的模型。

查看輸出內容示例

輸出內容可能如下所示 (已調整格式以利閱讀)：

Okay, I need to calculate the sum of the first 50 prime numbers. Here's how I'll
approach this:

1.  **Generate Prime Numbers:** I'll use an iterative method to find prime
    numbers. I'll start with 2 and check if each subsequent number is divisible
    by any number between 2 and its square root. If not, it's a prime.
2.  **Store Primes:** I'll store the prime numbers in a list until I have 50 of
    them.
3.  **Calculate the Sum:**  Finally, I'll sum the prime numbers in the list.

Here's the Python code to do this:

def is_prime(n):
  """Efficiently checks if a number is prime."""
  if n <= 1:
    return False
  if n <= 3:
    return True
  if n % 2 == 0 or n % 3 == 0:
    return False
  i = 5
  while i * i <= n:
    if n % i == 0 or n % (i + 2) == 0:
      return False
    i += 6
  return True

primes = []
num = 2
while len(primes) < 50:
  if is_prime(num):
    primes.append(num)
  num += 1

sum_of_primes = sum(primes)
print(f'{primes=}')
print(f'{sum_of_primes=}')

primes=[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67,
71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151,
157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229]
sum_of_primes=5117

The sum of the first 50 prime numbers is 5117.

這項輸出內容結合了模型在使用執行程式碼功能時傳回的多個內容部分：

text：模型生成的內嵌文字
executableCode：模型生成、要執行的程式碼
codeExecutionResult：執行程式碼的結果

這些部分的命名慣例會因程式設計語言而異。

在對話中使用執行程式碼功能

您也可以在對話中使用執行程式碼功能：

Swift


import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
let ai = FirebaseAI.firebaseAI(backend: .googleAI())

// Create a `GenerativeModel` instance with a model that supports your use case
let model = ai.generativeModel(
  modelName: "GEMINI_MODEL_NAME",
  // Provide code execution as a tool that the model can use to generate its response.
  tools: [.codeExecution()]
)

let prompt = """
What is the sum of the first 50 prime numbers?
Generate and run code for the calculation, and make sure you get all 50.
"""
let chat = model.startChat()

let response = try await chat.sendMessage(prompt)

guard let candidate = response.candidates.first else {
  print("No candidates in response.")
  return
}
for part in candidate.content.parts {
  if let textPart = part as? TextPart {
    print("Text = \(textPart.text)")
  } else if let executableCode = part as? ExecutableCodePart {
    print("Code = \(executableCode.code), Language = \(executableCode.language)")
  } else if let executionResult = part as? CodeExecutionResultPart {
    print("Outcome = \(executionResult.outcome), Result = \(executionResult.output ?? "no output")")
  }
}

Kotlin


// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
    modelName = "GEMINI_MODEL_NAME",
    // Provide code execution as a tool that the model can use to generate its response.
    tools = listOf(Tool.codeExecution())
)

val prompt =  "What is the sum of the first 50 prime numbers? " +
        "Generate and run code for the calculation, and make sure you get all 50."
val chat = model.startChat()
val response = chat.sendMessage(prompt)

response.candidates.first().content.parts.forEach {
    if(it is TextPart) {
        println("Text = ${it.text}")
    }
    if(it is ExecutableCodePart) {
        println("Code = ${it.code}, Language = ${it.language}")
    }
    if(it is CodeExecutionResultPart) {
       println("Outcome = ${it.outcome}, Result = ${it.output}")
    }
}

Java


// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())
                .generativeModel("GEMINI_MODEL_NAME",
                        null,
                        null,
                        // Provide code execution as a tool that the model can use to generate its response.
                        List.of(Tool.codeExecution()));

// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures model = GenerativeModelFutures.from(ai);
String text = "What is the sum of the first 50 prime numbers? " +
        "Generate and run code for the calculation, and make sure you get all 50.";

Content prompt = new Content.Builder()
    .addText(text)
    .build();

ChatFutures chat = model.startChat();
ListenableFuture response = chat.sendMessage(prompt);

Futures.addCallback(response, new FutureCallback() {
   @Override
public void onSuccess(GenerateContentResponse response)   {
    // Access the first candidate's content parts
    List parts = response.getCandidates().get(0).getContent().getParts();
    for (Part part : parts) {
        if (part instanceof TextPart) {
            TextPart textPart = (TextPart) part;
            System.out.println("Text = " + textPart.getText());
        } else if (part instanceof ExecutableCodePart) {
            ExecutableCodePart codePart = (ExecutableCodePart) part;
            System.out.println("Code = " + codePart.getCode() + ", Language = " + codePart.getLanguage());
        } else if (part instanceof CodeExecutionResultPart) {
            CodeExecutionResultPart resultPart = (CodeExecutionResultPart) part;
            System.out.println("Outcome = " + resultPart.getOutcome() + ", Result = " + resultPart.getOutput());
        }
    }
}

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

Web


import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai";

// TODO(developer): Replace the following with your app's Firebase configuration
// See: https://firebase.google.com/docs/web/learn-more#config-object
const firebaseConfig = {
  // ...
};

// Initialize FirebaseApp
const firebaseApp = initializeApp(firebaseConfig);

// Initialize the Gemini Developer API backend service.
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Create a `GenerativeModel` instance with a model that supports your use case.
const model = getGenerativeModel(
  ai,
  {
    model: "GEMINI_MODEL_NAME",
    // Provide code execution as a tool that the model can use to generate its response.
    tools: [{ codeExecution: {} }]
  }
);

const prompt =  "What is the sum of the first 50 prime numbers? " +
        "Generate and run code for the calculation, and make sure you get all 50."
const chat = model.startChat()
const result = await chat.sendMessage(prompt);

const parts = result.response.candidates?.[0].content.parts;

if (parts) {
  parts.forEach((part) => {
    if (part.type === "text") {
      console.log(`Text: ${part.text}`);
    } else if (part.type === "executableCode") {
      console.log(
        `Code: ${part.executableCode.code}, Language: ${part.executableCode.language}`
      );
    } else if (part.type === "codeExecutionResult") {
      console.log(
        `Outcome: ${part.codeExecutionResult.outcome}, Result: ${part.codeExecutionResult.output}`
      );
    }
  });
}

Dart


import 'package:firebase_core/firebase_core.dart';
import 'package:firebase_ai/firebase_ai.dart';
import 'firebase_options.dart';

// Initialize FirebaseApp
await Firebase.initializeApp(
  options: DefaultFirebaseOptions.currentPlatform,
);

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
final model = FirebaseAI.googleAI().generativeModel(
  model: 'GEMINI_MODEL_NAME',
  // Provide code execution as a tool that the model can use to generate its response.
  tools: [
    Tool.codeExecution(),
  ],
);

final codeExecutionChat = await model.startChat();

const prompt = 'What is the sum of the first 50 prime numbers? '
    'Generate and run code for the calculation, and make sure you get all 50.';
final response = await codeExecutionChat.sendMessage(Content.text(prompt));

final buffer = StringBuffer();
    for (final part in response.candidates.first.content.parts) {
      if (part is TextPart) {
        buffer.writeln(part.text);
      } else if (part is ExecutableCodePart) {
        buffer.writeln('Executable Code:');
        buffer.writeln('Language: ${part.language}');
        buffer.writeln('Code:');
        buffer.writeln(part.code);
      } else if (part is CodeExecutionResultPart) {
        buffer.writeln('Code Execution Result:');
        buffer.writeln('Outcome: ${part.outcome}');
        buffer.writeln('Output:');
        buffer.writeln(part.output);
      }
    }

Unity


using Firebase;
using Firebase.AI;

// Initialize the Gemini Developer API backend service.
var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());

// Create a `GenerativeModel` instance with a model that supports your use case.
var model = ai.GetGenerativeModel(
  modelName: "GEMINI_MODEL_NAME",
  // Provide code execution as a tool that the model can use to generate its response.
  tools: new Tool[] { new Tool(new CodeExecution()) }
);

var prompt = "What is the sum of the first 50 prime numbers? " +
        "Generate and run code for the calculation, and make sure you get all 50.";
var chat = model.StartChat();
var response = await chat.SendMessageAsync(prompt);

foreach (var part in response.Candidates.First().Content.Parts) {
  if (part is ModelContent.TextPart tp) {
    UnityEngine.Debug.Log($"Text = {tp.Text}");
  } else if (part is ModelContent.ExecutableCodePart esp) {
    UnityEngine.Debug.Log($"Code = {esp.Code}, Language = {esp.Language}");
  } else if (part is ModelContent.CodeExecutionResultPart cerp) {
    UnityEngine.Debug.Log($"Outcome = {cerp.Outcome}, Output = {cerp.Output}");
  }
}

瞭解如何根據用途和應用程式，選擇合適的模型。

定價

啟用執行程式碼功能，並將其做為模型工具，不會產生額外費用。如果模型決定使用執行程式碼功能，則會根據您使用的 Gemini 模型，按照目前的輸入和輸出權杖費率計費。

下圖顯示程式碼執行的計費模式：

圖表：模型執行程式碼時，系統如何計算權杖費用。

以下摘要說明模型使用執行程式碼功能時，詞元的計費方式：

原始提示會計費一次。其詞元會標示為中間詞元，並以輸入詞元計費。
生成的程式碼和執行結果的計費方式如下：
- 在執行程式碼時，這些詞元會標示為中間詞元，並以輸入詞元計費。
- 如果包含在最終回覆中，則會以輸出符記計費。
最終回覆中的最終摘要則會以輸出詞元計費。

Gemini API 會在 API 回應中提供中間詞元數量，方便您瞭解為何除了初始提示詞，系統還會針對輸入詞元收費。

請注意，生成的程式碼可能包含文字和多模態輸出內容，例如圖片。

限制與最佳做法

模型只能生成及執行 Python 程式碼。無法傳回媒體檔案等其他構件。
執行程式碼時間上限為 30 秒，超過即會逾時。
在某些情況下，啟用執行程式碼功能可能會導致模型輸出內容的其他部分 (例如撰寫故事) 出現迴歸。
執行程式碼工具不支援檔案 URI 做為輸入/輸出內容。不過，程式碼執行工具支援檔案輸入和圖表輸出，並以內嵌位元組的形式呈現。透過這些輸入和輸出功能，您可以上傳 CSV 和文字檔案、詢問檔案相關問題，並在執行程式碼時生成 Matplotlib 圖表。內嵌位元組支援的 MIME 類型包括 .cpp、.csv、.java、.jpeg、.js、.png、.py、.ts 和 .xml。

支援的程式庫

執行程式碼環境包含下列程式庫。您無法安裝自己的程式庫。

查看 Gemini Developer API 支援的程式庫

attrs
棋子
contourpy
fpdf
geopandas
imageio
jinja2
joblib
jsonschema
jsonschema-specifications
lxml
matplotlib
mpmath
numpy
opencv-python
openpyxl
包裝
pandas
pillow
protobuf
pylatex
pyparsing
PyPDF2
python-dateutil
python-docx
python-pptx
reportlab
scikit-learn
scipy
seaborn
six
striprtf
sympy
tabulate
TensorFlow
toolz
xlrd

查看 Agent Platform Gemini API (formerly Vertex AI) 支援的程式庫

提供「Firebase AI Logic」使用體驗的意見回饋

執行程式碼 透過集合功能整理內容 你可以依據偏好儲存及分類內容。

執行程式碼與函式呼叫的比較

支援的模型

使用執行程式碼功能

事前準備

啟用執行程式碼功能

Swift

Kotlin

Java

Web

Dart

Unity

在對話中使用執行程式碼功能

Swift

Kotlin

Java

Web

Dart

Unity

定價

限制與最佳做法

支援的程式庫

執行程式碼