Qwen3.8-27B

Qwen3.8-27B

Alibaba
Timestral

Timestral

Mistral AI
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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.

About

Timestral is a family of time-series foundation models and a Python inference package for generating multivariate probabilistic forecasts from historical sequences. The four model sizes (5M, 28M, 123M and 209M parameters) support past-only, known-future and categorical covariates, return quantile forecasts, and accept context windows up to 32,768 time steps. Developers can load pretrained weights from the Hugging Face Hub or from local files and run inference on CPU or GPU, then incorporate forecasts into monitoring, capacity-planning, energy, supply-chain, financial-risk, or maintenance workflows. Timestral supports zero-shot forecasting and can be adapted to domain data through Mistral Forge. The package is intended for data scientists, machine-learning engineers, and teams building forecasting systems; repository code is Apache 2.0, while model weights use a separate Modified MIT license.

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

Platforms Supported

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

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

Audience

Data scientists, machine-learning engineers, forecasting practitioners, and teams developing time-series forecasting systems.

Support

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

Support

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

API

Offers API Supported

API

Offers API Supported

Screenshots and Videos

Screenshots and Videos

No images available

Pricing

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

Pricing

No information available.
Free Version Supported
Free Trial Not Supported

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5

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:

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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 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

Alibaba
Founded: 1999
China
qwen.ai

Company Information

Mistral AI
Founded: 2023
France
github.com/mistralai/timestral

Alternatives

Alternatives

TimesFM-3

TimesFM-3

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GPT-6.1 Sol

GPT-6.1 Sol

OpenAI
Qwen 4

Qwen 4

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Solar Mini

Solar Mini

Upstage AI
Qwen2

Qwen2

Alibaba
Mistral NeMo

Mistral NeMo

Mistral AI

Categories

Categories

AI Models Supported

Integrations

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