DeepSeek-V4.1-Flash Ranked Agent Arena No. among open-source models and No. 11
BIBIBI
AT A GLANCE
PANews stated that DeepSeek the new version ranked No. in the Agent Arena open-source model rankings and No. 11。
Article
DeepSeek V4.1 Flash's new version tops the Agent Arena open-source model rankings, placing No. 11
PANews October 20 According to reports,AI model evaluation platform Arena announced that DeepSeek-V4.1-Flash-20261017(Max) has ranked Agent Arena No. among open-source models and No. 11. Based on approximately 7500 real-world usage sessions, the model's overall net improvement relative to the average model was approximately 5.1%, with a median per-task cost of approximately 0.20 USD. This version ranked No. among open-source models on all metrics: confirmed task success, positive and negative user feedback, Bash command execution reliability, and steerability. Its confirmed task success metric ranked No. 8。
PANews reported on October 20 that AI model evaluation platform Arena announced the ranking of DeepSeek-V4.1-Flash-20261017(Max).
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The model ranked No. among Agent Arena open-source models and No. 11。
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The evaluation described in the report was based on approximately 7500 real-world usage sessions.
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The model's overall net improvement relative to the average model was approximately 5.1%, with a median per-task cost of approximately 0.20 USD。
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This version ranked No. among open-source models on all metrics: confirmed task success, positive and negative user feedback, Bash command execution reliability, and steerability.
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This version's confirmed task success metric ranked No. 8。
AI-assisted interpretation
The following is analysis, separate from reported facts. Verify important claims independently.
This report indicates that, in Arena's Agent-type model evaluation,DeepSeek-V4.1-Flash-20261017(Max) ranked highest among open-source models; when all other evaluated models are included, it ranked No. 11. The report also listed the number of sessions, costs, and rankings across multiple metrics.
Why it matters to readers
The ranking results provide comparative information on the model's real-world usage sessions, task-completion-related metrics, and costs, which can be used to assess the performance of open-source Agent models.
Beginners can view the rankings as a cross-sectional comparison, but a ranking represents results only under a specific platform, set of metrics, and evaluation methodology, and cannot by itself prove that a model is suitable for all tasks.
Risks and unknowns
The evidence includes only PANews' paraphrase of Arena's announcement and does not provide Arena's original announcement or the complete rankings.
The evidence does not explain the specific calculation method for “overall net improvement.”
The evidence does not specify the task composition, sample distribution, or evaluation time range of the approximately 7500 sessions.
The evidence does not specify this version's official release status, availability, or commercialization progress.
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Open-Source Models
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