Mistral Small 3.1

Try Mistral Small 3.1 in the Workbench

Run this model interactively, tune parameters, and compare outputs.

Model ID: mistral-small-2503 Mistral Small 3.1 is a 24 billion parameter Multimodal LLM designed for a wide range of generative AI tasks. It excels in instruction following, conversational assistance, image understanding, and function calling, while being lightweight enough to run on a single RTX 4090 or a Mac with 32GB RAM when quantized. Some other noteworthy features of Mistral Small 3.1 include fast-response conversational assistance, low-latency function calling, and the ability to be fine-tuned for specialized domains such as legal advice, medical diagnostics, and technical support.

Metric	Value
Parameter Count	24 billion
Mixture of Experts	No
Context Length	128,000 tokens
Multilingual	Yes
Quantized*	Yes
Precision*	Unknown

*Quantization is specific to the inference provider and the model may be offered with different quantization levels by other providers.

Example request

Use the Workbench as a request builder: configure parameters for this model in the UI, then open the API tab to copy the exact cURL or Python call.

Minimal
Basic parameters
All parameters

curl -X POST https://hub.oxen.ai/api/ai/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OXEN_API_KEY" \
  -d '{
  "model": "mistral-small-2503",
  "messages": [
    {
      "role": "user",
      "content": "Hello, what can you do?"
    }
  ]
}'

import os
import requests

response = requests.post(
    "https://hub.oxen.ai/api/ai/chat/completions",
    headers={
        "Content-Type": "application/json",
        "Authorization": f"Bearer {os.environ['OXEN_API_KEY']}",
    },
    json={
        "model": "mistral-small-2503",
        "messages": [
            {
                "role": "user",
                "content": "Hello, what can you do?"
            }
        ]
    },
)
response.raise_for_status()
print(response.json())

curl -X POST https://hub.oxen.ai/api/ai/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OXEN_API_KEY" \
  -d '{
  "model": "mistral-small-2503",
  "messages": [
    {
      "role": "user",
      "content": "Hello, what can you do?"
    }
  ],
  "temperature": 0.7,
  "max_tokens": 1024,
  "stream": false
}'

import os
import requests

response = requests.post(
    "https://hub.oxen.ai/api/ai/chat/completions",
    headers={
        "Content-Type": "application/json",
        "Authorization": f"Bearer {os.environ['OXEN_API_KEY']}",
    },
    json={
        "model": "mistral-small-2503",
        "messages": [
            {
                "role": "user",
                "content": "Hello, what can you do?"
            }
        ],
        "temperature": 0.7,
        "max_tokens": 1024,
        "stream": false
    },
)
response.raise_for_status()
print(response.json())

curl -X POST https://hub.oxen.ai/api/ai/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OXEN_API_KEY" \
  -d '{
  "model": "mistral-small-2503",
  "messages": [
    {
      "role": "user",
      "content": "Hello, what can you do?"
    }
  ],
  "temperature": 0.7,
  "max_tokens": 1024,
  "stream": false,
  "top_p": 1.0
}'

import os
import requests

response = requests.post(
    "https://hub.oxen.ai/api/ai/chat/completions",
    headers={
        "Content-Type": "application/json",
        "Authorization": f"Bearer {os.environ['OXEN_API_KEY']}",
    },
    json={
        "model": "mistral-small-2503",
        "messages": [
            {
                "role": "user",
                "content": "Hello, what can you do?"
            }
        ],
        "temperature": 0.7,
        "max_tokens": 1024,
        "stream": false,
        "top_p": 1.0
    },
)
response.raise_for_status()
print(response.json())

Fetch model details

The models endpoint returns the full model object, including its json_request_schema.

curl -H "Authorization: Bearer $OXEN_API_KEY" https://hub.oxen.ai/api/ai/models/mistral-small-2503

Request parameters

This model follows the standard OpenAI chat completions request body. See the chat completions reference for the full parameter list.

Try Mistral Small 3.1 in the Workbench

​Example request

​Fetch model details

​Request parameters

Example request

Fetch model details

Request parameters