> ## Documentation Index
> Fetch the complete documentation index at: https://io.net/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Exploring AI Models
> The AI Models section provides access to a wide range of pre-trained and custom models. These models combine instructions, additional knowledge, and specialized skills for various tasks.
To get started, navigate to the [Models](https://id.io.net/ai/models) tab.
### What you can do on the Models Dashboard:
* Browse the list of models with their context lenghts and prices.
### Popular AI Models
| Model Name | Developer | Description |
| ----------------------------------------------------------------------------------------------------------------------------- | ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| [deepseek-ai/DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) | Deepseek | Enhanced model with improved reasoning, inference, and algorithmic post-training optimizations; designed for high-accuracy tasks. |
| [meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8](https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8) | Meta AI | Multimodal instruction-tuned model leveraging a mixture-of-experts (MoE) architecture for top-tier performance in both text and image understanding. |
| [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) | Open AI | Open-weight 117B parameter Mixture-of-Experts model supporting 128k context, advanced reasoning via chain-of-thought, optimized for real-world tool use, coding, and efficient local or cloud deployment. |
| [Intel/Qwen3-Coder-480B-A35B-Instruct-int4-mixed-ar](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct) | Qwen | High-capacity, instruction-tuned code generation model optimized with INT4 mixed-precision for fast inference, designed for complex programming tasks on Intel hardware. |
| [Qwen3-Next-80B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct) | Qwen | High-capacity model optimized for instruction following and knowledge-intensive tasks. |
| [gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) | Open AI | Open-source GPT-style model suitable for text generation and general-purpose tasks. |
| [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) | Qwen | Powerful 235B-parameter language model optimized for deep reasoning, planning, and complex multi-step tasks. |
| [Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407) | Mistral AI | Instruction-tuned model focusing on efficient reasoning and NLP tasks. |
| [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/unsloth/Llama-3.3-70B-Instruct) | Meta AI | Large-scale transformer model fine-tuned for instruction-following, aligning responses with human preferences. |
| [mistralai/Mistral-Large-Instruct-2411](https://huggingface.co/mistralai/Mistral-Large-Instruct-2411) | Mistral AI | Large instruction-tuned model offering strong general-purpose reasoning, summarization, and assistant-style responses. |
| [Qwen/Qwen2.5-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-32B-Instruct) | Qwen | Powerful vision-language model trained to follow multimodal instructions, suitable for image understanding, captioning, and reasoning. |
| [meta-llama/Llama-3.2-90B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-90B-Vision-Instruct) | Meta AI | Vision-language model with instruction tuning, capable of image analysis, visual Q\&A, and multimodal dialogue generation. |
| [BAAI/bge-multilingual-gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2) | BAAI | Multilingual embedding model optimized for semantic search and retrieval tasks across diverse languages. |
| [**zai-org/GLM-4.6**](https://huggingface.co/zai-org/GLM-4.6) | Z.AI | Advanced large-language model that expands context capacity to 200K tokens and significantly enhances coding, reasoning, and agentic capabilities. It excels in real-world coding tools, delivering more natural, human-aligned outputs. |
| [zai-org/GLM-4.7](https://huggingface.co/zai-org/GLM-4.7) | Z.AI | Advanced large-language model that retains a 200K-token context window and elevates coding, reasoning, and agentic capabilities with enhanced multi-step execution and consistency. It introduces sophisticated thinking modes like *Preserved Thinking* and *Turn-level Thinking*. |
| [**moonshotai/Kimi-K2-Instruct-0905**](https://huggingface.co/moonshotai/Kimi-K2-Instruct-0905) | Moonshot AI | A state-of-the-art mixture-of-experts (MoE) language model, featuring 32 billion activated parameters and a total of 1 trillion parameters. It delivers exceptional reasoning, coding, and content-generation performance. |
| [moonshotai/Kimi-K2-Thinking](https://huggingface.co/moonshotai/Kimi-K2-Thinking) | Moonshot AI | A high-performance open-source thinking model built for step-by-step thinking and dynamic tool use. It achieves state-of-the-art results on benchmarks such as Humanity’s Last Exam (HLE) and BrowseComp by dramatically scaling multi-step reasoning depth maintaining stable tool-use across 200–300 sequential calls. |
| [deepseek-ai/DeepSeek-V3.2](https://huggingface.co/deepseek-ai/DeepSeek-V3.2) | Deepseek | A LLM model that combines breakthrough efficiency with exceptional reasoning and tool-use performance. Powered by Sparse Attention and scalable RL post-training, it delivers premium long-context quality at reduced cost. Reports place it in the GPT-5 class with gold-medal wins in the 2025 IMO and IOI. |
For a full list of models, visit the [AI Models section](https://id.io.net/ai/models) in your dashboard.
## Testing an AI Model
Before using any of our AI models in your project, you can perform real-time testing directly from the dashboard. This allows you to evaluate the model’s performance and ensure it meets your requirements.
### How to Test an AI Model
1. **Select a Model:**
* Navigate to the **Models** tab.
* Select the model you want to test by clicking on it.
2. **Start Testing:**
* On the AI model chat page, type your question or input into the centered text field.
* Press your **Enter** key or the **arrow icon** to submit your query.
Your **Daily Credits usage** is shown above the request field. To view detailed model-specific usage information, visit the [**IO Intelligence Payments**](/guides/payment/io-intelligence-payments) page.
3. **Interact with the Model:**
* The AI model will respond, starting a conversation. You can continue testing by asking additional questions or providing more input.
* Compare different models to find the one that best suits your needs.
### Managing Chats
* **View Previous Chats:**
* On the left, you can manage your previously created chats with different AI models.
* Click on any chat to dive deeper into the conversation.
* **Create or Remove Chats:**
* Create a new chat by clicking the **+** button next to the model name.
* Remove a chat by clicking the three-dot menu and selecting **Delete**. Remove unnecessary chats to keep your workspace organized.
### Switching Between Models
* Next to the **+** button is a dropdown menu showing the currently selected model.
* Click the dropdown menu to select a different AI model and begin a new conversation with it.
