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Alibaba will release Qwen3.8-Max and open weights for Qwen3.8-27B next week; the…

2026-08-03 · 02:54 UTC ·@UnslothAI ·1 min read

Brief

Qwen3.8-Max, Alibaba's announced 2.4T-parameter model, and a 27B variant will be released as open weights next week; the 27B claims to run locally on 17 GB RAM/VRAM. Qwen3.8-Max promises 10+ days of self-evolving autonomous coding (GitHub trace), long-horizon planning (500+ chip-design turns, 365-day e-commerce), native multimodal feedback, and per-token pricing.

Why it matters

Alibaba will release Qwen3.8-Max and open weights for Qwen3.8-27B next week; the post claims Qwen3.8-27B will run locally on systems with 17 GB RAM/VRAM.

Key details

  • Qwen3.8-Max is advertised as a 2.4T-parameter model with autonomous coding (10+ days of self-evolving development from an empty folder with a GitHub trace), long-horizon planning (500+ chip-design optimization turns; 365-day e-commerce strategy), and native multimodal continuous-vision feedback.
  • Public pricing announced: input $2.00 per million tokens, output $6.00 per million tokens, implicit caching $0.25 per million; Qwen Studio and an API endpoint are provided for access.
Source evidence

Qwen3.8-27B is coming! 🔥

Will run locally on 17GB RAM/VRAM setups.

Qwen (@Alibaba_Qwen)

📢Meet Qwen3.8-Max — our most capable model to date.

Next week, the open weights of Qwen3.8-Max will be released, and Qwen3.8-27B is also going open-weights to meet you all!🎉

Qwen3.8-Max, a new bar for coding and cowork at 2.4T parameters:

  • Autonomous coding: 10+ days of self-evolving development, from empty folder to production without hand-holding, complete project trace in the GitHub：github.com/qwen-code-dev-bot…
  • Real work, real results: Production-quality deliverables across hundreds of professions.
  • Long-horizon mastery: System-level autonomous planning with closed-loop adaptive learning, driving 500+ turns of chip design optimization and 365 days of e-commerce strategy.
  • Native multimodal intelligence: Vision isn't just input — it's a continuous feedback loop for planning, execution, and self-correction.

💰Pricing:
Input: $2.0 / M tokens
Output: $6.0 / M tokens
Implicit Caching: $0.25 / M tokens

Start building with Qwen3.8-Max! 🚀

📖 Blog: qwen.ai/blog?id=qwen3.8
✅ Qwen Studio: chat.qwen.ai/?models=qwen3.8…
⚡ API: qwencloud.com/models/qwen3.8…

— https://nitter.net/Alibaba_Qwen/status/2084100707423289643#m