Select a model

The Prompt API lets you explicitly select the version of Gemini Nano used by your app. By configuring the model release stage and performance preference, you can access new model capabilities earlier or optimize your app for specific hardware constraints.

Understand model configurations

To select a specific model, you must configure two key parameters within the ModelConfig class: the release stageand the model preference.

Release stages

The release stage lets you choose between a stable or preview model:

• Stable ( ModelReleaseStage.STABLE ):Selects the latest model version that is fully tested and on consumer devices. This is the default setting.
• Preview ( ModelReleaseStage.PREVIEW ):Selects the latest model version in the preview stage. This stage lets you test beta features or newer model architectures before they are widely deployed to the stable stage.

For prerequisites and enrollment instructions for the developer preview, see the AICore Developer Preview guide .

Model preference

The model preference lets you specify which performance characteristics are most important for your use case.

• Full ( ModelPreference.FULL ):This preference is recommended when model accuracy and full capabilities are prioritized over speed.
• Fast ( ModelPreference.FAST ):This preference is recommended for latency-sensitive apps that require minimal response times.

Configure the generative model

To use a specific model variant, you must define a ModelConfig and pass it to your GenerationConfig when initializing the client.

The following example demonstrates how to configure the client to use a Fastmodel from the Previewstage:

  // Define the configuration with a specific stage and preference 
 val 
  
 previewFastConfig 
  
 = 
  
 generationConfig 
  
 { 
  
 modelConfig 
  
 = 
  
 modelConfig 
  
 { 
  
 releaseStage 
  
 = 
  
 ModelReleaseStage 
 . 
 PREVIEW 
  
 preference 
  
 = 
  
 ModelPreference 
 . 
 FAST 
  
 } 
 } 
 // Initialize the GenerativeModel with the configuration 
 val 
  
 generativeModel 
  
 = 
  
 Generation 
 . 
 getClient 
 ( 
 previewFastConfig 
 ) 
 

  // Define the configuration with a specific stage and preference 
 GenerationConfig 
  
 previewFastConfig 
  
 = 
  
 new 
  
 GenerationConfig 
 . 
 Builder 
 () 
  
 . 
 setModelConfig 
 ( 
 new 
  
 ModelConfig 
 . 
 Builder 
 () 
  
 . 
 setReleaseStage 
 ( 
 ModelReleaseStage 
 . 
 PREVIEW 
 ) 
  
 . 
 setPreference 
 ( 
 ModelPreference 
 . 
 FAST 
 ) 
  
 . 
 build 
 ()) 
  
 . 
 build 
 (); 
 // Initialize the GenerativeModel with the configuration 
 GenerativeModel 
  
 generativeModel 
  
 = 
  
 Generation 
 . 
 INSTANCE 
 . 
 getClient 
 ( 
 previewFastConfig 
 ); 
 

Check model availability

Not all devices support every combination of release stage and model preference. Preview models are only available on the list of supported devices in the AICore Developer Preview guide.

The API does not provide a method to list all available model configurations upfront. Instead, initialize the client with your desired configuration and then verify its status.

• Initialize the client:Create the GenerativeModel instance using your preferred ModelConfig .

• Check status:Call the checkStatus() method on the instance to verify that the specific model variant is available on the device.

  val 
  
 generativeModel 
  
 = 
  
 Generation 
 . 
 getClient 
 ( 
 previewFastConfig 
 ) 
 // Verify that the specific preview model is available 
 val 
  
 status 
  
 = 
  
 generativeModel 
 . 
 checkStatus 
 () 
 when 
  
 ( 
 status 
 ) 
  
 { 
  
 FeatureStatus 
 . 
 UNAVAILABLE 
  
 - 
>  
 { 
  
 // Specified preview model is not available on this device 
  
 } 
  
 FeatureStatus 
 . 
 DOWNLOADABLE 
  
 - 
>  
 { 
  
 // Specified preview model is available for this device, but not downloaded yet. 
  
 // Model may be downloaded through the AICore app or by calling generativeModel.download() 
  
 } 
  
 FeatureStatus 
 . 
 AVAILABLE 
  
 - 
>  
 { 
  
 // Proceed with inference 
  
 } 
  
 FeatureStatus 
 . 
 DOWNLOADING 
  
 - 
>  
 { 
  
 // Specified preview model is downloading 
  
 } 
 } 
 

  GenerativeModel 
  
 generativeModel 
  
 = 
  
 Generation 
 . 
 INSTANCE 
 . 
 getClient 
 ( 
 previewFastConfig 
 ); 
 // For Java, use GenerativeModelFutures if you prefer ListenableFuture 
 GenerativeModelFutures 
  
 generativeModelFutures 
  
 = 
  
 GenerativeModelFutures 
 . 
 from 
 ( 
 generativeModel 
 ); 
 Futures 
 . 
 addCallback 
 ( 
  
 generativeModelFutures 
 . 
 checkStatus 
 (), 
  
 new 
  
 FutureCallback<Integer> 
 () 
  
 { 
  
 @Override 
  
 public 
  
 void 
  
 onSuccess 
 ( 
 Integer 
  
 status 
 ) 
  
 { 
  
 if 
  
 ( 
 status 
  
 == 
  
 FeatureStatus 
 . 
 AVAILABLE 
 ) 
  
 { 
  
 // Proceed with inference 
  
 } 
  
 else 
  
 if 
  
 ( 
 status 
  
 == 
  
 FeatureStatus 
 . 
 DOWNLOADING 
 ) 
  
 { 
  
 // Specified preview model is downloading 
  
 } 
  
 else 
  
 if 
  
 ( 
 status 
  
 == 
  
 FeatureStatus 
 . 
 DOWNLOADABLE 
 ) 
  
 { 
  
 // Specified preview model is available for this device, but not downloaded yet. 
  
 // Call generativeModelFutures.download(callback) or use the AICore app. 
  
 } 
  
 else 
  
 if 
  
 ( 
 status 
  
 == 
  
 FeatureStatus 
 . 
 UNAVAILABLE 
 ) 
  
 { 
  
 // Specified preview model is not available on this device 
  
 } 
  
 } 
  
 @Override 
  
 public 
  
 void 
  
 onFailure 
 ( 
 Throwable 
  
 t 
 ) 
  
 { 
  
 // Handle failure 
  
 } 
  
 }, 
  
 ContextCompat 
 . 
 getMainExecutor 
 ( 
 context 
 )); 
 

To ensure forward compatibility with potential future configurations, the SDK is designed to avoid throwing a GenAiException during client initialization, relying solely on the checkStatus() method for availability verification.

Best practices

• Implement fallback strategies:Because preview models are not guaranteed to be available on all devices, always implement a fallback strategy. If checkStatus() returns false for a preview configuration, your app should gracefully revert to ModelReleaseStage.STABLE and ModelPreference.FULL .
• Use model release stages for development and production:Use the Previewstage during development and internal testing to evaluate upcoming model improvements. Switch to Stablefor your public production releases to ensure consistent behavior for your users.

Known issues

• For some devices in the preview release stage, they won't support multiple candidates in the output and will return an error if candidateCount is set larger than 1. See the API reference for more details.
• Multiple samples are not supported on some implementations. Performance will be slower than final production.