Deepseek V4.1 Flash 1017 beats larger rivals in agentic tasks while once again massively cutting per-task costs

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Chinese AI company Deepseek has shipped an updated version of its Flash model with major gains in agentic and coding tasks. The company is also opening up its in-house search engine to outside developers for the first time.

Deepseek-V4.1-Flash-1017 is available now through the company's website, app, and API. Deepseek also published the model weights as open source on Hugging Face and ModelScope. Compared to its direct predecessor, the model posts better benchmark scores, finishes tasks faster, and produces shorter outputs. The company calls the update a Pareto improvement in the tradeoff between performance and cost.

For building complete software projects from scratch, Deepseek uses reinforcement learning with self-feedback (RLSF). This post-training method lets the model use its own internal confidence scores or self-generated critiques as a reward signal. That removes the need for human evaluators or external reward models.