Nebius AI Studio

https://docs.nebius.com/studio/inference/quickstart

tip

**Litellm provides support to all models from Nebius AI Studio. To use a model, set model=nebius/<any-model-on-nebius-ai-studio> as a prefix for litellm requests. The full list of supported models is provided at https://studio.nebius.ai/ **

API Key

import os
# env variable
os.environ['NEBIUS_API_KEY']

Sample Usage: Text Generation

from litellm import completion
import os

os.environ['NEBIUS_API_KEY'] = "insert-your-nebius-ai-studio-api-key"
response = completion(
    model="nebius/Qwen/Qwen3-235B-A22B",
    messages=[
        {
            "role": "user",
            "content": "What character was Wall-e in love with?",
        }
    ],
    max_tokens=10,
    response_format={ "type": "json_object" },
    seed=123,
    stop=["\n\n"],
    temperature=0.6,  # either set temperature or `top_p`
    top_p=0.01,  # to get as deterministic results as possible
    tool_choice="auto",
    tools=[],
    user="user",
)
print(response)

Sample Usage - Streaming

from litellm import completion
import os

os.environ['NEBIUS_API_KEY'] = ""
response = completion(
    model="nebius/Qwen/Qwen3-235B-A22B",
    messages=[
        {
            "role": "user",
            "content": "What character was Wall-e in love with?",
        }
    ],
    stream=True,
    max_tokens=10,
    response_format={ "type": "json_object" },
    seed=123,
    stop=["\n\n"],
    temperature=0.6,  # either set temperature or `top_p`
    top_p=0.01,  # to get as deterministic results as possible
    tool_choice="auto",
    tools=[],
    user="user",
)

for chunk in response:
    print(chunk)

Sample Usage - Embedding

from litellm import embedding
import os

os.environ['NEBIUS_API_KEY'] = ""
response = embedding(
    model="nebius/BAAI/bge-en-icl",
    input=["What character was Wall-e in love with?"],
)
print(response)

Usage with LiteLLM Proxy Server

Here's how to call a Nebius AI Studio model with the LiteLLM Proxy Server

1. Modify the config.yaml

model_list:
  - model_name: my-model
    litellm_params:
      model: nebius/<your-model-name>  # add nebius/ prefix to use Nebius AI Studio as provider
      api_key: api-key                 # api key to send your model

2. Start the proxy

$ litellm --config /path/to/config.yaml

3. Send Request to LiteLLM Proxy Server

OpenAI Python v1.0.0+

import openai
client = openai.OpenAI(
    api_key="litellm-proxy-key",             # pass litellm proxy key, if you're using virtual keys
    base_url="http://0.0.0.0:4000" # litellm-proxy-base url
)

response = client.chat.completions.create(
    model="my-model",
    messages = [
        {
            "role": "user",
            "content": "What character was Wall-e in love with?"
        }
    ],
)

print(response)

curl

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Authorization: litellm-proxy-key' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "my-model",
    "messages": [
        {
        "role": "user",
        "content": "What character was Wall-e in love with?"
        }
    ],
}'

Supported Parameters

The Nebius provider supports the following parameters:

Chat Completion Parameters

Parameter	Type	Description
frequency_penalty	number	Penalizes new tokens based on their frequency in the text
function_call	string/object	Controls how the model calls functions
functions	array	List of functions for which the model may generate JSON inputs
logit_bias	map	Modifies the likelihood of specified tokens
max_tokens	integer	Maximum number of tokens to generate
n	integer	Number of completions to generate
presence_penalty	number	Penalizes tokens based on if they appear in the text so far
response_format	object	Format of the response, e.g., {"type": "json"}
seed	integer	Sampling seed for deterministic results
stop	string/array	Sequences where the API will stop generating tokens
stream	boolean	Whether to stream the response
temperature	number	Controls randomness (0-2)
top_p	number	Controls nucleus sampling
tool_choice	string/object	Controls which (if any) function to call
tools	array	List of tools the model can use
user	string	User identifier

Embedding Parameters

Parameter	Type	Description
input	string/array	Text to embed
user	string	User identifier

Error Handling

The integration uses the standard LiteLLM error handling. Common errors include:

• Authentication Error: Check your API key
• Model Not Found: Ensure you're using a valid model name
• Rate Limit Error: You've exceeded your rate limits
• Timeout Error: Request took too long to complete