DeepSeek released DeepSeek-V4.1-Flash-1017 as a minor update to its prior model. It improves reasoning, coding, agentic tasks, and visual benchmarks. The model reduces task execution time and output length. It advances the performance-cost trade-off across agent harnesses. The release includes a Python reference implementation for prompt encoding. A Rust library set called deepseek-recipe is also provided for production use. The model supports a 1M token context window. Recommended sampling parameters include temperature 1.0 and top_p 1.0. The model weights are MIT licensed. The technical report is available for review.
What changed
DeepSeek-V4.1-Flash offered lower reasoning, coding, and agentic performance with longer execution times and output lengths.
What it unlocks
Faster and cheaper task completion with improved reasoning, coding, and agentic capabilities.
- 763B parameters
- 1M token context window
- ≥256K max tokens
- 10,233 downloads last month
Sources