Research|LLM: DeepSeek V4.1-Flash-1017 Reaches the Top Tier of Open-Weight Models, Still Behind Proprietary Frontrunners

DeepSeek released V4.1-Flash-1017 on October 17, an update to the V4.1 Flash model launched on September 10. The API model name is unchanged. Pricing remains $0.30 per million input tokens and $1.20 per million output tokens, with a 50% off-peak discount. The weights are available under the MIT license. The new version outperforms its predecessor on all 11 benchmarks reported by DeepSeek: its Terminal-Bench 4.0 score increased from 31.2 to 42.3 and its DeepSWE score from 74.2 to 77.0. In our view, it now offers the best balance of capability and cost among open-weight models for coding and agent tasks.

Since the release, some articles have claimed that 1017 beats GPT-6, mainly on the strength of its DeepSWE score and cost per task. Our preliminary view is that 1017 has joined Kimi K3 and Xiaomi's MiMo-V2.6 Pro in the top tier of open-weight models. DeepSeek has made clear progress, but this update has not brought the open-weight frontier any closer to the leading proprietary models.

Our channel checks indicate that data from the previous generation of proprietary models remains accessible. Distilling from newer models, particularly Fable 5.1 and Opus 5.5, has become more difficult. Opus 5.5 retains a clear lead, and 1017 has not narrowed the gap. OpenAI also has an even stronger internal model, Bel.

Joins the top open-weight tier, but trails proprietary leaders overall

DeepSeek's model card compares 1017 with Kimi K3, GLM-5.3, GPT-5.6 Sol and Opus 5. It does not include Opus 5.5, GPT-6, Fable 5.1 or Gemini 4.

DeepSeek's published benchmark comparison for V4.1-Flash-1017