Polyant

Polyant

Exelab
Qwen3.8-27B

Qwen3.8-27B

Alibaba
+
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About

Polyant is an open source platform for building teams of specialised AI agents that work alongside the people in a company, handling work like sales briefs, customer triage, document extraction, and compliance reporting. Its composable, self-hosted architecture lets teams choose their own model providers, including open-weight models, and their own infrastructure, so they're never locked into one vendor. Governance and audit tooling designed around EU AI Act requirements is built in from day one, with every agent decision logged in an audit registry compliance teams can query directly. Companies keep full control over data residency, deploying on-premises, in their own cloud, or on EU infrastructure managed by Exelab. And agents reach production in days rather than months, without giving up self-hosting or governance. It's built for mid-to-large European enterprises in regulated sectors like banking, insurance, and healthcare, where "just use a hosted API" isn't an option.

About

Qwen3.8-27B is a compact open-weights model in Alibaba’s Qwen3.8 family, aimed at developers and researchers who want strong local AI performance without using the full Max-scale model. Reports from Alibaba’s Qwen3.8 launch state that Qwen3.8-27B was planned for open-weight release alongside Qwen3.8-Max, expanding access for builders working on AI applications. The model is positioned for coding, research, professional workflows, and local deployment scenarios where a 27B model can be more practical than frontier-scale systems. Qwen3.8’s broader launch emphasizes software development, document processing, data analysis, and professional “cowork” use cases. Qwen3.8-27B is especially relevant for teams that need a capable open model for experimentation, coding agents, assistant workflows, and self-hosted inference. Built for practical deployment, Qwen3.8-27B gives developers a smaller Qwen3.8 option for building AI tools, testing agents, and running advanced language model workflows.

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Supported
On-Premises Supported
iPhone Supported
iPad Supported
Android Supported
Chromebook Supported

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

Enterprises

Audience

Software developers, AI researchers, coding agent builders, local LLM users, startup teams, enterprise AI teams, infrastructure teams, data teams, and organizations that need an open-weights 27B model for coding assistance, agent testing, self-hosted inference, document processing, data analysis, workflow automation, model benchmarking, private deployment, and Qwen-family experimentation

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Not Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Not Supported

API

Offers API Supported

Screenshots and Videos

No images available

Screenshots and Videos

Pricing

No information available.
Free Version Supported
Free Trial Not Supported

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5

Pros & Cons from Real Users

Pros

  • A 27B model is big enough to be useful for serious coding, reasoning, writing, and local-agent workflows, but still small enough that developers can realistically experiment with quantized versions on enthusiast hardware. That is the sweet spot for a lot of builders. Not everyone wants a massive cloud-only model, and not every workflow needs a 2T+ parameter system. A strong 27B model can be great for private coding help, local RAG, repo exploration, prompt testing, and lightweight agents. I also like the open-weight/local angle. Community reports around Qwen3.8-27B are already focused on GGUFs, MLX builds, VRAM needs, and local performance, which is exactly the kind of ecosystem momentum that makes a model useful beyond a demo.

Cons

  • The main downside is clarity. I would want a stable official model card, confirmed architecture details, benchmarks, license info, and serving recommendations before treating it as a production-ready model.

Training

Documentation Not Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

Exelab
Italy
polyant.ai

Company Information

Alibaba
Founded: 1999
China
qwen.ai

Alternatives

Alternatives

GPT-6 Astra

GPT-6 Astra

OpenAI
Qwen3.8-27B

Qwen3.8-27B

Alibaba
Qwen 4

Qwen 4

Alibaba
Qwen3.6

Qwen3.6

Alibaba

Categories

AI Agent Builders Supported

Categories

AI Coding Models Supported
AI Models Supported
Foundation Models Supported
Multimodal Models Supported

Integrations

Alibaba Cloud Not Supported
Alibaba Cloud Model Studio Not Supported
Cherry Studio Not Supported
Cline Not Supported
ClinePass Not Supported
Hermes Agent Not Supported
Hugging Face Not Supported
Model Context Protocol (MCP) Not Supported
ModelScope Not Supported
Novita AI Not Supported
Odysseus Not Supported
OfoxAI Not Supported
Ollama Not Supported
OpenClaw Not Supported
Python Not Supported
Qwen Not Supported
Qwen Code Not Supported
Qwen Studio Not Supported
QwenCloud Not Supported

Integrations

Alibaba Cloud Supported
Alibaba Cloud Model Studio Supported
Cherry Studio Supported
Cline Supported
ClinePass Supported
Hermes Agent Supported
Hugging Face Supported
Model Context Protocol (MCP) Supported
ModelScope Supported
Novita AI Supported
Odysseus Supported
OfoxAI Supported
Ollama Supported
OpenClaw Supported
Python Supported
Qwen Supported
Qwen Code Supported
Qwen Studio Supported
QwenCloud Supported
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