Model selectors
Mechanisms in an API or software that let developers or users select a specific AI model for a task.
Model selectors are like a menu for programmers. When building an application that uses AI, developers often need to choose which specific AI model will power a feature. Model selectors provide a way to make that choice.
For example, a developer might use a model selector to pick a faster, less powerful AI model for a simple chatbot that answers basic questions, or a more advanced, slower model for generating complex creative text. This allows them to balance cost, speed, and capability for different parts of their application.
Model selectors are components within an AI platform's API or SDK that enable users to specify which particular AI model should process a given request. This allows for fine-grained control over the inference process, enabling selection based on factors like model size, performance characteristics, cost, and specific capabilities.
These selectors can range from simple string identifiers (e.g., 'gemini-pro', 'gpt-4o') to more complex configurations that might include versioning, fine-tuning identifiers, or even routing logic for dynamic model selection based on input characteristics. The ability to select models is crucial for optimizing resource usage and tailoring AI behavior to specific application needs.
Google Gemini is updating its API cookbook to include new model options and improve how developers select them. These updates suggest a growing need for granular control over model selection as more specialized AI models become available.
- Gemini API Cookbook: feat(models): add gemini-nano-banana-2.1 to model selectors across notebooks (#1410)Google · Gemini API Cookbook ·
- Gemini API Cookbook: fix(nblint): report invalid model-selector options without crashing (#1405)Google · Gemini API Cookbook ·
- Google Gemini
- Google Gemini's API cookbook explicitly mentions and updates 'model selectors' to manage different versions and types of Gemini models.
- Model versioning
- This refers to tracking and managing different iterations of t
- Model deployment
- This is the process of making a trained model available for us
- Prompt engineering
- This involves crafting input for AI models, whereas model se
The concept of selecting specific models within a larger suite of AI offerings has evolved with the proliferation of large language models and AI platforms. Early APIs often exposed single models, but as model families grew, explicit selection mechanisms became necessary for developers.
Go to the Google AI Studio or OpenAI Playground. Look for a dropdown menu or input field labeled 'Model'. Try selecting a different model from the list and send the same prompt to see how the output changes. Note any differences in response style, speed, or quality.
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
- PromptRole-play a student struggling with math for AI tutor simulationSimulate a student's math struggles and uncertainty for AI tutor evaluation. · 5 min
- TemplateReusable workflow templates for healthcare teamsTeams can create and share templates for common workflows like documentation or prior authorizations. · 15 min
- TemplateTemplate for building an AI tool tailored to your workA structured approach to defining and building a custom AI tool for specific business needs. · 15 min
- TemplateEvals CSV prompt list for agent skillsA CSV file template for listing prompts used in evaluating agent skills. · 20 min
- HeuristicDouble coding agent task completion with docs linksProviding coding agents with a documentation link significantly increases their task completion rate. · 2 min
Questions people ask
- Why would I need to select a model?
- You select a model to match the task's requirements. Some models are faster and cheaper for simple tasks, while others are more powerful for complex tasks, offering a trade-off between performance, cost, and capability.
- Are model selectors the same as model versions?
- Not exactly. Model selectors let you choose between different AI models (e.g., a small vs. a large model), while model versioning refers to different iterations or updates of the same specific model.
First seen on Subvolts Oct 6, 2026. Updated Oct 13, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.