| title |
aliases |
tags |
sources |
created |
updated |
| LM Studio — OpenAI Chat Completions Endpoint |
| lmstudio-openai-chat |
| lm-studio-chat-completions |
|
| lmstudio |
| openai-compat |
| local-llm |
| api |
| chat |
|
|
2026-04-30 |
2026-04-30 |
LM Studio — OpenAI Chat Completions Endpoint
LM Studio exposes an OpenAI-compatible POST /v1/chat/completions endpoint. Any client built for OpenAI can point at LM Studio with two changes: base_url and api_key.
Endpoint
| Field |
Value |
| Method |
POST |
| URL |
http://localhost:1234/v1/chat/completions |
| Auth |
any string (e.g. "lm-studio") |
- Prompt template is applied automatically for chat-tuned models
- Stream with
stream: true for token-by-token output
- Inspect actual model input with
lms log stream in a second terminal
Python Example
from openai import OpenAI
client = OpenAI(base_url="http://localhost:1234/v1", api_key="lm-studio")
completion = client.chat.completions.create(
model="model-identifier",
messages=[
{"role": "system", "content": "Always answer in rhymes."},
{"role": "user", "content": "Introduce yourself."}
],
temperature=0.7,
)
print(completion.choices[0].message)
Replace "model-identifier" with the exact model name shown in LM Studio's UI.
Supported Payload Parameters
model top_p top_k
messages temperature max_tokens
stream stop presence_penalty
frequency_penalty logit_bias repeat_penalty
seed
top_k and repeat_penalty are LM Studio extensions not in the OpenAI spec — they work here but not against the real OpenAI API.
Debugging
lms log stream # live view of what the model actually receives
Key Takeaways
- Drop-in replacement for
openai.chat.completions.create — change base_url only
api_key value is ignored; pass any non-empty string- Chat-tuned models get their prompt template applied automatically
top_k and repeat_penalty are bonus params unavailable on real OpenAI- Use
lms log stream to verify the exact prompt being sent to the model
Related
Sources
