> For the complete documentation index, see [llms.txt](https://docs.tensorx.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tensorx.ai/api-reference/models.md).

# Models

Available models on the TensorX platform.

***

## Overview

TensorX provides access to leading open-source and proprietary AI models through a unified API. All models are accessible via the same OpenAI-compatible endpoint.

{% hint style="info" %}
**Live Pricing & Full List**: Visit [tensorx.ai/models](https://tensorx.ai/models) for real-time pricing and the complete model catalog.
{% endhint %}

***

## Model Capabilities

Our Large Language Models (LLMs) support:

* **Text Generation** - Generate coherent, contextual content
* **Language Understanding** - Understand meaning and context
* **Code Generation** - Write, analyze, and debug code
* **Reasoning** - Complex problem solving and analysis
* **Function Calling** - Tool use and structured outputs
* **Vision** - Image understanding (select models)
* **Multilingual** - Support for multiple languages
* **Text-to-Speech** - Convert text to natural audio
* **Speech-to-Text** - Transcribe audio to text

***

## Available Models

TensorX hosts a broad catalogue of leading open and proprietary models (GLM, MiniMax, Moonshot Kimi, DeepSeek, Qwen, Llama, GPT-OSS) plus audio models for TTS and STT, all on the same OpenAI-compatible endpoint.

{% hint style="info" %}
**The model list and pricing change regularly.** For the current catalogue and live pricing, see [tensorx.ai/models](https://tensorx.ai/models), or call the [List Models API](#list-models-api) below to fetch the available model IDs programmatically.
{% endhint %}

See the [Model Recommendations](#model-recommendations) below for which model to pick by use case.

***

## Model Recommendations

| Use Case              | Recommended Models                                                   |
| --------------------- | -------------------------------------------------------------------- |
| **General Chat**      | `deepseek/deepseek-chat-v3.1`, `meta-llama/llama-3.3-70b-instruct`   |
| **Complex Reasoning** | `deepseek/deepseek-r1-0528`, `z-ai/glm-5.1`                          |
| **Coding**            | `z-ai/glm-5.1`, `minimax/minimax-m2`                                 |
| **Vision Tasks**      | `moonshotai/kimi-k2.5`, `meta-llama/llama-4-maverick`                |
| **Long Context**      | `moonshotai/kimi-k2.5` (262K), `meta-llama/llama-4-maverick` (1050K) |
| **Multilingual**      | `z-ai/glm-5.1` (Chinese/English)                                     |
| **Text-to-Speech**    | `chatterbox-turbo`                                                   |
| **Speech-to-Text**    | `Systran/faster-whisper-large-v3`                                    |

***

## List Models API

Retrieve the list of available models programmatically:

```bash
curl https://api.tensorx.ai/v1/models \
  -H "Authorization: Bearer $TENSORX_API_KEY"
```

### Response

```json
{
  "object": "list",
  "data": [
    {
      "id": "deepseek/deepseek-chat-v3.1",
      "object": "model",
      "created": 1706745600,
      "owned_by": "deepseek"
    },
    {
      "id": "z-ai/glm-5.1",
      "object": "model",
      "created": 1706745600,
      "owned_by": "z-ai"
    }
  ]
}
```

***

## Using Models

Specify the model ID in your API request:

```python
from openai import OpenAI

client = OpenAI(
    api_key="your-tensorx-api-key",
    base_url="https://api.tensorx.ai/v1"
)

# Use DeepSeek for fast reasoning
response = client.chat.completions.create(
    model="deepseek/deepseek-chat-v3.1",
    messages=[{"role": "user", "content": "Explain quantum computing"}]
)

# Use GLM for coding
response = client.chat.completions.create(
    model="z-ai/glm-5.1",
    messages=[{"role": "user", "content": "Write a Python function to sort a list"}]
)

# Use Llama for long context
response = client.chat.completions.create(
    model="meta-llama/llama-4-maverick",
    messages=[{"role": "user", "content": "Summarize this document..."}]
)
```

***

## Pricing

Model pricing is based on token usage (input + output tokens).

{% hint style="success" %}
**View Current Pricing**: [tensorx.ai/models](https://tensorx.ai/models)

Pricing is displayed per 1M tokens for each model.
{% endhint %}

### How Pricing Works

```
Cost = (Input Tokens × Input Price) + (Output Tokens × Output Price)
```

* **Input tokens**: Text you send to the model
* **Output tokens**: Text the model generates

### Tips to Optimize Costs

1. **Choose the right model** - Use smaller models for simple tasks
2. **Set max\_tokens** - Limit output length when appropriate
3. **Use caching** - Cache responses for repeated queries
4. **Monitor usage** - Check your [dashboard](https://app.tensorx.ai/dashboard) regularly

***

## Feature Support

| Feature              | Supported Models                      |
| -------------------- | ------------------------------------- |
| **Function Calling** | DeepSeek, GLM, Qwen, GPT-OSS, MiniMax |
| **Reasoning**        | DeepSeek, GPT-OSS                     |
| **Vision**           | Llama 4 Maverick                      |
| **Streaming**        | All models                            |
| **JSON Mode**        | All models                            |
| **Text-to-Speech**   | chatterbox-turbo                      |
| **Speech-to-Text**   | Systran/faster-whisper-large-v3       |

***

## See Also

* [Chat Completions](https://github.com/Tensorix-ai/tensorix-docs/blob/main/api-reference/chat-completions/README.md) - API endpoint documentation
* [Quantisation](https://github.com/Tensorix-ai/tensorix-docs/blob/main/api-reference/quantisation/README.md) - How models are quantised and how to check the level for any specific model
* [Audio API](https://github.com/Tensorix-ai/tensorix-docs/blob/main/audio/README.md) - TTS and STT documentation
* [API Examples](https://github.com/Tensorix-ai/tensorix-docs/blob/main/api-examples/README.md) - Code examples
* [Pricing](https://tensorx.ai/models) - Live pricing


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