DeepSeek-V4.1-Flash-1017 builds full projects from scratch
Self-feedback reinforcement learning that targets whole-project output, not single-file answers, signals a shift in coding benchmarks toward multi-file, runnable deliverables. Developers evaluating open-weight models for end-to-end scaffolding can compare V4.1-Flash-1017 against Claude and GPT coding models on game and 3D tasks before committing to a stack.
DeepSeek's V4.1-Flash-1017 model can now construct complete projects from zero, according to a demonstration posted on X on 17 Oct. The model was trained with reinforcement learning that incorporates self-feedback to strengthen end-to-end project development. Demonstrated tasks include game development and 3D modelling. The post includes video examples of the model building these projects unassisted.
🎮 DeepSeek-V4.1-Flash-1017 已经可以从零构建完整项目。 它通过带有自我反馈的强化学习，提升端到端项目开发能力，覆盖游戏开发、3D 建模等任务。 看看实际效果👇 Your browser does not support the video tag. 🔗 View on Twitter Your browser does not support the video tag. 🔗 View on Twitter Your browser does not support the video tag. 🔗 View on Twitter Your browser does not support the video tag. 🔗 View on Twitter 💬 3 🔄 0 ❤️ 6 👀 873 📊 3 ⚡
