Inceptron
on Orq.ai
Use Inceptron’s optimized open‑model endpoints through a single Orq.ai API. Route models such as Kimi‑K2.6, GLM‑5.1, MiniMax‑M2.5, and Llama 3.3 70B Instruct via Orq’s AI Router for high‑performance chat, reasoning, coding, and long‑context workloads.
Capabilities:
Chat
Reasoning
Vision
Models Supported:
Kimi-K2.7-Code
GLM-5.2
Kimi-K2.6
MiniMaxAI/MiniMax-M2.5
Provider HQ:
Inceptron AB, headquartered in Lund, Skåne, Sweden.
Access Inceptron through Orq.ai’s AI Router
Inceptron is a European AI infrastructure provider focused on compiler‑accelerated, high‑efficiency LLM serving, offering serverless endpoints, batched inference, quantized variants, and “bring your own model” (BYOM) support. These deployments cover high‑end reasoning, general‑purpose coding, and cost‑efficient high‑volume use cases.
H Company models available on Orq.ai
Model | Type | Context / scope | Best for | Pricing tier |
|---|---|---|---|---|
GLM‑5.1 (Inceptron‑optimized) | Chat / reasoning / coding | Around 200K+ tokens (check Inceptron / Orq docs) | Complex reasoning, analysis, coding, and high‑stakes tasks where you want a strong GLM‑class model hosted with Inceptron’s optimizations | Premium – GLM‑5.1 typically around 1.00–1.40 USD / 1M input tokens and 4.00–4.40 USD / 1M output tokens depending on region and FP8/quantized variant |
Kimi‑K2.6 (Inceptron‑optimized) | Chat / reasoning / long context | Up to ~262K tokens (verify in docs) | Long‑context workloads, RAG, coding, and product features that need large memory while balancing quality and price | Mid‑tier – Kimi‑K2.6 around 0.73–0.80 USD / 1M input tokens and 3.50–4.00 USD / 1M output tokens, with discounted cache reads |
MiniMax‑M2.5 / Llama 3.3 70B Instruct | Chat / fast / cost‑efficient | Roughly 130K–200K tokens (confirm per model) | Fast, cost‑efficient tasks such as classification, extraction, lightweight chat, routing, and high‑volume support workflows | Cost‑efficient – MiniMax‑M2.5 often around 0.15–0.30 USD / 1M input tokens and 0.90–1.10 USD / 1M output; Llama 3.3 70B Instruct around 0.10–0.12 USD input and 0.30–0.38 USD output per 1M tokens |
Why use Inceptron through Orq.ai?
Capability | Provider / models | Direct | Through Orq.ai |
|---|---|---|---|
Chat | Inceptron‑hosted chat models (Kimi‑K2.6, GLM‑5.1, MiniMax‑M2.5, Llama 3.3 70B) | Call Inceptron endpoints directly for chat, reasoning, coding, and RAG using their OpenAI‑compatible or HTTP APIs | Use Inceptron models through Orq.ai’s OpenAI‑compatible endpoint, adding routing, tracing, evals, budgets, and governance controls around each request |
Code | GLM / Kimi / Llama models tuned or suitable for coding/analysis | Use Inceptron directly for code generation, debugging, refactoring, and agentic coding workflows with strong price‑performance | Route coding workloads through Orq.ai, compare Inceptron‑hosted models against other providers, and monitor cost, latency, and quality from one control layer |
Embeddings | Embedding providers configured in Orq.ai | Inceptron focuses on LLM inference and BYOM; embeddings can be served by separate models or providers | Use Orq.ai to route embedding workloads to supported embedding providers while keeping Inceptron for reasoning, generation, and agent steps |
This gives teams a practical way to use Inceptron where it performs best while centralising routing, observability, evals, and cost controls across the wider model stack.
Pricing
Model rates
Inceptron model pricing may differ depending on whether you:
connect your own Inceptron account and API key (BYOK), or
use Inceptron models billed through Orq.ai where available
Examples from current public pricing:
Llama 3.3 70B Instruct: about 0.10 USD / 1M input tokens and 0.30 USD / 1M output tokens on serverless endpoints
MiniMax‑M2.5: around 0.15–0.28 USD / 1M input and 0.90–1.10 USD / 1M output tokens
Kimi‑K2.6 and GLM‑5.1: 0.73–1.40 USD / 1M input and 3.50–4.40 USD / 1M output tokens depending on model and region.
Inceptron also offers hourly H100/H200/B200 dedicated deployments (for example B200 at around 8 USD per hour), plus commit‑and‑save discounts for longer reservations.
Check the Orq.ai pricing page and your workspace’s provider configuration for current per‑model rates, quotas, and plan details.
Compatible frameworks and tools
Orq.ai exposes Inceptron via:
a provider configuration in AI Router / Model Garden pointing at Inceptron’s OpenAI‑compatible or HTTP endpoints, and
an OpenAI‑compatible API layer for applications that expect that interface.
That means:
Popular AI frameworks (for example, OpenAI‑compatible clients, LangChain‑style frameworks, and other orchestration libraries) can talk to Inceptron through Orq’s router.
Code assistants and IDE tools that support MCP or OpenAI‑compatible APIs (Cursor, VS Code, Claude Desktop, Warp, Zed, and similar tools you’ve documented) can route through Orq.ai to Inceptron, depending on model and integration configuration.
Check the Orq.ai integration docs for the latest supported frameworks and tools for Inceptron.
FAQs
Do I need a separate Inceptron account to use Inceptron through Orq.ai?
You can either connect your own Inceptron API key into Orq.ai or, where available, use Inceptron models billed via Orq.ai; the exact options depend on your Orq plan, region, and how Inceptron is configured in your workspace. In both cases, Orq.ai gives you one place to manage routing, observability, and cost controls around that Inceptron usage.
Can I route only some workflows to Inceptron and others to different providers?
Yes. You define routes per workflow in Orq.ai and decide which ones should use Inceptron vs other providers, so you can reserve Inceptron for EU‑hosted, open‑model, or price‑performance‑sensitive workloads while sending other tasks to different providers.
Does using Inceptron 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 while benefiting from Inceptron’s compiler‑accelerated serving.
Alternatives to
Inceptron
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
Open 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.
Chat
Code
Image Generation
Reasoning
Speech
Vision
Models:
gpt-5.5 (EU)
gpt-5.6-luna (EU)
gpt-5.6-sol (EU)
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
