Cerebras
on Orq.ai
Access Cerebras-hosted AI models through Orq.ai for high-throughput reasoning, coding, chat, and other inference-heavy workloads through one API.
Capabilities:
Chat
Reasoning
Vision
Code
Models Supported:
cerebras/gemma-4-31b
zai-glm-4.7
cerebras/gpt-oss-120b
...
Provider HQ:
Sunnyvale, California
Access Cerebras through Orq.ai’s AI Router
Cerebras provides an inference cloud built around wafer-scale systems designed for high-throughput model serving. Its platform is suited to workloads where fast token generation, low response times, and efficient serving are important.
Cerebras models available on Orq.ai
Compare the Cerebras-hosted models currently available through Orq.ai, including their supported capabilities, context windows, pricing, and availability.
Model
Type
Context
Input / 1M
Output / 1M
cerebras/gemma-4-31b
chat
Reasoning
Vision
131K
$0.99
$1.49
zai-glm-4.7
chat
Reasoning
131K
$2.25
$2.75
cerebras/gpt-oss-120b
chat
131K
$0.35
$0.75
Why use Cerebras through Orq.ai?
Using Cerebras through Orq.ai lets teams add high-throughput inference to a wider model stack without maintaining separate routing, evaluation, observability, and cost-control logic for each provider.
Capability | Provider | Direct | Through Orq.ai |
|---|---|---|---|
Chat | Models served through Cerebras | Call supported models directly through the Cerebras API for chat, reasoning, coding, and other supported workloads. | Use Cerebras-hosted models through Orq.ai’s OpenAI-compatible endpoint while applying routing, tracing, evals, budgets, and governance controls around each request. |
Code | Models served through Cerebras | Use supported models for code generation, debugging, refactoring, and agentic coding workflows. | Route coding workloads through Orq.ai, compare Cerebras with other providers, and monitor cost, latency, and quality from a shared control layer. |
Embeddings | Supported embedding models and providers | Use supported embedding models where available through your provider configuration. | Route embedding workloads alongside reasoning, generation, and agent workflows through the same Orq.ai platform. |
Using Cerebras through Orq.ai lets teams add high-throughput inference to a wider model stack without maintaining separate routing, evaluation, observability, and cost-control logic for each provider.
Pricing
Cerebras model pricing varies by model, provider configuration, and billing setup.
You can connect supported Cerebras credentials to Orq.ai or use other available access options depending on your workspace configuration. Check Orq.ai and your Cerebras setup for current per-model rates, quotas, and billing details.
Compatible frameworks and tools
Orq.ai works with OpenAI-compatible clients and common AI development frameworks. Tools that support compatible APIs or supported integration standards can also connect through Orq.ai where available.
Check the Orq.ai integration documentation for the latest setup options and compatibility information for Cerebras.
FAQs
Do I need a separate Cerebras account to use Cerebras through Orq.ai?
You can connect supported Cerebras credentials to Orq.ai or use other available access options depending on your workspace, plan, and region.
Can I route only some workflows to Cerebras and others to different providers?
Yes. Orq.ai lets you route different workloads to different providers, so you can use Cerebras for workloads where throughput, latency, or provider availability fit your requirements while routing other requests elsewhere.
Does using Cerebras through Orq.ai add latency?
Orq.ai adds a routing layer between your application and the model provider. Teams can monitor end-to-end latency and use routing, caching, and provider controls where appropriate to manage performance.
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Cerebras
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