DeepSeek V3.1 is a unified hybrid reasoning open-weight model that powers agentic workflows—FP8 training, strong post-training for tool/function calling (non-thinking), Anthropic API support, and big SWE-Bench gains. In this video I unpack pricing and token efficiency, benchmark V3.1 vs R1 and Claude Sonnet 4, and show how to use it for coding agents without wasting tokens. LINKS: https://api-docs.deepseek.com/news/news250821 https://huggingface.co/deepseek-ai/DeepSeek-V3.1 Website: https://engineerprompt.ai/ RAG Beyond Basics Course: https://prompt-s-site.thinkific.com/courses/rag Let's Connect: 🦾 Discord: https://discord.com/invite/t4eYQRUcXB ☕ Buy me a Coffee: https://ko-fi.com/promptengineering |🔴 Patreon: https://www.patreon.com/PromptEngineering 💼Consulting: https://calendly.com/engineerprompt/consulting-call 📧 Business Contact: engineerprompt@gmail.com Become Member: http://tinyurl.com/y5h28s6h 💻 Pre-configured localGPT VM: https://bit.ly/localGPT (use Code: PromptEngineering for 50% off). Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 00:00 DeepSeek V3.1 00:31 Hybrid Inference Model Explained 01:04 Performance and Efficiency Improvements 05:02 Token Efficiency and Cost Implications 08:03 API and Hosting Considerations 13:23 Testing

Credits: Prompt Engineering