全新架构，极致性价比！阿里千问Qwen3.8-Flash发布并开源
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Merged summary
TL;DR - Alibaba released and open-sourced Qwen3.8-Flash, a multimodal mixture-of-experts model designed to deliver strong agentic and reasoning performance at substantially lower training and inference costs. Its new architecture is also positioned as a precursor to Qwen4.
- The model has 125B Transformer parameters but activates only 6B per token, supplemented by 51B parameters of N-gram embeddings.
- Its architecture combines Qwen Sparse Attention with GDN, gated residual pathways, and training-system optimizations; Alibaba reports over 8× acceleration in high-cache-hit, 1M-token contexts.
- Alibaba claims nearly 90% lower training cost than Qwen3.7-Plus, with API pricing of RMB 1 per million input tokens and RMB 3 per million output tokens.
- Reported benchmarks show strengths in agentic coding, tool use, long-horizon professional tasks, mobile interaction, and visual reasoning, though the article provides company-reported comparisons rather than independent validation.
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全新架构，极致性价比！阿里千问Qwen3.8-Flash发布并开源
Public signals
N/A
TL;DR - Alibaba released and open-sourced Qwen3.8-Flash, a multimodal mixture-of-experts model designed to deliver strong agentic and reasoning performance at substantially lower training and inference costs. Its new architecture is also positioned as a precursor to Qwen4.
- The model has 125B Transformer parameters but activates only 6B per token, supplemented by 51B parameters of N-gram embeddings.
- Its architecture combines Qwen Sparse Attention with GDN, gated residual pathways, and training-system optimizations; Alibaba reports over 8× acceleration in high-cache-hit, 1M-token contexts.
- Alibaba claims nearly 90% lower training cost than Qwen3.7-Plus, with API pricing of RMB 1 per million input tokens and RMB 3 per million output tokens.
- Reported benchmarks show strengths in agentic coding, tool use, long-horizon professional tasks, mobile interaction, and visual reasoning, though the article provides company-reported comparisons rather than independent validation.