> ## Documentation Index
> Fetch the complete documentation index at: https://gladekit.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Model Options 

Choose between a variety of AI model options to handle your requests

![App Model Options Dropdown](https://mintcdn.com/gladestudio-6d9f489c/SW-c_HXtS-2BOHLV/images/app/App_ModelOptions_Dropdown.png?fit=max\&auto=format\&n=SW-c_HXtS-2BOHLV\&q=85\&s=9e3a3feb04a0ad1950768040090efee2)

GladeKit supports models from **OpenAI (GPT)**, **Anthropic (Claude)**, and **Google (Gemini)**. Which models appear in the dropdown is controlled in your **Settings**. You can use the default set or choose exactly which models to show in list of options.

![App Settings Window Model Options](https://mintcdn.com/gladestudio-6d9f489c/20ymm7ILym1pTQoD/images/app/App_SettingsWindow_ModelOptions.png?fit=max\&auto=format\&n=20ymm7ILym1pTQoD\&q=85\&s=f30eed6ee51b3d0c10fcda9b1f9ac95f)

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## **Choosing a model**

* **Default pick:** **Auto** lets GladeKit choose which model to use depending on your task. We recommend using Auto, GPT 5.6 Terra, or Claude Sonnet 5 for most tasks.
* **Fast / cheap:** Pair **Fast** input mode with a “mini” or “flash” model (e.g. **GPT 5.6 Luna**) for quick answers.
* **Hard problems:** Use **Thinking** input mode with a top-tier model (**GPT 5.6 Sol**, **Claude Opus 4.8**) when you need deep reasoning or complex code.

Different models have different context limits (how much of your project and chat history they can use at once). If you have a very large project or long conversation, a model with a larger context (e.g. latest GPT/Claude models) may handle it better.

***

## Local Model Integration

GladeKit can route requests to a locally running model server. This is useful if you want to use a custom fine-tuned model, keep data off the cloud, or just experiment with open-weight models.

**Supported servers**

Any server that speaks the OpenAI Chat Completions API works:

* [LM Studio](https://lmstudio.ai/) — `http://localhost:1234/v1` (default)
* [Ollama](https://ollama.com/) — `http://localhost:11434/v1`
* [llama.cpp](https://github.com/ggerganov/llama.cpp) — `http://localhost:8080/v1`
* Any custom endpoint - set your own base URL in Settings

**Setup**

1. Start your local server and pull a model (e.g. `ollama run qwen3:4b`).
2. In GladeKit Settings → Models, enter your server's base URL.
3. Click **Test connection**. GladeKit will discover available models and add them to your model dropdown as **Local: \<model-id>**.

**Things to keep in mind**

* **Tool budget is reduced.** GladeKit sends a smaller set of tools (24 vs. 64) to local models because smaller models tend to degrade with large tool lists. Complex multi-step tasks may require more back-and-forth.
* **Context windows are smaller.** Most local models support 8k to 32k tokens. Very large projects or long conversations may overflow. If you see a context error, reload the model with a larger context (32768 is a good starting point in LM Studio or Ollama).
* **Tool quality varies.** Results depend heavily on the model size and how well it follows structured output. 7B to 14B models generally work better for agentic tasks than smaller ones.
* **The cloud loop stays active.** GladeKit's agentic loop (planning, retries, context gathering) still runs in the cloud. Only the LLM inference call goes to your local server. Full offline mode is not supported.


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