DBRX

DBRX

Databricks
Qwen3.8-27B

Qwen3.8-27B

Alibaba
+
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About

Today, we are excited to introduce DBRX, an open, general-purpose LLM created by Databricks. Across a range of standard benchmarks, DBRX sets a new state-of-the-art for established open LLMs. Moreover, it provides the open community and enterprises building their own LLMs with capabilities that were previously limited to closed model APIs; according to our measurements, it surpasses GPT-3.5, and it is competitive with Gemini 1.0 Pro. It is an especially capable code model, surpassing specialized models like CodeLLaMA-70B in programming, in addition to its strength as a general-purpose LLM. This state-of-the-art quality comes with marked improvements in training and inference performance. DBRX advances the state-of-the-art in efficiency among open models thanks to its fine-grained mixture-of-experts (MoE) architecture. Inference is up to 2x faster than LLaMA2-70B, and DBRX is about 40% of the size of Grok-1 in terms of both total and active parameter counts.

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
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Organizations looking for an advanced Large Language Model solution

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
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

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

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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
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Databricks
United States
www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm

Company Information

Alibaba
Founded: 1999
China
qwen.ai

Alternatives

FLIP

FLIP

Kanerika

Alternatives

GLM-5.3

GLM-5.3

Z.ai
Kimi K2

Kimi K2

Moonshot AI
Qwen3.8-Max

Qwen3.8-Max

Alibaba
Qwen2

Qwen2

Alibaba
Qwen3.6

Qwen3.6

Alibaba
Qwen2

Qwen2

Alibaba

Categories

Categories

Integrations

Alibaba Cloud
Alibaba Cloud Model Studio
Cherry Studio
Cline
Cogent DataHub
GPT-3.5
GPT-4
Hermes Agent
Hugging Face
ModelScope
Novita AI
Odysseus
OfoxAI
OpenClaw
Python
Qwen Code
Qwen Studio
QwenCloud
Visplore
ZenML

Integrations

Alibaba Cloud
Alibaba Cloud Model Studio
Cherry Studio
Cline
Cogent DataHub
GPT-3.5
GPT-4
Hermes Agent
Hugging Face
ModelScope
Novita AI
Odysseus
OfoxAI
OpenClaw
Python
Qwen Code
Qwen Studio
QwenCloud
Visplore
ZenML
Claim DBRX and update features and information
Claim DBRX and update features and information
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Claim Qwen3.8-27B and update features and information