Models

Enabling new Models

To see available Models and enable them for use, navigate to the Models page in AI Gateway.

Models page listing available LLM models with columns for name, input and output pricing, feature badges, release date, max output tokens, context length, and location. — The Models page showing available models with pricing, features, and metadata columns.

Each model displays its full name alongside a set of sortable columns:

Name: full model name and provider
Input / Output pricing: per-token cost for input and output
Features: capability badges indicating support for ZDR, BYOK, and other model-specific features
Released: the model’s release date
Max Output Tokens: maximum tokens the model can generate per response
Context Length: total token window (input + output)
Location: the region where the model is served

Use Sort: Newest to reorder by Newest, Pricing (low to high or high to low), Context (low to high or high to low), or Max Output Tokens. Use Columns to show or hide individual columns.

Use the Status Toggle to Enable a model for use with the AI Gateway.

Filters

Use the modality tabs at the top of the list to scope models by type: All Text Image Audio Speech Embedding Moderation Rerank The sidebar provides additional filters:

Filter	Description
Location	Filter by region: Europe, United States, Global, APAC, Australia, Singapore
Access	Toggle Zero data retention for ZDR-compliant providers, or BYOK for providers where an API key has been added
Providers	Filter by LLM provider. See Providers to configure API keys
Status	Show Enabled or Disabled models
Features	Filter by capability: Base64, Code Execution, Image Edit, JSON Mode, PDF, Reasoning, Streaming, Tool Calling, URL, Vision, Web Search
Context length	Drag the range slider to filter by context window size (512 to 2M tokens)
Owner	Filter between Public (Orq.ai-provided) and Private (onboarded) models

To enable a model, toggle it on. It will immediately be available to call with the AI Gateway.

Enable or Disable Models via the API

Models can also be enabled and disabled programmatically using the Models API. This is useful for CI/CD pipelines, automation scripts, or infrastructure-as-code workflows. These endpoints sit on the management plane and authenticate with a Management Key that has workspace-model write access. A standard API Key cannot be granted the workspace-model domain and is rejected with 403. Enable a model:

curl --request POST \
  --url https://my.orq.ai/v2/workspace-models \
  --header "Authorization: Bearer $ORQ_MANAGEMENT_KEY" \
  --header "Content-Type: application/json" \
  --data '{ "model_id": "openai/gpt-5.6-sol" }'

import { Orq } from "@orq-ai/node";

const orq = new Orq({ apiKey: process.env.ORQ_MANAGEMENT_KEY });

await orq.models.enable({ modelId: "openai/gpt-5.6-sol" });

from orq_ai_sdk import Orq
import os

orq = Orq(api_key=os.environ["ORQ_MANAGEMENT_KEY"])

orq.models.enable(model_id="openai/gpt-5.6-sol")

Disable a model:

curl --request DELETE \
  --url https://my.orq.ai/v2/workspace-models/openai/gpt-5.6-sol \
  --header "Authorization: Bearer $ORQ_MANAGEMENT_KEY"

import { Orq } from "@orq-ai/node";

const orq = new Orq({ apiKey: process.env.ORQ_MANAGEMENT_KEY });

await orq.models.disable({ modelId: "openai/gpt-5.6-sol" });

from orq_ai_sdk import Orq
import os

orq = Orq(api_key=os.environ["ORQ_MANAGEMENT_KEY"])

orq.models.disable(model_id="openai/gpt-5.6-sol")

Both endpoints return 204 on success. Re-enabling an already-enabled model or disabling an already-disabled model is idempotent and also returns 204. When Enforce enabled models is turned on in General Settings, only models enabled through the dashboard or this API are available for routing. Requests that reference a non-enabled model are rejected. For full request and response schemas, see Enable model for workspace and Disable model for workspace.

Restrict Model Access by Project

Once a model is enabled, workspace admins can see an Access control icon next to it. Select it to choose which projects can use the model, in one of two modes:

All projects (default): every project in the workspace can use the model.
Custom: each project gets its own on/off toggle, letting admins grant or revoke access per project.

Changes save immediately; there is no separate save action.

Access control is admin-only. Members with other roles see only the models an admin has approved within their chosen projects.

Onboarding Private Models

Onboard private models by choosing Model at the top-right of the screen. This is useful when hosting a fine-tuned model or any model deployed on a private provider such as an OpenAI-compatible endpoint, Azure AI Foundry, or AWS Bedrock.

Private Models Providers

Show OpenAI Compatible

In the AI Gateway sidebar, go to Models, then click Model at the top-right and select OpenAI Compatible.

Set the model type

Choose Chat Completion, Completion, Embedding, or Image.

Connect the endpoint

Enter the Base URL of the OpenAI-compatible endpoint, the Model ID the provider defines, and an API Key.

Identify the model

Set the Model name the gateway exposes, then fill in the token limits, per-token costs, and capabilities.

For the full field reference, see OpenAI-compatible models.

Show Azure AI Foundry

From the Azure AI Foundry project homepage, copy the API key and one of the two endpoints shown at the top. Orq.ai accepts the following endpoints:

Endpoint type	Format	What it imports
Azure OpenAI endpoint	`https://<resource>.openai.azure.com/openai/v1`	OpenAI-compatible deployments
Project endpoint	`https://<resource>.services.ai.azure.com/api/projects/<project>`	All deployments from publishers in the project (Anthropic, Cohere, and xAI)

Paste the endpoint URL exactly as shown in Azure AI Foundry. Orq.ai does not append any path suffix.

To learn more about the Azure AI Foundry deployment, see our Provider Documentation.

Open Add Model

In the AI Gateway sidebar, go to Models, then click Model at the top-right and select Azure.

Enter credentials

Enter the Base URL and API Key.

Azure AI Foundry credentials panel showing the API key, Project endpoint, and Azure OpenAI endpoint at the top of the project homepage.

Azure private model configuration form showing endpoint URL and API Key fields.

Fetch deployments

Click Fetch deployments to automatically import all available deployments. The imported models appear in the Models list. Toggle each model Enabled before use. Enabled models are available for routing requests through Routing Rules.

Reference imported models in code using <workspacename>@azure/<modelname> (see Referencing Private Models in Code).

Show AWS Bedrock

To add Bedrock models, first create an Integration for the AWS account.

Open Add Model

In the AI Gateway sidebar, go to Models, click Model at the top-right and select AWS Bedrock.

Choose the model

Select the AWS Integration, enter the Inference Profile ARN, and confirm the Region parsed from the ARN.

Validate the connection

Click Validate Connection, then enter a display name and the model details.

Show LiteLLM

To import LiteLLM models, first create an Integration for the LiteLLM instance. After creation, return to the AI Gateway and import models from the connected instance.

LiteLLM model import screen showing how to select and import models from a connected LiteLLM instance.

Referencing Private Models in Code

When referencing private models through the SDKs, API, or Supported Libraries, the model is referenced by the following string: <workspacename>@<provider>/<modelname>.

Example: corp@azure/gpt-5.6-sol

Bring Your Own Key (BYOK)

To start using models, connect provider API keys via BYOK in the AI Gateway sidebar.

Overview

APIs

MCP Portal

Routing

Security & Privacy

Model Capabilities

Server Tools

Common Scenarios

Enabling new Models

Filters

Enable or Disable Models via the API

Restrict Model Access by Project

Onboarding Private Models

Private Models Providers

Referencing Private Models in Code

Bring Your Own Key (BYOK)

​Enabling new Models

​Filters

​Enable or Disable Models via the API

​Restrict Model Access by Project

​Onboarding Private Models

​Private Models Providers

​Referencing Private Models in Code

​Bring Your Own Key (BYOK)

Enabling new Models

Filters

Enable or Disable Models via the API

Restrict Model Access by Project

Onboarding Private Models

Private Models Providers

Referencing Private Models in Code

Bring Your Own Key (BYOK)