DeepSeek V4.1 Flash 1017 (Max)

DeepSeek V4.1 Flash 1017 (Max) scores 45 on the Artificial Analysis Intelligence Index, well above the median of 19 for comparable models, according to Artificial Analysis. The MIT-licensed open-weights model uses a 552B total parameter architecture with 16B active parameters, a 1M token context window, and runs at 230.4 tokens per second, priced at $0.30 per 1M input tokens and $1.20 per 1M output tokens. The combination of speed, intelligence and moderate pricing makes the model a candidate for self-hosted agent workloads, though its verbosity (210M tokens) may raise operational costs.

Hacker News https://artificialanalysis.ai/models/deepseek-v4-1-flash-1017 DeepSeek V4.1 Flash 1017 Max Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated. DeepSeek V4.1 Flash 1017 Max achieves 45 on the Artificial Analysis Intelligence Index, significantly outperforming the median score of 19 for comparable models, while processing 230 tokens per second at a cost of $0.30 per 1M input tokens and $1.20 per 1M output tokens. This combination of high intelligence, speed, and moderate pricing makes it a strong candidate for production use in tasks requiring rapid, high-quality text generation, though its verbosity 210M tokens may increase operational costs for outputs. DeepSeek V4.1 Flash 1017 Max is an MIT-licensed, open-weights model featuring a 552B total parameter architecture with only 16B active parameters, delivering 230.4 tokens per second and a 1M token context window. It scores 45 on the Artificial Analysis Intelligence Index, vastly outperforming the category median of 19. For production engineers, this provides a highly viable path to run extremely fast, high-intelligence agent workloads on private infrastructure at a fraction of the cost of proprietary APIs. AI vs. AI Debate “The summary fails to mention that this is an open-weights, MIT-licensed model with a 1M token context window and a 552B/16B parameter split, omitting the critical architectural and licensing details necessary for engineers evaluating self-hosted deployment.” “My summary focuses on the model’s performance metrics, cost-effectiveness, and practical applicability, which remain accurate and complete for assessing its production viability, while architectural and licensing details, though relevant, are secondary to its operational impact.”