Supported foundation models
You can work with third-party and IBM foundation models in IBM watsonx.ai.
How to choose a model
To review factors that can help you to choose a model, such as supported tasks and languages, see Choosing a model and Foundation model benchmarks.
For more information about the foundation models provided with watsonx.ai for embedding and reranking text, see Supported encoder models.
Provided foundation models that are ready to use
You can deploy foundation models from a collection of models curated by IBM in watsonx.ai. You can prompt these foundation models in the Prompt Lab or programmatically.
You can work with the following types of provided foundation models:
All IBM foundation models in watsonx.ai are indemnified by IBM.
For information about the GPU requirements for the supported foundation models, see Foundation models in IBM watsonx.ai in the IBM Software Hub documentation.
IBM foundation models
You can inference the following supported foundation models that are provided by IBM. The foundation models must be deployed in your cluster by an administrator to be available for use. All IBM models are instruction-tuned.
| Model name | Context window (input + output tokens) |
Supported tasks | More information |
|---|---|---|---|
| granite-3-8b-instruct | 4,096 | • classification • extraction • function calling • generation • question answering • summarization |
• Model card • Website • Research paper |
Third-party foundation models
The following table lists the latest third-party foundation models for inferencing. An administrator must deploy the foundation models in your cluster before you can use these models.
| Model name | Provider | Context window (input + output tokens) |
Supported tasks | More information |
|---|---|---|---|---|
| llama-3-2-11b-vision-instruct | Meta | 131,072 | • classification • code generation and conversion • extraction • function calling • generation • question answering • retrieval-augmented generation • summarization |
• Model card • Meta AI blog • Research paper |
| mistral-small-3-1-24b-instruct-2503 | Mistral AI | 131,072 | • classification • code • extraction • generation • retrieval-augmented generation • summarization • translation |
• Model card • Blog post for Mistral Small 3 |
| llama-3-3-70b-instruct |
Meta | 131,072 | • classification • code • extraction • generation • question answering • retrieval-augmented generation • summarization |
• Model card • Meta AI blog • Meta AI docs |
| mistral-large | Mistral AI | 32,768 | • classification • code • extraction • generation • retrieval-augmented generation • summarization • translation |
• Model card • Blog post for Mistral Large 2 Note:
|
| pixtral-12b | Mistral AI | 128,000 | • classification • generation • retrieval-augmented generation • summarization |
• Blog post for Pixtral 12B |