Inference Providers documentation
Baseten
Get Started
Guides
Your First API CallBuilding Your First AI AppStructured Outputs with LLMsFunction CallingResponses API (beta)How to use OpenAI gpt-ossBuild an Image EditorAutomating Code Review with GitHub ActionsAgentic Coding Environments with OpenEnvEvaluating Models with Inspect
Integrations
OverviewPiOpenCodeCodexClaude CodeHermes AgentNeMo Data DesignerMacWhisperVision AgentsVS Code with GitHub CopilotAdd Your Integration
Inference Tasks
Providers
BasetenCerebrasCohereDeepInfraFal AIFeatherless AIFireworksGroqHF InferenceNovitaNscaleOVHcloud AI EndpointsPublic AIReplicateScalewayTogetherWaveSpeedAIZ.ai
Hub APIRegister as an Inference ProviderBaseten
All supported Baseten models can be found here
Baseten provides on-demand frontier model APIs designed for production applications, not just experimentation. Built on the Baseten Inference Stack, these APIs deliver enterprise-grade performance and reliability with optimized inference for leading open-source models.
Supported tasks
Chat Completion (LLM)
Find out more about Chat Completion (LLM) here.
Language
Client
Provider
Copied
import os
from openai import OpenAI
client = OpenAI(
base_url="https://router.huggingface.co/v1",
api_key=os.environ["HF_TOKEN"],
)
completion = client.chat.completions.create(
model="deepseek-ai/DeepSeek-V4-Flash-0731:baseten",
messages=[
{
"role": "user",
"content": "What is the capital of France?"
}
],
)
print(completion.choices[0].message)Chat Completion (VLM)
Find out more about Chat Completion (VLM) here.
Language
Client
Provider
Copied
import os
from openai import OpenAI
client = OpenAI(
base_url="https://router.huggingface.co/v1",
api_key=os.environ["HF_TOKEN"],
)
completion = client.chat.completions.create(
model="moonshotai/Kimi-K3:baseten",
messages=[
{
"role": "user",
"content": [
{
"type": "text",
"text": "Describe this image in one sentence."
},
{
"type": "image_url",
"image_url": {
"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
}
}
]
}
],
)
print(completion.choices[0].message)
