DeepSeek announced an updated V4.1 Flash artificial intelligence model alongside an open-source inference framework, pricing enterprise tasks down to roughly $0.20 each.

The developer posted a 77 per cent Pass@1 score on the DeepSWE v1.1 coding benchmark, cutting compute demands through shorter operational outputs.

Its release of V4.1-Flash-1017 on October 17 raised the system’s Terminal-Bench 4.0 score from 31.2 to 42.3, while DeepSWE v1.1 gained almost three points to 77.0. Benchmark evaluations show the system completing tasks for $0.11 to $0.14 when running lower-cost operational settings, scoring between 72 and 73 per cent. Independent tracking on Arena’s Agent Arena leaderboard placed the updated model first among open-weight competitors and 11th overall across 7,498 real-use sessions, with median execution costs between $0.18 and $0.19 per task.

Three days after the model release, DeepSeek published DeepInfer, the inference engine running its production services, alongside a request-handling library named deepseek-recipe. The framework manages hardware allocation, latency schedules and computing kernels to serve queries at scale. It runs directly on Nvidia SM100 graphics processing units and Huawei Ascend 950 neural processing units, targeting V4.1 Flash deployments.

The open-source code does not provide an end-to-end service stack out of the box. Production teams still need to supply their own key-value cache infrastructure, load balancing across parallel replicas, and complete disaggregation systems. Developers gain internal scheduling kernels, but enterprise engineering teams will absorb the integration overhead.

For online marketplaces, customer support platforms and software houses across Asia, the release attacks the cost ceiling that keeps autonomous agents out of routine commercial workflows.

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