Qwen3.6-27B

Qwen3.6-27B

Alibaba
Tev1

Tev1

Together AI
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+

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About

Qwen3.6-27B is a dense, open source multimodal language model in the Qwen3.6 series, designed to deliver flagship-level performance in coding, reasoning, and agent-based workflows while maintaining a relatively efficient parameter size of 27 billion. It is positioned as a high-performance general model that “punches above its weight,” achieving results competitive with or superior to significantly larger models on key benchmarks, particularly in agentic coding tasks. It supports both thinking and non-thinking modes, allowing it to dynamically balance deep reasoning with fast responses depending on the task, and integrates capabilities across text and multimodal inputs such as images and video. Built as part of the Qwen3.6 family, the model emphasizes real-world usability, stability, and developer productivity, incorporating improvements driven by community feedback and practical deployment needs.

About

Tev1 is a fast decision and classification model built by Together AI and fine-tuned from Qwen3.5 4B. It is designed around a simple interface: provide a piece of state, a question, and 2 to 24 labeled options, and the model returns exactly one letter that software can map back to a semantic key. This structure supports production tasks such as routing support tickets, evaluating automated returns, applying policy rules, rating sentiment, categorizing documents, and inserting deterministic decision points into larger agent pipelines. The model has 4.7B parameters, a 32.8K-token context window, and runs with non-thinking generation for fast, predictable classification. It was trained on 37,840 examples spanning intent detection, boolean comprehension, sentiment, policy application, routing, news categories, and research-paper taxonomy. Recommended settings use temperature 0 and a very small output budget so responses remain stable and easy to parse.

Platforms Supported

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

Developers and AI engineers who need a powerful, open source model capable of advanced coding, reasoning, and agent workflows

Audience

Developers and AI engineering teams seeking to route, classify, score, and apply policy decisions through fast, deterministic structured outputs

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

Pricing

Free
Free Version Supported
Free Trial Not Supported

Pricing

$0.04 per 1M tokens
Free Version Supported
Free Trial Not Supported

Reviews/Ratings

Overall 5.0 / 5
features 4.0 / 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

  • Qwen3.6-27B is exciting because it packs a lot of capability into a model size that still feels realistic for serious builders. It is open-weight, dense, multimodal, and clearly tuned for coding-heavy agent workflows. The agentic coding focus is the biggest win. Qwen specifically calls out integration with coding assistants like OpenClaw, Claude Code, and Qwen Code, which makes it feel useful for real development workflows instead of just benchmark screenshots. I also like the long-context support. For coding agents, repo analysis, technical docs, visual debugging, and multi-step reasoning, having more room for context makes a real difference.

Cons

  • It is still a 27B model, so local use is not effortless. You still need the right hardware, quantization, serving setup, and patience if you want good speed.

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/blog

Company Information

Together AI
Founded: 2022
United States
www.together.ai/models/tev1-4b-experimental

Alternatives

Alternatives

Clef-flash

Clef-flash

Cloudflare
Bonsai 27B

Bonsai 27B

PrismML
Clef

Clef

Cloudflare
Jev

Jev

TypeSafe AI
Qwen3.5

Qwen3.5

Alibaba
Qwen3.6

Qwen3.6

Alibaba

Categories

AI Coding Models Supported
AI Models Supported
AI Vision Models Supported

Categories

AI Decision Models Supported

Integrations

Python Supported
Alibaba Cloud Model Studio Supported
Cheaper Inference Supported
Hugging Face Supported
ModelScope Supported
Ollama Supported
OpenClaw Supported
Qwen Supported
Qwen Studio Supported
Qwen3.5 Not Supported
Qwen3.6 Supported
TypeScript Not Supported

Integrations

Python Supported
Alibaba Cloud Model Studio Not Supported
Cheaper Inference Not Supported
Hugging Face Not Supported
ModelScope Not Supported
Ollama Not Supported
OpenClaw Not Supported
Qwen Not Supported
Qwen Studio Not Supported
Qwen3.5 Supported
Qwen3.6 Not Supported
TypeScript Supported
Claim Qwen3.6-27B and update features and information
Claim Qwen3.6-27B and update features and information
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