Chat Completions.

Endpoint

POST /v1/chat/completions

Authenticate with Authorization: Bearer lx1_.... Any model in the catalog is valid in model.

Request parameters

Parameter	Type	Notes
`model`	string · required	Any catalog id, e.g. `lx1-gpt-oss-120b`.
`messages`	array · required	Chat history. Content can be a string or content parts; image parts (`image_url`, data URLs included) are accepted on vision models.
`max_tokens`	integer	Output ceiling. On reasoning models, hidden reasoning counts against it — budget generously.
`temperature`	number	Sampling temperature.
`top_p`	number	Nucleus sampling.
`stop`	string \| string[]	Stop sequences.
`stream`	boolean	SSE chunks when `true`. See Streaming.
`stream_options`	object	`{ "include_usage": true }` appends a final usage chunk.
`tools`	array	Function tools. See Tool calling.
`tool_choice`	string \| object	`auto`, `none`, `required`, or a named function.
`response_format`	object	JSON mode / `json_schema`. See Structured output.
`reasoning_effort`	string	Opt into visible reasoning and set the level. Returned on `choices[].message.reasoning_content`. See Reasoning.
`user`	string	A conversation-stable identity hint used for cache affinity. See Prompt caching.

Example

curl https://api.layerx1.com/v1/chat/completions \
  -H "authorization: Bearer $LAYERX1_API_KEY" \
  -H "content-type: application/json" \
  -d '{
    "model": "lx1-gpt-oss-120b",
    "messages": [
      { "role": "system", "content": "You are concise." },
      { "role": "user", "content": "Say hi." }
    ]
  }'

from openai import OpenAI

client = OpenAI(
    base_url="https://api.layerx1.com/v1",
    api_key="lx1_your_key",
)

r = client.chat.completions.create(
    model="lx1-gpt-oss-120b",
    messages=[
        {"role": "system", "content": "You are concise."},
        {"role": "user", "content": "Say hi."},
    ],
)
print(r.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.layerx1.com/v1",
  apiKey: process.env.LAYERX1_API_KEY,
});

const r = await client.chat.completions.create({
  model: "lx1-gpt-oss-120b",
  messages: [
    { role: "system", content: "You are concise." },
    { role: "user", content: "Say hi." },
  ],
});
console.log(r.choices[0].message.content);

Response (shape)

{
  "id": "chatcmpl_...",
  "object": "chat.completion",
  "model": "lx1-gpt-oss-120b",
  "choices": [{
    "index": 0,
    "message": { "role": "assistant", "content": "Hi." },
    "finish_reason": "stop"
  }],
  "usage": { "prompt_tokens": 14, "completion_tokens": 3, "total_tokens": 17 }
}

Response fields

Field	Notes
`choices[].message`	The assistant turn — `content`, and `tool_calls` when the model calls a tool.
`choices[].finish_reason`	`stop`, `length` (hit `max_tokens`), or `tool_calls`.
`usage`	Prompt, completion, and total token counts for the request.

Streaming

With stream: true the response is Server-Sent Events: incremental chat.completion.chunk objects, terminated by data: [DONE].

curl -N https://api.layerx1.com/v1/chat/completions \
  -H "authorization: Bearer $LAYERX1_API_KEY" \
  -H "content-type: application/json" \
  -d '{
    "model": "lx1-gpt-oss-120b",
    "stream": true,
    "stream_options": { "include_usage": true },
    "messages": [{ "role": "user", "content": "Count to five." }]
  }'

stream = client.chat.completions.create(
    model="lx1-gpt-oss-120b",
    messages=[{"role": "user", "content": "Count to five."}],
    stream=True,
)
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

const stream = await client.chat.completions.create({
  model: "lx1-gpt-oss-120b",
  messages: [{ role: "user", content: "Count to five." }],
  stream: true,
});
for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
}

Errors

Failures return standard status codes with OpenAI-shaped bodies — match on the status, retry on 429/5xx with backoff. Full reference: Errors.

Requesting vision input on a text-only model is a 400. Image input is a short list — check capabilities.vision on GET /v1/models before you send. See Vision.

Get started

API reference

Agent-native API

Guides

Reference

Chat Completions.

Endpoint

Request parameters

Example

Response fields

Streaming

Errors

​Endpoint

​Request parameters

​Example

​Response fields

​Streaming

​Errors

Endpoint

Request parameters

Example

Response fields

Streaming

Errors