Open AI
on Orq.ai
Use OpenAI's foundation models through a single Orq.ai API. Route models such as GPT-4.1, GPT-4.1-mini, o3-mini, and GPT-4o-class models via Orq's AI Router for chat, reasoning, coding, and multimodal workloads.
Capabilities:
Chat
Code
Image Generation
Reasoning
Speech
Vision
Models Supported:
gpt-5.5 (EU)
gpt-5.6-luna (EU)
gpt-5.6-sol (EU)
gpt-5.6-terra (EU)
chatgpt-image-latest (EU)
Provider HQ:
San Francisco, California
OpenAI models available on Orq.ai
Model | Type | Context | Best for | Pricing tier |
|---|---|---|---|---|
GPT-4.1 | Chat / reasoning / coding / multimodal | Up to around 1M tokens (see OpenAI/Orq docs for current limits) | Complex reasoning, deep analysis, coding, and high-stakes tasks where you want OpenAI’s strongest general-purpose model | Premium - standard API pricing: $2 / 1M input tokens, $8 / 1M output tokens (cached input ~$0.50 / 1M) |
GPT-4.1-mini | Chat / reasoning / coding | Long context relative to smaller models (verify in docs for current limits) | Everyday production workloads, RAG, coding, product features, and workflows that balance quality with cost and latency | Mid-tier - standard API pricing: $0.40 / 1M input tokens, $1.60 / 1M output tokens |
GPT-4.1-nano | Chat / fast / cost-efficient | Medium-long context compared to older 3.x models | Fast, lower-cost tasks such as classification, extraction, lightweight chat, routing, and high-volume support workflows | Cost-efficient - standard API pricing: ~$0.20 / 1M input tokens, ~$0.80–1.25 / 1M output tokens |
See pricing for current rates.
Why use OpenAI through Orq.ai
Capability | Provider | Direct | Through Orq.ai |
|---|---|---|---|
Chat | OpenAI chat models (GPT-4.1, GPT-4.1-mini, GPT-4.1-nano, GPT-4o-class) | Call OpenAI models directly via the OpenAI API for chat, reasoning, coding, and multimodal tasks. | Use OpenAI models through Orq.ai’s AI Router, adding routing, tracing, evals, budgets, and governance controls around each request. |
Code | OpenAI models tuned or suitable for coding | Use OpenAI directly for code generation, debugging, refactoring, and agentic coding workflows. | Route coding workloads through Orq.ai, compare OpenAI models against other providers, and monitor cost, latency, and quality from one control layer. |
Vision / multimodal | GPT-4o-class multimodal models and tools | Use OpenAI’s image and audio tools alongside GPT chat models via the native API. | Use Orq.ai to orchestrate which parts of a workflow call OpenAI (for example vision or audio steps) while other steps use alternative providers, with unified observability and cost controls. |
This gives teams a practical way to use OpenAI where it performs best while centralizing routing, observability, evals, and cost controls across the wider model stack.
Pricing
Plans and API access
OpenAI API pricing is pay-per-token, with separate rates per model and discounts for cached input and Batch/Flex options.
Illustrative ranges from current public data:
Tier / Model | Input tokens | Output tokens | Cached input |
|---|---|---|---|
Premium – GPT-4.1 | Around 2.00 USD / 1M input tokens | Around 8.00 USD / 1M output tokens | Cached input around 0.50 USD / 1M tokens |
Mid-tier – GPT-4.1-mini | Around 0.40 USD / 1M input tokens | Around 1.60 USD / 1M output tokens | Cached input around 0.10–0.20 USD / 1M tokens |
Cost-efficient – GPT-4.1-nano | Around 0.20 USD / 1M input tokens | Output in the 0.80–1.25 USD / 1M tokens range depending on latest numbers.openai+1 |
OpenAI also offers:
request-based pricing for search content on some models (for example 25 USD / 1K calls for GPT-4.1/GPT-4o search content where tokens are free, 10 USD / 1K calls for o-series and Deep Research with search tokens billed at model rates)
separate pricing for fine-tuning, images, audio, and containers
Check the Orq.ai pricing page and your workspace’s provider configuration for current per-model rates, quotas, and plan details when using OpenAI via Orq.ai.
Compatible frameworks and tools
Orq.ai exposes OpenAI through a native OpenAI provider configuration in AI Gateway / Model Garden, where you add your OpenAI API key, and an OpenAI-compatible API layer, so existing OpenAI SDK code works by only changing the base URL to Orq’s router. That means existing backend workflows that already use the OpenAI SDK or OpenAI-compatible clients can point at Orq’s base URL to route to OpenAI or other providers without changing application logic. Agents, code assistants, and tools that integrate with Orq.ai can be configured so that specific steps are served by OpenAI while others use different providers, all sharing the same observability and governance layer. Check the Orq.ai integration docs for the latest supported frameworks and tools for OpenAI.
FAQs
Do I need a separate OpenAI account to use OpenAI through Orq.ai?
You connect your own OpenAI API key into Orq.ai; in some plans, OpenAI usage billed via Orq.ai may also be available depending on region and configuration. In both cases, Orq.ai gives you one place to manage routing, observability, and cost controls around that OpenAI usage.
Can I route only some workflows to OpenAI and others to different providers?
Yes. You define routes per workflow in Orq.ai and decide which ones should use OpenAI vs other models, so you can reserve OpenAI for specific regions, compliance needs, or workloads while sending other tasks to different providers.
Does using OpenAI through Orq.ai add latency?
Orq.ai is designed as a lightweight router layer, so the added overhead is small compared to the model’s own latency. You can use routing policies, caching, and provider selection to keep end-to-end performance within your targets.
Alternatives to
Open AI
Anthropic
Use Claude Opus 4.7, Sonnet 4.6, Haiku 4.5, and other supported Claude models through one API.
Chat
Code
Reasoning
Vision
Models:
claude-opus-5
claude-sonnet-5
claude-fable-5
Google AI
Use Google’s Gemini models through a single Orq.ai API. Route models such as Gemini 3.1 Pro, Gemini 2.5 Flash, and Gemini 2.0 Flash‑Lite via Orq’s AI Router for chat, reasoning, coding, and multimodal workloads.
Chat
Code
Embeddings
Image Generation
Reasoning
Speech
Vision
Models:
gemini-3.5-flash-lite (Gemini API)
gemini-3.6-flash (Gemini API)
Gemini 3 Pro Image
AWS
Use AWS Bedrock’s foundation models through a single Orq.ai API. Route models such as Amazon Nova, Amazon Titan Text, and compatible third‑party models exposed via Bedrock through Orq’s AI Router for chat, reasoning, coding, and multimodal workloads.
Chat
Code
Embeddings
Reasoning
Vision
Models:
eu.anthropic.claude-opus-5
global.anthropic.claude-opus-5
us.anthropic.claude-opus-5
Z.ai
Use Z.ai’s GLM‑5 family through a single Orq.ai integration. Route models such as GLM‑5.2, GLM‑5.1, GLM‑5, GLM‑5‑Turbo, GLM‑4.7, and GLM‑4.7‑FlashX via Orq’s AI Router for chat, reasoning, coding, multilingual tasks, vision, and cost‑efficient high‑volume workloads.
Chat
Image Generation
Reasoning
Vision
Models:
glm-5.2
glm-5.1
glm-5v-turbo
