DeepSeek Releases V4.1 Flash-1017 with Enhanced Performance and Lower Costs
DeepSeek has launched the V4.1-Flash-1017 update, delivering higher benchmark scores, shorter execution times, and reduced token lengths across its web, app, and API platforms while keeping pricing unchanged. The model also introduces stronger 'wish-driven' programming capabilities through self-feedback reinforcement learning, allowing it to build complete projects from minimal prompts. This release achieves a Pareto improvement by simultaneously increasing model capabilities and decreasing operational costs and times for developers. The enhanced ability to generate functional applications from vague or minimal specifications significantly lowers the barrier for software creation and agentic coding workflows. The model weights have been released under an open MIT license and are available for download on HuggingFace and ModelScope. Additionally, DeepSeek updated platform adapters, recommending users to upgrade DeepSeek Harness to version 0.2.1-rc.1 or higher for full compatibility.
## BACKGROUND
DeepSeek is a prominent Chinese artificial intelligence company known for developing open-weight large language models with high computational efficiency. Benchmarks like Terminal-Bench and DeepSWE are evaluation frameworks designed to measure the performance of AI agents on complex, real-world software engineering and command-line tasks.