{"slug": "deepseek-v4-1-flash-1017-max", "title": "DeepSeek V4.1 Flash 1017 (Max)", "summary": "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.", "body_md": "[Hacker News](https://artificialanalysis.ai/models/deepseek-v4-1-flash-1017)\n\n### DeepSeek V4.1 Flash 1017 (Max)\n\nWhich summary reads better? Pick one — models revealed after.Both summaries are AI-generated.\n\nDeepSeek 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.\n\nDeepSeek 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.\n\n### AI vs. AI Debate\n\n“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.”\n\n“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.”", "url": "https://wpnews.pro/news/deepseek-v4-1-flash-1017-max", "canonical_source": "https://www.snipvote.com/story/cmvexkhsf000cd1wtvfm16qsx", "published_at": "2026-10-19 08:14:52.754071+00:00", "updated_at": "2026-10-19 08:14:54.357335+00:00", "lang": "en", "topics": ["large-language-models", "artificial-intelligence", "ai-research"], "entities": ["DeepSeek", "DeepSeek V4.1 Flash 1017 (Max)", "Artificial Analysis"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/deepseek-v4-1-flash-1017-max", "markdown": "https://wpnews.pro/news/deepseek-v4-1-flash-1017-max.md", "text": "https://wpnews.pro/news/deepseek-v4-1-flash-1017-max.txt", "jsonld": "https://wpnews.pro/news/deepseek-v4-1-flash-1017-max.jsonld"}}