Olmo 2

Olmo 2

Ai2
Qwen3.8-27B

Qwen3.8-27B

Alibaba
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About

Olmo 2 is a family of fully open language models developed by the Allen Institute for AI (AI2), designed to provide researchers and developers with transparent access to training data, open-source code, reproducible training recipes, and comprehensive evaluations. These models are trained on up to 5 trillion tokens and are competitive with leading open-weight models like Llama 3.1 on English academic benchmarks. Olmo 2 emphasizes training stability, implementing techniques to prevent loss spikes during long training runs, and utilizes staged training interventions during late pretraining to address capability deficiencies. The models incorporate state-of-the-art post-training methodologies from AI2's Tülu 3, resulting in the creation of Olmo 2-Instruct models. An actionable evaluation framework, the Open Language Modeling Evaluation System (OLMES), was established to guide improvements through development stages, consisting of 20 evaluation benchmarks assessing core capabilities.

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

Developers and researchers searching for a tool to streamline their AI research and operations

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

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

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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

Ai2
Founded: 2014
United States
allenai.org/blog/olmo2

Company Information

Alibaba
Founded: 1999
China
qwen.ai

Alternatives

Molmo

Molmo

Ai2

Alternatives

GLM-5.3

GLM-5.3

Z.ai
Olmo 3

Olmo 3

Ai2
Qwen3.8-Max

Qwen3.8-Max

Alibaba
GLM-5.3

GLM-5.3

Z.ai
Qwen3.6

Qwen3.6

Alibaba
Llama 2

Llama 2

Meta
Qwen2

Qwen2

Alibaba

Categories

Categories

Integrations

Alibaba Cloud
Alibaba Cloud Model Studio
Cherry Studio
Cline
ClinePass
Hermes Agent
Hugging Face
Model Context Protocol (MCP)
ModelScope
Molmo 2
Novita AI
Odysseus
OfoxAI
Ollama
OpenClaw
Python
Qwen
Qwen Code
Qwen Studio
QwenCloud

Integrations

Alibaba Cloud
Alibaba Cloud Model Studio
Cherry Studio
Cline
ClinePass
Hermes Agent
Hugging Face
Model Context Protocol (MCP)
ModelScope
Molmo 2
Novita AI
Odysseus
OfoxAI
Ollama
OpenClaw
Python
Qwen
Qwen Code
Qwen Studio
QwenCloud
Claim Olmo 2 and update features and information
Claim Olmo 2 and update features and information
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Claim Qwen3.8-27B and update features and information