Gemini 3 Pro & Flash, Gemini 3 Pro Image (nano banana pro), and the latest Gemini Live API native audio models are now available to use with Firebase AI Logic on all platforms!

Gemini 2.0 Flash and Flash-Lite models will be retired on March 3, 2026. To avoid service disruption, update to a newer model like gemini-2.5-flash-lite. Learn more.

Thinking

Gemini 3 and Gemini 2.5 models can use an internal "thinking process" that significantly improves their reasoning and multi-step planning abilities, making them highly effective for complex tasks such as coding, advanced mathematics, and data analysis.

Thinking models offer the following configurations and options:

Control the amount of thinking
You can configure how much "thinking" that a model can do. This configuration is particularly important if reducing latency or cost is a priority. Also, review the comparison of task difficulties to decide how much a model might need its thinking capability.

Control this configuration either with thinking levels (Gemini 3 and later models) or with thinking budgets (Gemini 2.5 models).
Get thought summaries
You can enable thought summaries to include with the generated response. These summaries are synthesized versions of the model's raw thoughts and offer insights into the model's internal reasoning process.
Handle thought signatures
The Firebase AI Logic SDKs automatically handle thought signatures for you, which ensures that the model has access to the thought context from previous turns specifically when using function calling.

Make sure to review the best practices and prompting guidance for using thinking models.

Use a thinking model

Use a thinking model just like you'd use any other Gemini model.

To get the most out of thinking models, review Best practices & prompting guidance for using thinking models later on this page.

Models that support this capability

Only Gemini 3 and Gemini 2.5 models support this capability.

gemini-3-pro-preview
gemini-3-pro-image-preview (aka "nano banana pro")
gemini-3-flash-preview
gemini-2.5-pro
gemini-2.5-flash
gemini-2.5-flash-lite

Best practices & prompting guidance for using thinking models

We recommend testing your prompt in Google AI Studio or Vertex AI Studio where you can view the full thinking process. You can identify any areas where the model may have gone astray so that you can refine your prompts to get more consistent and accurate responses.

Begin with a general prompt that describes the desired outcome, and observe the model's initial thoughts on how it determines its response. If the response isn't as expected, help the model generate a better response by using any of the following prompting techniques:

Provide step-by-step instructions
Provide several examples of input-output pairs
Provide guidance for how the output and responses should be phrased and be formatted
Provide specific verification steps

In addition to prompting, consider using these recommendations:

Set system instructions, which are like a "preamble" that you add before the model gets exposed to any further instructions from the prompt or end user. They let you steer the behavior of the model based on your specific needs and use cases.
Set a thinking level (or thinking budget for Gemini 2.5 models) to control how much thinking the model can do. If you set it high, then the model can think more, if needed. If you set it lower, then the model won't "overthink" its response, and it also reserves more of the total token output limit for the actual response and can help reduce latency and cost.
Enable AI monitoring in the Firebase console to monitor the count of thinking tokens and the latency of your requests that have thinking enabled. And if you have thought summaries enabled, they will display in the console where you can inspect the model's detailed reasoning to help you debug and refine your prompts.

Control the amount of thinking

You can configure how much "thinking" and reasoning that a model can do before it returns a response. This configuration is particularly important if reducing latency or cost is a priority.

Make sure to review the comparison of task difficulties to decide how much a model might need its thinking capability. Here's some high-level guidance:

Set a lower thinking value for less complex tasks or if reducing latency or cost is a priority for you.
Set a higher thinking value for more complex tasks.

Control this configuration either with thinking levels (Gemini 3 and later models) or with thinking budgets (Gemini 2.5 models).

Thinking levels (Gemini 3 models)

To control how much thinking a Gemini 3 model can do to generate its response, you can specify a thinking level for the amount of thinking tokens that it's allowed to use.

Set the thinking level

Click your Gemini API provider to view provider-specific content and code on this page.

Set the thinking level in a GenerationConfig as part of creating the GenerativeModel instance for a Gemini 3 model. The configuration is maintained for the lifetime of the instance. If you want to use different thinking levels for different requests, then create GenerativeModel instances configured with each level.

Learn about supported values for thinking level later in this section.

Swift

Set the thinking level in a GenerationConfig as part of creating a GenerativeModel instance.


// ...

// Set the thinking configuration
// Use a thinking level value appropriate for your model (example value shown here)
let generationConfig = GenerationConfig(
  thinkingConfig: ThinkingConfig(thinkingLevel: .low)
)

// Specify the config as part of creating the `GenerativeModel` instance
let model = FirebaseAI.firebaseAI(backend: .googleAI()).generativeModel(
  modelName: "GEMINI_3_MODEL_NAME",
  generationConfig: generationConfig
)

// ...

Kotlin

Set the values of the parameters in a GenerationConfig as part of creating a GenerativeModel instance.


// ...

// Set the thinking configuration
// Use a thinking level value appropriate for your model (example value shown here)
val generationConfig = generationConfig {
  thinkingConfig = thinkingConfig {
      thinkingLevel = ThinkingLevel.LOW
  }
}

// Specify the config as part of creating the `GenerativeModel` instance
val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
  modelName = "GEMINI_3_MODEL_NAME",
  generationConfig,
)

// ...

Java

Set the values of the parameters in a GenerationConfig as part of creating a GenerativeModel instance.


// ...

// Set the thinking configuration
// Use a thinking level value appropriate for your model (example value shown here)
ThinkingConfig thinkingConfig = new ThinkingConfig.Builder()
    .setThinkingLevel(ThinkingLevel.LOW)
    .build();

GenerationConfig generationConfig = GenerationConfig.builder()
    .setThinkingConfig(thinkingConfig)
    .build();

// Specify the config as part of creating the `GenerativeModel` instance
GenerativeModelFutures model = GenerativeModelFutures.from(
        FirebaseAI.getInstance(GenerativeBackend.googleAI())
                .generativeModel(
                  /* modelName */ "GEMINI_3_MODEL_NAME",
                  /* generationConfig */ generationConfig
                );
);

// ...

Web

Set the values of the parameters in a GenerationConfig as part of creating a GenerativeModel instance.


// ...

const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Set the thinking configuration
// Use a thinking level value appropriate for your model (example value shown here)
const generationConfig = {
  thinkingConfig: {
    thinkingLevel: ThinkingLevel.LOW
  }
};

// Specify the config as part of creating the `GenerativeModel` instance
const model = getGenerativeModel(ai, { model: "GEMINI_3_MODEL_NAME", generationConfig });

// ...

Dart

Set the values of the parameters in a GenerationConfig as part of creating a GenerativeModel instance.


// ...

// Set the thinking configuration
// Use a thinking level value appropriate for your model (example value shown here)
final thinkingConfig = ThinkingConfig.withThinkingLevel(ThinkingLevel.low);

final generationConfig = GenerationConfig(
  thinkingConfig: thinkingConfig
);

// Specify the config as part of creating the `GenerativeModel` instance
final model = FirebaseAI.googleAI().generativeModel(
  model: 'GEMINI_3_MODEL_NAME',
  config: generationConfig,
);

// ...

Unity

Set the values of the parameters in a GenerationConfig as part of creating a GenerativeModel instance.


// ...

// Set the thinking configuration
// Use a thinking level value appropriate for your model (example value shown here)
var thinkingConfig = new ThinkingConfig(thinkingLevel: ThinkingLevel.Low);

var generationConfig = new GenerationConfig(
  thinkingConfig: thinkingConfig
);

// Specify the config as part of creating the `GenerativeModel` instance
var model = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI()).GetGenerativeModel(
  modelName: "GEMINI_3_MODEL_NAME",
  generationConfig: generationConfig
);

// ...

Supported thinking level values

The following table lists the thinking level values that you can set for each model by configuring the model's thinkingLevel.

	`MINIMAL`	`LOW`	`MEDIUM`	`HIGH`
	Model uses as few tokens as possible; close to no thinking Low-complexity tasks	Model uses fewer tokens; minimizes latency & cost Simple tasks and high-throughput tasks	Model uses a balanced approach Moderate complexity tasks	Model uses tokens up to its maximum level Complex prompts that require deep reasoning
Gemini 3 Pro				(default)
Gemini 3 Pro Image ("nano banana pro")				(default)
Gemini 3 Flash				(default)

Thinking budgets (Gemini 2.5 models)

To control how much thinking a Gemini 2.5 model can do to generate its response, you can specify a thinking budget for the amount of thinking tokens that it's allowed to use.

Set the thinking budget

Click your Gemini API provider to view provider-specific content and code on this page.

Set the thinking budget in a GenerationConfig as part of creating the GenerativeModel instance for a Gemini 2.5 model. The configuration is maintained for the lifetime of the instance. If you want to use different thinking budgets for different requests, then create GenerativeModel instances configured with each budget.

Learn about supported values for thinking budget later in this section.

Swift

Set the thinking budget in a GenerationConfig as part of creating a GenerativeModel instance.


// ...

// Set the thinking configuration
// Use a thinking budget value appropriate for your model (example value shown here)
let generationConfig = GenerationConfig(
  thinkingConfig: ThinkingConfig(thinkingBudget: 1024)
)

// Specify the config as part of creating the `GenerativeModel` instance
let model = FirebaseAI.firebaseAI(backend: .googleAI()).generativeModel(
  modelName: "GEMINI_2.5_MODEL_NAME",
  generationConfig: generationConfig
)

// ...

Kotlin

Set the values of the parameters in a GenerationConfig as part of creating a GenerativeModel instance.


// ...

// Set the thinking configuration
// Use a thinking budget value appropriate for your model (example value shown here)
val generationConfig = generationConfig {
  thinkingConfig = thinkingConfig {
      thinkingBudget = 1024
  }
}

// Specify the config as part of creating the `GenerativeModel` instance
val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
  modelName = "GEMINI_2.5_MODEL_NAME",
  generationConfig,
)

// ...

Java

Set the values of the parameters in a GenerationConfig as part of creating a GenerativeModel instance.


// ...

// Set the thinking configuration
// Use a thinking budget value appropriate for your model (example value shown here)
ThinkingConfig thinkingConfig = new ThinkingConfig.Builder()
    .setThinkingBudget(1024)
    .build();

GenerationConfig generationConfig = GenerationConfig.builder()
    .setThinkingConfig(thinkingConfig)
    .build();

// Specify the config as part of creating the `GenerativeModel` instance
GenerativeModelFutures model = GenerativeModelFutures.from(
        FirebaseAI.getInstance(GenerativeBackend.googleAI())
                .generativeModel(
                  /* modelName */ "GEMINI_2.5_MODEL_NAME",
                  /* generationConfig */ generationConfig
                );
);

// ...

Web

Set the values of the parameters in a GenerationConfig as part of creating a GenerativeModel instance.


// ...

const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Set the thinking configuration
// Use a thinking budget value appropriate for your model (example value shown here)
const generationConfig = {
  thinkingConfig: {
    thinkingBudget: 1024
  }
};

// Specify the config as part of creating the `GenerativeModel` instance
const model = getGenerativeModel(ai, { model: "GEMINI_2.5_MODEL_NAME", generationConfig });

// ...

Dart

Set the values of the parameters in a GenerationConfig as part of creating a GenerativeModel instance.


// ...

// Set the thinking configuration
// Use a thinking budget value appropriate for your model (example value shown here)
final thinkingConfig = ThinkingConfig.withThinkingBudget(1024);

final generationConfig = GenerationConfig(
  thinkingConfig: thinkingConfig
);

// Specify the config as part of creating the `GenerativeModel` instance
final model = FirebaseAI.googleAI().generativeModel(
  model: 'GEMINI_2.5_MODEL_NAME',
  config: generationConfig,
);

// ...

Unity

Set the values of the parameters in a GenerationConfig as part of creating a GenerativeModel instance.


// ...

// Set the thinking configuration
// Use a thinking budget value appropriate for your model (example value shown here)
var thinkingConfig = new ThinkingConfig(thinkingBudget: 1024);

var generationConfig = new GenerationConfig(
  thinkingConfig: thinkingConfig
);

// Specify the config as part of creating the `GenerativeModel` instance
var model = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI()).GetGenerativeModel(
  modelName: "GEMINI_2.5_MODEL_NAME",
  generationConfig: generationConfig
);

// ...

Supported thinking budget values

The following table lists the thinking budget values that you can set for each model by configuring the model's thinkingBudget.

Model	Default value	Available range for thinking budget		Value to disable thinking	Value to enable dynamic thinking
Model	Default value			Value to disable thinking	Value to enable dynamic thinking	Minimum value	Maximum value
Gemini 2.5 Pro	`8,192`	`128`	`32,768`	cannot be disabled	`-1`
Gemini 2.5 Flash	`8,192`	`1`	`24,576`	`0`	`-1`
Gemini 2.5 Flash‑Lite	`0` (thinking is disabled by default)	`512`	`24,576`	`0` (or don't configure thinking budget at all)	`-1`

Disable thinking for Gemini 2.5 models

For some easier tasks, the thinking capability isn't as necessary, and traditional inference is sufficient. Also, if reducing latency or cost is a priority, you may not want the model to take any more time or cost more than necessary to generate a response.

In these situations, you can disable (or turn off) thinking for some models:

Gemini 2.5 Pro: thinking cannot be disabled
Gemini 2.5 Flash: disable thinking by setting thinkingBudget to 0 tokens
Gemini 2.5 Flash‑Lite: thinking is disabled by default (so don't set thinkingBudget explicitly or just set it to 0)

Note that for all Gemini 3 models, thinking cannot be disabled.

Enable dynamic thinking for Gemini 2.5 models

With dynamic thinking, the model decides when and how much it thinks (up to a max thinking budget, as described below).

Enable dynamic thinking by setting the thinkingBudget to -1.
When dynamic thinking is enabled, the max thinking tokens will always be 8,192 tokens.

Note that all Gemini 3 models always use dynamic thinking.

Task complexity for all thinking models

Easy tasks — thinking isn't as necessary
Straightforward requests where complex reasoning isn't required, such as fact retrieval or classification. Examples:
- "Where was DeepMind founded?"
- "Is this email asking for a meeting or just providing information?"
Moderate tasks — some thinking is likely necessary
Common requests that benefit from a degree of step-by-step processing or deeper understanding. Examples:
- "Create an analogy between photosynthesis and growing up."
- "Compare and contrast electric cars and hybrid cars."
Hard tasks — maximum thinking may be necessary
Truly complex challenges, such as solving complex math problems or coding tasks. These types of tasks require the model to engage its full reasoning and planning capabilities, often involving many internal steps before providing an answer. Examples:
- "Solve problem 1 in AIME 2025: Find the sum of all integer bases b > 9 for which 17b is a divisor of 97b."
- "Write Python code for a web application that visualizes real-time stock market data, including user authentication. Make it as efficient as possible."

Thought summaries

Thought summaries are synthesized versions of the model's raw thoughts and offer insights into the model's internal reasoning process.

Here are some reasons to include thought summaries in responses:

You can display the thought summary in your app's UI or make them accessible to your users. The thought summary is returned as a separate part in the response so that you have more control over how it's used in your app.
If you also enable AI monitoring in the Firebase console, then thought summaries display in the console where you can inspect the model's detailed reasoning to help you debug and refine your prompts.

Here are some key notes about thought summaries:

Thought summaries are not controlled by thinking budgets (budgets only apply to the model's raw thoughts). However, if thinking is disabled, then the model won't return a thought summary.
Thought summaries are considered part of the model's regular generated-text response and count as output tokens.

Enable thought summaries

Click your Gemini API provider to view provider-specific content and code on this page.

You can enable thought summaries by setting includeThoughts to true in your model configuration. You can then access the summary by checking the thoughtSummary field from the response.

Here's an example demonstrating how to enable and retrieve thought summaries with the response:

Swift

Enable thought summaries in the GenerationConfig as part of creating a GenerativeModel instance.


// ...

// Set the thinking configuration
// Optionally enable thought summaries in the generated response (default is false)
let generationConfig = GenerationConfig(
  thinkingConfig: ThinkingConfig(includeThoughts: true)
)

// Specify the config as part of creating the `GenerativeModel` instance
let model = FirebaseAI.firebaseAI(backend: .googleAI()).generativeModel(
  modelName: "GEMINI_MODEL_NAME",
  generationConfig: generationConfig
)

let response = try await model.generateContent("solve x^2 + 4x + 4 = 0")

// Handle the response that includes thought summaries
if let thoughtSummary = response.thoughtSummary {
  print("Thought Summary: \(thoughtSummary)")
}
guard let text = response.text else {
  fatalError("No text in response.")
}
print("Answer: \(text)")

Kotlin

Enable thought summaries in the GenerationConfig as part of creating a GenerativeModel instance.


// ...

// Set the thinking configuration
// Optionally enable thought summaries in the generated response (default is false)
val generationConfig = generationConfig {
  thinkingConfig = thinkingConfig {
      includeThoughts = true
  }
}

// Specify the config as part of creating the `GenerativeModel` instance
val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
  modelName = "GEMINI_MODEL_NAME",
  generationConfig,
)

val response = model.generateContent("solve x^2 + 4x + 4 = 0")

// Handle the response that includes thought summaries
response.thoughtSummary?.let {
    println("Thought Summary: $it")
}
response.text?.let {
    println("Answer: $it")
}

Java

Enable thought summaries in the GenerationConfig as part of creating a GenerativeModel instance.


// ...

// Set the thinking configuration
// Optionally enable thought summaries in the generated response (default is false)
ThinkingConfig thinkingConfig = new ThinkingConfig.Builder()
    .setIncludeThoughts(true)
    .build();

GenerationConfig generationConfig = GenerationConfig.builder()
    .setThinkingConfig(thinkingConfig)
    .build();

// Specify the config as part of creating the `GenerativeModel` instance
GenerativeModelFutures model = GenerativeModelFutures.from(
        FirebaseAI.getInstance(GenerativeBackend.googleAI())
                .generativeModel(
                  /* modelName */ "GEMINI_MODEL_NAME",
                  /* generationConfig */ generationConfig
                );
);

// Handle the response that includes thought summaries
ListenableFuture responseFuture = model.generateContent("solve x^2 + 4x + 4 = 0");
Futures.addCallback(responseFuture, new FutureCallback() {
    @Override
    public void onSuccess(GenerateContentResponse response) {
        if (response.getThoughtSummary() != null) {
            System.out.println("Thought Summary: " + response.getThoughtSummary());
        }
        if (response.getText() != null) {
            System.out.println("Answer: " + response.getText());
        }
    }

    @Override
    public void onFailure(Throwable t) {
        // Handle error
    }
}, MoreExecutors.directExecutor());

Web

Enable thought summaries in the GenerationConfig as part of creating a GenerativeModel instance.


// ...

const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Set the thinking configuration
// Optionally enable thought summaries in the generated response (default is false)
const generationConfig = {
  thinkingConfig: {
    includeThoughts: true
  }
};

// Specify the config as part of creating the `GenerativeModel` instance
const model = getGenerativeModel(ai, { model: "GEMINI_MODEL_NAME", generationConfig });

const result = await model.generateContent("solve x^2 + 4x + 4 = 0");
const response = result.response;

// Handle the response that includes thought summaries
if (response.thoughtSummary()) {
    console.log(`Thought Summary: ${response.thoughtSummary()}`);
}
const text = response.text();
console.log(`Answer: ${text}`);

Dart

Enable thought summaries in the GenerationConfig as part of creating a GenerativeModel instance.


// ...

// Set the thinking configuration
// Optionally enable thought summaries in the generated response (default is false)
final thinkingConfig = ThinkingConfig(includeThoughts: true);

final generationConfig = GenerationConfig(
  thinkingConfig: thinkingConfig
);

// Specify the config as part of creating the `GenerativeModel` instance
final model = FirebaseAI.googleAI().generativeModel(
  model: 'GEMINI_MODEL_NAME',
  generationConfig: generationConfig,
);

final response = await model.generateContent('solve x^2 + 4x + 4 = 0');

// Handle the response that includes thought summaries
if (response.thoughtSummary != null) {
  print('Thought Summary: ${response.thoughtSummary}');
}
if (response.text != null) {
  print('Answer: ${response.text}');
}

Unity

Enable thought summaries in the GenerationConfig as part of creating a GenerativeModel instance.


// ...

// Set the thinking configuration
// Optionally enable thought summaries in the generated response (default is false)
var thinkingConfig = new ThinkingConfig(includeThoughts: true);

var generationConfig = new GenerationConfig(
  thinkingConfig: thinkingConfig
);

// Specify the config as part of creating the `GenerativeModel` instance
var model = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI()).GetGenerativeModel(
  modelName: "GEMINI_MODEL_NAME",
  generationConfig: generationConfig
);

var response = await model.GenerateContentAsync("solve x^2 + 4x + 4 = 0");

// Handle the response that includes thought summaries
if (response.ThoughtSummary != null) {
    Debug.Log($"Thought Summary: {response.ThoughtSummary}");
}
if (response.Text != null) {
    Debug.Log($"Answer: {response.Text}");
}

View the response and thought summary

# Example Response:
#     Okay, let's solve the quadratic equation x² + 4x + 4 = 0.
#     ...
#     **Answer:**
#     The solution to the equation x² + 4x + 4 = 0 is x = -2. This is a repeated root (or a root with multiplicity 2).

# Example Thought Summary:
#     **My Thought Process for Solving the Quadratic Equation**
#
#     Alright, let's break down this quadratic, x² + 4x + 4 = 0. First things first:
#     it's a quadratic; the x² term gives it away, and we know the general form is
#     ax² + bx + c = 0.
#
#     So, let's identify the coefficients: a = 1, b = 4, and c = 4. Now, what's the
#     most efficient path to the solution? My gut tells me to try factoring; it's
#     often the fastest route if it works. If that fails, I'll default to the quadratic
#     formula, which is foolproof. Completing the square? It's good for deriving the
#     formula or when factoring is difficult, but not usually my first choice for
#     direct solving, but it can't hurt to keep it as an option.
#
#     Factoring, then. I need to find two numbers that multiply to 'c' (4) and add
#     up to 'b' (4). Let's see... 1 and 4 don't work (add up to 5). 2 and 2? Bingo!
#     They multiply to 4 and add up to 4. This means I can rewrite the equation as
#     (x + 2)(x + 2) = 0, or more concisely, (x + 2)² = 0. Solving for x is now
#     trivial: x + 2 = 0, thus x = -2.
#
#     Okay, just to be absolutely certain, I'll run the quadratic formula just to
#     double-check. x = [-b ± √(b² - 4ac)] / 2a. Plugging in the values, x = [-4 ±
#     √(4² - 4 * 1 * 4)] / (2 * 1). That simplifies to x = [-4 ± √0] / 2. So, x =
#     -2 again - a repeated root. Nice.
#
#     Now, let's check via completing the square. Starting from the same equation,
#     (x² + 4x) = -4. Take half of the b-value (4/2 = 2), square it (2² = 4), and
#     add it to both sides, so x² + 4x + 4 = -4 + 4. Which simplifies into (x + 2)²
#     = 0. The square root on both sides gives us x + 2 = 0, therefore x = -2, as
#      expected.
#
#     Always, *always* confirm! Let's substitute x = -2 back into the original
#     equation: (-2)² + 4(-2) + 4 = 0. That's 4 - 8 + 4 = 0. It checks out.
#
#     Conclusion: the solution is x = -2. Confirmed.

Stream thought summaries

You can also view thought summaries if you choose to stream a response using generateContentStream. This will return rolling, incremental summaries during the response generation.

Swift

Enable thought summaries in the GenerationConfig as part of creating a GenerativeModel instance.


// ...

// Set the thinking configuration
// Optionally enable thought summaries in the generated response (default is false)
let generationConfig = GenerationConfig(
  thinkingConfig: ThinkingConfig(includeThoughts: true)
)

// Specify the config as part of creating the `GenerativeModel` instance
let model = FirebaseAI.firebaseAI(backend: .googleAI()).generativeModel(
  modelName: "GEMINI_MODEL_NAME",
  generationConfig: generationConfig
)

let stream = try model.generateContentStream("solve x^2 + 4x + 4 = 0")

// Handle the streamed response that includes thought summaries
var thoughts = ""
var answer = ""
for try await response in stream {
  if let thought = response.thoughtSummary {
    if thoughts.isEmpty {
      print("--- Thoughts Summary ---")
    }
    print(thought)
    thoughts += thought
  }

  if let text = response.text {
    if answer.isEmpty {
      print("--- Answer ---")
    }
    print(text)
    answer += text
  }
}

Kotlin

Enable thought summaries in the GenerationConfig as part of creating a GenerativeModel instance.


// ...

// Set the thinking configuration
// Optionally enable thought summaries in the generated response (default is false)
val generationConfig = generationConfig {
  thinkingConfig = thinkingConfig {
      includeThoughts = true
  }
}

// Specify the config as part of creating the `GenerativeModel` instance
val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
  modelName = "GEMINI_MODEL_NAME",
  generationConfig,
)

// Handle the streamed response that includes thought summaries
var thoughts = ""
var answer = ""
model.generateContentStream("solve x^2 + 4x + 4 = 0").collect { response ->
    response.thoughtSummary?.let {
        if (thoughts.isEmpty()) {
            println("--- Thoughts Summary ---")
        }
        print(it)
        thoughts += it
    }
    response.text?.let {
        if (answer.isEmpty()) {
            println("--- Answer ---")
        }
        print(it)
        answer += it
    }
}

Java

Enable thought summaries in the GenerationConfig as part of creating a GenerativeModel instance.


// ...

// Set the thinking configuration
// Optionally enable thought summaries in the generated response (default is false)
ThinkingConfig thinkingConfig = new ThinkingConfig.Builder()
    .setIncludeThoughts(true)
    .build();

GenerationConfig generationConfig = GenerationConfig.builder()
    .setThinkingConfig(thinkingConfig)
    .build();

// Specify the config as part of creating the `GenerativeModel` instance
GenerativeModelFutures model = GenerativeModelFutures.from(
        FirebaseAI.getInstance(GenerativeBackend.googleAI())
                .generativeModel(
                  /* modelName */ "GEMINI_MODEL_NAME",
                  /* generationConfig */ generationConfig
                );
);

// Streaming with Java is complex and depends on the async library used.
// This is a conceptual example using a reactive stream.
Flowable responseStream = model.generateContentStream("solve x^2 + 4x + 4 = 0");

// Handle the streamed response that includes thought summaries
StringBuilder thoughts = new StringBuilder();
StringBuilder answer = new StringBuilder();

responseStream.subscribe(response -> {
    if (response.getThoughtSummary() != null) {
        if (thoughts.length() == 0) {
            System.out.println("--- Thoughts Summary ---");
        }
        System.out.print(response.getThoughtSummary());
        thoughts.append(response.getThoughtSummary());
    }
    if (response.getText() != null) {
        if (answer.length() == 0) {
            System.out.println("--- Answer ---");
        }
        System.out.print(response.getText());
        answer.append(response.getText());
    }
}, throwable -> {
    // Handle error
});

Web

Enable thought summaries in the GenerationConfig as part of creating a GenerativeModel instance.


// ...

const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Set the thinking configuration
// Optionally enable thought summaries in the generated response (default is false)
const generationConfig = {
  thinkingConfig: {
    includeThoughts: true
  }
};

// Specify the config as part of creating the `GenerativeModel` instance
const model = getGenerativeModel(ai, { model: "GEMINI_MODEL_NAME", generationConfig });

const result = await model.generateContentStream("solve x^2 + 4x + 4 = 0");

// Handle the streamed response that includes thought summaries
let thoughts = "";
let answer = "";
for await (const chunk of result.stream) {
  if (chunk.thoughtSummary()) {
    if (thoughts === "") {
      console.log("--- Thoughts Summary ---");
    }
    // In Node.js, process.stdout.write(chunk.thoughtSummary()) could be used
    // to avoid extra newlines.
    console.log(chunk.thoughtSummary());
    thoughts += chunk.thoughtSummary();
  }

  const text = chunk.text();
  if (text) {
    if (answer === "") {
      console.log("--- Answer ---");
    }
    // In Node.js, process.stdout.write(text) could be used.
    console.log(text);
    answer += text;
  }
}

Dart

Enable thought summaries in the GenerationConfig as part of creating a GenerativeModel instance.


// ...

// Set the thinking configuration
// Optionally enable thought summaries in the generated response (default is false)
final thinkingConfig = ThinkingConfig(includeThoughts: true);

final generationConfig = GenerationConfig(
  thinkingConfig: thinkingConfig
);

// Specify the config as part of creating the `GenerativeModel` instance
final model = FirebaseAI.googleAI().generativeModel(
  model: 'GEMINI_MODEL_NAME',
  generationConfig: generationConfig,
);

final responses = model.generateContentStream('solve x^2 + 4x + 4 = 0');

// Handle the streamed response that includes thought summaries
var thoughts = '';
var answer = '';
await for (final response in responses) {
  if (response.thoughtSummary != null) {
    if (thoughts.isEmpty) {
      print('--- Thoughts Summary ---');
    }
    thoughts += response.thoughtSummary!;
  }
  if (response.text != null) {
    if (answer.isEmpty) {
      print('--- Answer ---');
    }
    answer += response.text!;
  }
}

Unity

Enable thought summaries in the GenerationConfig as part of creating a GenerativeModel instance.


// ...

// Set the thinking configuration
// Optionally enable thought summaries in the generated response (default is false)
var thinkingConfig = new ThinkingConfig(includeThoughts: true);

var generationConfig = new GenerationConfig(
  thinkingConfig: thinkingConfig
);

// Specify the config as part of creating the `GenerativeModel` instance
var model = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI()).GetGenerativeModel(
  modelName: "GEMINI_MODEL_NAME",
  generationConfig: generationConfig
);

var stream = model.GenerateContentStreamAsync("solve x^2 + 4x + 4 = 0");

// Handle the streamed response that includes thought summaries
var thoughts = "";
var answer = "";
await foreach (var response in stream)
{
    if (response.ThoughtSummary != null)
    {
        if (string.IsNullOrEmpty(thoughts))
        {
            Debug.Log("--- Thoughts Summary ---");
        }
        Debug.Log(response.ThoughtSummary);
        thoughts += response.ThoughtSummary;
    }
    if (response.Text != null)
    {
        if (string.IsNullOrEmpty(answer))
        {
            Debug.Log("--- Answer ---");
        }
        Debug.Log(response.Text);
        answer += response.Text;
    }
}

Thought signatures

When using thinking in multi-turn interactions, the model doesn't have access to thought context from previous turns. However, if you're using function calling, you can take advantage of thought signatures to maintain thought context across turns. Thought signatures are encrypted representations of the model's internal thought process, and they're available when using thinking and function calling. Specifically, thought signatures are generated when:

Thinking is enabled and thoughts are generated.
The request includes function declarations.

To take advantage of thought signatures, use function calling as normal. The Firebase AI Logic SDKs simplify the process by managing the state and automatically handling thought signatures for you. The SDKs automatically pass any generated thought signatures between subsequent sendMessage or sendMessageStream calls in a Chat session.

Pricing and counting thinking tokens

Thinking tokens use the same pricing as text-output tokens. If you enable thought summaries, they are considered to be thinking tokens and are priced accordingly.

You can enable AI monitoring in the Firebase console to monitor the count of thinking tokens for requests that have thinking enabled.

You can get the total number of thinking tokens from the thoughtsTokenCount field in the usageMetadata attribute of the response:

Swift

// ...

let response = try await model.generateContent("Why is the sky blue?")

if let usageMetadata = response.usageMetadata {
  print("Thoughts Token Count: \(usageMetadata.thoughtsTokenCount)")
}

Kotlin

// ...

val response = model.generateContent("Why is the sky blue?")

response.usageMetadata?.let { usageMetadata ->
    println("Thoughts Token Count: ${usageMetadata.thoughtsTokenCount}")
}

Java

// ...

ListenableFuture<GenerateContentResponse> response =
    model.generateContent("Why is the sky blue?");

Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {
    @Override
    public void onSuccess(GenerateContentResponse result) {
        String usageMetadata = result.getUsageMetadata();
        if (usageMetadata != null) {
            System.out.println("Thoughts Token Count: " +
                usageMetadata.getThoughtsTokenCount());
        }
    }

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

Web

// ...

const response = await model.generateContent("Why is the sky blue?");

if (response?.usageMetadata?.thoughtsTokenCount != null) {
    console.log(`Thoughts Token Count: ${response.usageMetadata.thoughtsTokenCount}`);
}

Dart

// ...

final response = await model.generateContent(
  Content.text("Why is the sky blue?"),
]);

if (response?.usageMetadata case final usageMetadata?) {
  print("Thoughts Token Count: ${usageMetadata.thoughtsTokenCount}");
}

Unity

// ...

var response = await model.GenerateContentAsync("Why is the sky blue?");

if (response.UsageMetadata != null)
{
    UnityEngine.Debug.Log($"Thoughts Token Count: {response.UsageMetadata?.ThoughtsTokenCount}");
}

Learn more about tokens in the count tokens guide.

Thinking Stay organized with collections Save and categorize content based on your preferences.

Use a thinking model

Models that support this capability

Best practices & prompting guidance for using thinking models

Control the amount of thinking

Thinking levels (Gemini 3 models)

Set the thinking level

Swift

Kotlin

Java

Web

Dart

Unity

Supported thinking level values

Thinking budgets (Gemini 2.5 models)

Set the thinking budget

Swift

Kotlin

Java

Web

Dart

Unity

Supported thinking budget values

Disable thinking for Gemini 2.5 models

Enable dynamic thinking for Gemini 2.5 models

Task complexity for all thinking models

Thought summaries

Enable thought summaries

Swift

Kotlin

Java

Web

Dart

Unity

Stream thought summaries

Swift

Kotlin

Java

Web

Dart

Unity

Thought signatures

Pricing and counting thinking tokens

Swift

Kotlin

Java

Web

Dart

Unity

Thinking