qwenlm/qwen3.8

Qwen3.8 is the large language model series developed by Qwen team, Alibaba Group.

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Summary Information

Updated 1 hour ago
Added to GitGenius on September 16th, 2026
Created on September 11th, 2025
Open Issues & Pull Requests: 20 (+0)
GitHub issues: Enabled
Number of forks: 314
Total Stargazers: 4,145 (+0)
Total Subscribers: 31 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 11.5 hours
Mean response time: 3.2 days
90th percentile: 10.1 days
Tracked items: 72

Most active contributors

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How this project is maintained

Roughly one issue in four opened in the past year never receives a reply. 80% of issues opened in the past year have been closed, leaving a working backlog. Three people close 53% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 19
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 38 days
Stale 30+ days: 3
Stale 90+ days: 1

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 4
Events in 7 days: 8

Top labels

  • inactive (52)
  • api (7)
  • eval (7)
  • qwen.ai (6)
  • bug (4)
  • badcase (3)
  • badcase-confirmed (3)
  • enhancement (3)

Detailed Description

Qwen3.8 is a large language model series developed by Alibaba Group's Qwen team.

The project provides a family of language models designed for various computational scales and use cases. The models are built to handle diverse natural language processing tasks, from text generation to understanding and reasoning. The approach centers on making capable language models accessible across different deployment scenarios, whether for research, commercial applications, or integration into larger systems.

Developers considering adoption should evaluate whether the model size and capabilities align with their computational resources and latency requirements. The series spans multiple model variants, allowing teams to select versions suited to their infrastructure constraints. Organizations building applications that require multilingual support or specialized domain knowledge may find the models particularly relevant. The project is positioned as an alternative to other large language model offerings in the market, though specific comparisons are not detailed in the available documentation.

The project maintains active development with regular updates to model variants and improvements to the underlying architecture. Documentation and implementation details are continuously refined to support integration across different platforms and frameworks. The team provides resources for both research applications and production deployment scenarios.