Source URL: https://simonwillison.net/2025/Jan/22/llm-gemini/
Source: Simon Willison’s Weblog
Title: llm-gemini 0.9
Feedly Summary: llm-gemini 0.9
This new release of my llm-gemini plugin adds support for two new experimental models:
learnlm-1.5-pro-experimental is “an experimental task-specific model that has been trained to align with learning science principles when following system instructions for teaching and learning use cases" – more here.
gemini-2.0-flash-thinking-exp-01-21 is a brand new version of the Gemini 2.0 Flash Thinking model released today:
Latest version also includes code execution, a 1M token content window & a reduced likelihood of thought-answer contradictions.
The most exciting new feature though is support for Google search grounding, where some Gemini models can execute Google searches as part of answering a prompt. This feature can be enabled using the new -o google_search 1 option.
Tags: gemini, llm, projects, generative-ai, inference-scaling, ai, llms
AI Summary and Description: Yes
Summary: The text discusses the new release of the llm-gemini plugin that introduces two experimental models, including advanced features like Google search integration and an expanded content processing capability. This is particularly relevant for professionals engaged in AI development and security, as it highlights advancements in language model functionality and potential implications for learning and information retrieval.
Detailed Description: The recent update to the llm-gemini plugin comes with significant enhancements aimed at improving model performance and usability in educational contexts and broader AI applications.
– The release includes two new experimental models:
– **learnlm-1.5-pro-experimental**: Designed specifically for educational purposes, this model aligns with principles of learning science, indicating a targeted approach to applying AI in the educational sector.
– **gemini-2.0-flash-thinking-exp-01-21**: This version features notable enhancements:
– **Code Execution**: The new capability for executing code during processing extends the model’s functionality and could strengthen security by automating verification processes.
– **1M Token Content Window**: This allows for the processing of longer contexts, which is particularly useful for complex tasks that require comprehensive information handling.
– **Reduced Likelihood of Thought-Answer Contradictions**: This improvement could enhance trust in AI outputs, reducing security risks related to incorrect information dissemination.
– **Google Search Grounding**:
– The integration of Google search capabilities is particularly innovative. By allowing models to execute searches as part of their responses, it not only improves the relevance and accuracy of outputs but also poses security considerations.
– Concerns over data privacy, compliance with regulations, and the management of sensitive information surfaced during search processes must be addressed.
These developments indicate a trend toward more interactive and context-aware AI systems, essential for applications in education and other fields where accurate and dependable information is paramount. The advances also emphasize the growing intersection of AI with practical functionalities that require thoughtful considerations around security and compliance.