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.

Live Pricing & Full List: Visit tensorx.ai/models for real-time pricing and the complete model catalog.

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.

The model list and pricing change regularly. For the current catalogue and live pricing, see tensorx.ai/models, or call the List Models API below to fetch the available model IDs programmatically.

See the 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

qwen/qwen3-vl-235b-a22b-instruct, minimax/minimax-m3, moonshotai/kimi-k2.6, z-ai/glm-5v-turbo

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:

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

Response

{
  "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:

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).

View Current Pricing: tensorx.ai/models

Pricing is displayed per 1M tokens for each model.

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

Choose the right model - Use smaller models for simple tasks
Set max_tokens - Limit output length when appropriate
Use caching - Cache responses for repeated queries
Monitor usage - Check your dashboard regularly

Feature Support

Feature

Supported Models

Function Calling

DeepSeek, GLM, Qwen, GPT-OSS, MiniMax

Reasoning

GLM, DeepSeek, MiniMax, Kimi, GPT-OSS

Vision

moonshotai/kimi-k2.5, moonshotai/kimi-k2.6, moonshotai/kimi-k2.7-code, minimax/minimax-m3, qwen/qwen3-vl-235b-a22b-instruct, z-ai/glm-5v-turbo, meta-llama/llama-4-maverick

Streaming

All models

JSON Mode