Chat Completions

Create a model response for the given chat conversation.

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Endpoint

POST https://api.tensorx.ai/v1/chat/completions

---

Authentication

Authorization: Bearer YOUR_API_KEY

API key is required for all requests. Get your key from your TensorX dashboard.

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Request Body

Parameter	Type	Required	Default	Description
model	string	Yes	-	Model ID (e.g., deepseek/deepseek-chat-v3.1)
messages	array	Yes	-	Array of message objects
max_tokens	integer	No	Model default	Maximum tokens to generate
temperature	number	No	1.0	Sampling temperature (0-2)
top_p	number	No	1.0	Nucleus sampling (0-1)
top_k	integer	No	-	Top-k sampling
stream	boolean	No	false	Stream partial responses
stop	string/array	No	-	Stop sequences (up to 4)
presence_penalty	number	No	0	Presence penalty (-2 to 2)
frequency_penalty	number	No	0	Frequency penalty (-2 to 2)
response_format	object	No	-	Force output format
tools	array	No	-	Function calling tools

Message Object

Field	Type	Required	Description
role	string	Yes	system, user, or assistant
content	string	Yes	Message content

---

Example Request

cURL

curl https://api.tensorx.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TENSORX_API_KEY" \
  -d '{
    "model": "deepseek/deepseek-chat-v3.1",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "What is the capital of France?"}
    ],
    "max_tokens": 1000,
    "temperature": 0.7
  }'

Python

from openai import OpenAI

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

response = client.chat.completions.create(
    model="deepseek/deepseek-chat-v3.1",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of France?"}
    ],
    max_tokens=1000,
    temperature=0.7
)

print(response.choices[0].message.content)

JavaScript

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'your-tensorx-api-key',
  baseURL: 'https://api.tensorx.ai/v1'
});

const response = await client.chat.completions.create({
  model: 'deepseek/deepseek-chat-v3.1',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'What is the capital of France?' }
  ],
  max_tokens: 1000,
  temperature: 0.7
});

console.log(response.choices[0].message.content);

---

Response

Success (200)

{
  "id": "chatcmpl-abc123xyz",
  "object": "chat.completion",
  "created": 1706745600,
  "model": "deepseek/deepseek-chat-v3.1",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The capital of France is Paris."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 25,
    "completion_tokens": 8,
    "total_tokens": 33
  }
}

Response Fields

Field	Type	Description
id	string	Unique response identifier
object	string	Always chat.completion
created	integer	Unix timestamp
model	string	Model used for generation
choices	array	Generated completions
choices[].message	object	Assistant's response message
choices[].finish_reason	string	Why generation stopped (stop, length, tool_calls)
usage	object	Token usage statistics

---

Streaming

Enable streaming for real-time responses:

curl https://api.tensorx.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TENSORX_API_KEY" \
  -d '{
    "model": "deepseek/deepseek-chat-v3.1",
    "messages": [{"role": "user", "content": "Tell me a story"}],
    "stream": true
  }'

Streaming responses are sent as Server-Sent Events (SSE):

data: {"id":"chatcmpl-123","object":"chat.completion.chunk","choices":[{"delta":{"content":"Once"}}]}

data: {"id":"chatcmpl-123","object":"chat.completion.chunk","choices":[{"delta":{"content":" upon"}}]}

data: {"id":"chatcmpl-123","object":"chat.completion.chunk","choices":[{"delta":{"content":" a"}}]}

data: [DONE]

Python Streaming

from openai import OpenAI

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

stream = client.chat.completions.create(
    model="deepseek/deepseek-chat-v3.1",
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

---

JSON Mode

Force the model to output valid JSON:

curl https://api.tensorx.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TENSORX_API_KEY" \
  -d '{
    "model": "deepseek/deepseek-chat-v3.1",
    "messages": [
      {"role": "user", "content": "List 3 countries and their capitals as JSON"}
    ],
    "response_format": {"type": "json_object"}
  }'

When using JSON mode, include "JSON" in your prompt for best results.

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Reasoning

Several models expose a separate reasoning (thinking) step, returned in reasoning_content on the message. Controls differ by model family. See Reasoning for the full per-model reference.

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Function Calling

Use tools for function calling:

from openai import OpenAI

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

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather in a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City name"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="deepseek/deepseek-chat-v3.1",
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
    tools=tools
)

---

Error Responses

401 Unauthorized

{
  "error": {
    "message": "Invalid API key",
    "type": "authentication_error",
    "code": "invalid_api_key"
  }
}

402 Insufficient Credits

{
  "error": {
    "message": "Insufficient credits",
    "type": "payment_error",
    "code": "insufficient_credits"
  }
}

429 Rate Limited

{
  "error": {
    "message": "Rate limit exceeded",
    "type": "rate_limit_error",
    "code": "rate_limit_exceeded"
  }
}

---

Best Practices

1. Use streaming for long responses to improve user experience
2. Set appropriate max_tokens to control costs and response length
3. Use lower temperature (0.1-0.3) for factual tasks, higher (0.7-1.0) for creative tasks
4. Include system prompts to guide model behavior
5. Handle errors gracefully with retry logic for transient failures

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See Also

• Models - Available models
• API Examples - More code examples
• Pricing - Model pricing