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Platform / Inference

Two ways to run inference,
one compatible API

Call a Platform model that OneNexus already hosts, or deploy a self-hosted model your organisation owns.

01 — Platform models

Call a Platform model

The shared catalog speaks the OpenAI API, so your client and your code stay as they are. Only three things change: the base URL, a Platform API key, and the model value.

python

import os

from openai import OpenAI

client = OpenAI(
    base_url=os.environ["ONX_BASE_URL"],
    api_key=os.environ["ONX_API_KEY"],
)

resp = client.chat.completions.create(
    model="glm-5.3",
    messages=[{"role": "user", "content": "Refactor this module"}],
)

print(resp.choices[0].message.content)

curl

curl $ONX_BASE_URL/chat/completions \
  -H "Authorization: Bearer $ONX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "glm-5.3",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

# Base URL and API keys both come from the console

ONX_BASE_URL → https://catalog.onenexus-do.cloud/v1

Follow the Platform model quickstart.

02 — Models

Hosted models today

GLM-5.3

Available in pilot

Long-horizon work, extensive reasoning, and agentic projects. Can be effectively used with smaller model subagents for project-scale workloads.

Context window

500K as configured

Limits and capacity are confirmed during onboarding.

API format

OpenAI

compatible endpoints

Tool callingStreamingJSON Schema output

DeepSeek-V4-Flash

Available in pilot

Fast and efficient everyday tasks.

Context window

500K as configured

Limits and capacity are confirmed during onboarding.

API format

OpenAI

compatible endpoints

Tool callingStreamingJSON Schema output

Model availability, context limits, and capacity are confirmed during pilot setup.

03 — Self-hosted models

Two ways to run a model your organisation owns

Train a model on the platform, or bring weights you already have. Either path serves from your own endpoint, and needs inference GPU quota.

Trained on the platform

Train a model with OneNexus Training, then deploy the result to your organisation without moving it off the platform.

Explore OneNexus Training

Weights you bring

Deploy public model weights, or a custom checkpoint your organisation already owns, behind an OpenAI-compatible endpoint.

04 — Operating controls

The controls around the model

The operational pieces around every model call.

Workload setup

Agree users, access, and boundaries before service is enabled.

Usage and quotas

Track usage by team and key, with enforceable limits.

Usage visibility

Review request and token usage by model in the Console.

Explicit model selection

The model you specify is the model that runs.

Start with either path

The shared catalog needs only a Platform API key. Self-hosting needs inference GPU quota, and gives you a model your organisation owns.