What is Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled?

95/100
Trust Score (A)
✅ Safe

Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled is a image-text-to-text that An AI model. It has a Nerq Trust Score of 95/100 (A). 2.9K GitHub stars. Published by Unknown. Last analyzed August 2026.

Why This Score

Trust & Safety Overview

95
TRUST SCORE
A
GRADE
2.9K
STARS
1.2M
DOWNLOADS

What Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled Does

Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled is a model in the image-text-to-text category. An AI model. It is published by an independent developer and has no specified license. With 2.9K GitHub stars and 1.2M downloads, it has a growing community of users and contributors.

Who Should Use Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled

Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled is well-suited for production use given its strong trust score and active community.

Details

AuthorUnknown
Categoryimage-text-to-text
LicenseNot specified
Typemodel
SourceView on GitHub
Security Score0/100
Activity Score0/100

How to Get Started

Check the trust score before installing:

curl nerq.ai/v1/preflight?target=jackrong-qwen3-5-27b-claude-4-6-opus-reasoning-distilled

Setup guide · Full safety report · Production review · Is it safe?

Safer Alternatives

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Frequently Asked Questions

What is Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled used for?
Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled is a image-text-to-text tool. An AI model.
Is Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled free?
License: Check project page. Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled has 2.9K GitHub stars.
Is Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled safe?
Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled has a Nerq Trust Score of 95/100 (A). Safe for production use.
What are alternatives to Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled?
Top alternatives: Kimi-K3, gemma-4-31B-it, Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive. See full comparison.

Last updated August 2026. Trust scores based on automated analysis of public data.

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