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Report: DeepSeek V5 plans pretraining on Ascend chips

On November 3, 2026, according to a report by Star Market Daily, multiple industry chain sources said that DeepSeek's next-generation large model, DeepSeek V5, plans to conduct pretraining based on Huawei Ascend chips; previously, Ascend chips were mainly used for inference tasks of DeepSeek large models. East Money reported the same day that after the release of DeepSeek-V4 in April 2026, Huawei said that its full series of Ascend SuperNode products could fully support the V4 series models; on September 30, DeepSeek open-sourced infrastructure components for the Ascend computing platform, covering TileLang compilation tools, DeepGEMM, FlashMLA, DeepEP, and others. The report also said that domestic computing power deployment is currently still concentrated on the inference side, and that the full-parameter post-training of DeepSeek-V4-Pro previously completed by an Ascend 910C cluster did not involve basic pretraining. There is currently no official response from DeepSeek or Huawei.

ARC Prize publishes evaluation results for DeepSeek V4.1 Flash 1017

On November 3, 2026, ARC Prize published the verified ARC-AGI results for DeepSeek V4.1 Flash 1017: 78.1% on ARC-AGI-2 at $0.16 per task, and 94.5% on ARC-AGI-1 at $0.06 per task; on ARC-AGI-3 it scored 5.8% at a cost of $1.2K under the provider adapter harness, and 0.2% at $1.0K under the standard harness. ARC Prize stated that the model leads open-weight models on ARC-AGI-3 and ARC-AGI-2, and matches the best open-weight model results on ARC-AGI-1. Compared with the original V4.1 Flash of September 10, 2026, 1017's ARC-AGI-3 score rose from 2.68% to 5.81% on the semi-private set while falling from 11.05% to 8.97% on the public set; at maximum reasoning effort, the reasoning tokens consumed by its first action decreased by an average of 36% across 25 public games and by 48.6% across 55 semi-private games.

ARC Prize announces ARC-AGI evaluation results for DeepSeek V4.1 Flash 1017

On November 3, 2026, ARC Prize published the verified ARC-AGI results for DeepSeek V4.1 Flash 1017: 78.1% on ARC-AGI-2 at $0.16 per task, and 94.5% on ARC-AGI-1 at $0.06 per task; on ARC-AGI-3 it scored 5.8% at a cost of $1.2K under the provider adapter harness, and 0.2% at $1.0K under the standard harness. ARC Prize stated that the model leads open-weight models on ARC-AGI-3 and ARC-AGI-2, and matches the best open-weight model results on ARC-AGI-1. Compared with the original V4.1 Flash of September 10, 2026, 1017's ARC-AGI-3 score rose from 2.68% to 5.81% on the semi-private set while falling from 11.05% to 8.97% on the public set; at maximum reasoning effort, the reasoning tokens consumed by its first action decreased by an average of 36% across 25 public games and by 48.6% across 55 semi-private games.

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Unsloth publishes DeepSeek-V4.1-Flash-1017 GGUF benchmark results

On November 2, 2026, Unsloth published the benchmark results of the GGUF quantized version of DeepSeek-V4.1-Flash-1017 at a 512K context. The tests covered tasks including Terminal Bench and DeepSWE, and Unsloth stated that it ranks highest overall. Among them, the 1-bit Unsloth GGUF scored 68.1 on DeepSWE, 72.2 on Terminal-Bench, and 52.7 on AutomationBench, leading other quantized versions by more than 10%. There are currently no further developments.