TensorX
on Orq.ai
Access models hosted through TensorX using Orq.ai for reasoning, coding, multimodal, and privacy-sensitive AI workloads through one API.
Capabilities:
Chat
Reasoning
Code
Vision
Models Supported:
GLM-5.1
GLM-5.2
Kimi-K2.7-Code
DeepSeek-V4-Flash
DeepSeek-V4-Pro
GLM-4.7
GLM-5-Turbo
GLM-5V-Turbo
MiniMax-M3
Qwen3-VL-235B-A22B-Instruct
...
Provider HQ:
Dublin, Ireland
Access TensorX through Orq.ai’s AI Router
TensorX is an EU-focused inference provider for open and open-weight AI models. Its platform provides OpenAI-compatible access to models for reasoning, coding, multimodal applications, embeddings, speech, and other AI workloads, with an emphasis on privacy-conscious infrastructure.
TensorX models available on Orq.ai
Compare the TensorX-hosted models currently available through Orq.ai, including their supported capabilities, context windows, pricing, and regional availability.
Model
Type
Context
Input / 1M
Output / 1M
GLM-5.1
chat
198K
$1.40
$4.40
GLM-5.2
chat
Reasoning
1M
$1.50
$4.50
Kimi-K2.7-Code
chat
Reasoning
Vision
Video
256K
$1.25
$4.50
Why use TensorX through Orq.ai?
Using TensorX through Orq.ai lets teams add EU-hosted model 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 & reasoning | Models hosted through TensorX | Call supported models directly through TensorX for chat, reasoning, generation, and other language-model workloads. | Route TensorX-hosted models through Orq.ai while applying routing, tracing, evals, budgets, and governance controls around each request. |
Code | Models hosted through TensorX | Use supported models for code generation, debugging, refactoring, and agentic coding workflows. | Route coding workloads through Orq.ai, compare TensorX with other providers, and monitor cost, latency, and quality from a shared control layer. |
Multimodal & specialized workloads | Supported TensorX models | Use supported models for embeddings, speech, vision, and other specialized workloads where available. | Combine these workloads with other models and providers while keeping observability, evaluations, and governance in the same platform. |
Pricing
TensorX pricing varies by model, capability, usage volume, and billing configuration.
Different models can have separate input and output rates, while other capabilities may use different billing units. You can connect supported TensorX credentials to Orq.ai or use other available access options depending on your workspace configuration.
Check Orq.ai and your TensorX setup for current rates, quotas, and billing details.
Compatible frameworks and tools
Orq.ai works with OpenAI-compatible clients and common AI development frameworks. Supported TensorX models can be incorporated into Orq.ai workflows depending on the model, endpoint, and integration configuration.
Check the Orq.ai integration documentation for the latest setup options and compatibility information for TensorX.
FAQs
Do I need a separate TensorX account to use TensorX through Orq.ai?
You can connect supported TensorX credentials to Orq.ai or use other available access options depending on your workspace, plan, and region.
Can I route only some workflows to TensorX and others to different providers?
Yes. Orq.ai lets you route different workloads to different providers, so you can use TensorX where regional hosting, privacy requirements, cost, or model availability fit the workload while routing other requests elsewhere.
Does using TensorX 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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