> For the complete documentation index, see [llms.txt](https://docs.glbnxt.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.glbnxt.com/faq/glbnxt-workspace/which-ai-models-are-available-in-workspace.md). # Which AI models are available in Workspace? GLBNXT Workspace provides access to a selection of leading open-source large language models, all running within your sovereign environment. Unlike consumer AI tools where you interact with a single model chosen by the provider, Workspace gives your organisation the ability to work with multiple models and select the one best suited to the task at hand. The models available in your environment are configured by your administrator, who can enable or restrict access based on team needs, use case requirements, or organisational policy. This means the model selection your teams see reflects a deliberate choice made within your organisation, not a default imposed by an external provider. In general terms, the models available through Workspace cover a broad range of capabilities including long-form writing, document analysis, summarisation, translation, reasoning, and coding assistance. Some models are optimised for speed and everyday tasks, while others are better suited to complex, multi-step reasoning or handling large volumes of text. Because GLBNXT is built on open-source foundations and the AI landscape evolves quickly, the model catalogue is updated regularly. New models are evaluated, tested, and made available as they meet the performance and security standards required for enterprise use. For a current list of available models in your environment, check the model selector within Workspace or speak with your administrator. If your organisation has specific model requirements not currently available, contact your GLBNXT account team to discuss options. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.glbnxt.com/faq/glbnxt-workspace/which-ai-models-are-available-in-workspace.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.