{ "timestamp": "2025-01-28T16:23:51.699727", "events": [ { "timestamp": "2025-01-28T16:24:02.605542", "type": "event", "data": { "type": "logs", "content": "starting_research", "output": "\ud83d\udd0d Starting the research task for 'What is DeepSeek R1'...", "metadata": null } }, { "timestamp": "2025-01-28T16:24:02.615122", "type": "event", "data": { "type": "logs", "content": "agent_generated", "output": "\ud83e\udd16 Tech Agent", "metadata": null } }, { "timestamp": "2025-01-28T16:24:02.633209", "type": "event", "data": { "type": "logs", "content": "planning_research", "output": "\ud83c\udf10 Browsing the web to learn more about the task: What is DeepSeek R1...", "metadata": null } }, { "timestamp": "2025-01-28T16:24:06.780296", "type": "event", "data": { "type": "logs", "content": "planning_research", "output": "\ud83e\udd14 Planning the research strategy and subtasks...", "metadata": null } }, { "timestamp": "2025-01-28T16:24:09.885841", "type": "event", "data": { "type": "logs", "content": "subqueries", "output": "\ud83d\uddc2\ufe0f I will conduct my research based on the following queries: ['DeepSeek R1 capabilities comparison OpenAI o1', 'DeepSeek R1 reinforcement learning training process XAI', 'DeepSeek R1 open-source license cost advantages', 'DeepSeek R1 performance benchmarks reasoning tasks datasets', 'What is DeepSeek R1']...", "metadata": [ "DeepSeek R1 capabilities comparison OpenAI o1", "DeepSeek R1 reinforcement learning training process XAI", "DeepSeek R1 open-source license cost advantages", "DeepSeek R1 performance benchmarks reasoning tasks datasets", "What is DeepSeek R1" ] } }, { "timestamp": "2025-01-28T16:24:09.904677", "type": "event", "data": { "type": "logs", "content": "running_subquery_research", "output": "\n\ud83d\udd0d Running research for 'DeepSeek R1 capabilities comparison OpenAI o1'...", "metadata": null } }, { "timestamp": "2025-01-28T16:24:09.916304", "type": "event", "data": { "type": "logs", "content": "running_subquery_research", "output": "\n\ud83d\udd0d Running research for 'DeepSeek R1 reinforcement learning training process XAI'...", "metadata": null } }, { "timestamp": "2025-01-28T16:24:09.922684", "type": "event", "data": { "type": "logs", "content": "running_subquery_research", "output": "\n\ud83d\udd0d Running research for 'DeepSeek R1 open-source license cost advantages'...", "metadata": null } }, { "timestamp": "2025-01-28T16:24:09.934404", "type": "event", "data": { "type": "logs", "content": "running_subquery_research", "output": "\n\ud83d\udd0d Running research for 'DeepSeek R1 performance benchmarks reasoning tasks datasets'...", "metadata": null } }, { "timestamp": "2025-01-28T16:24:09.949469", "type": "event", "data": { "type": "logs", "content": "running_subquery_research", "output": "\n\ud83d\udd0d Running research for 'What is DeepSeek R1'...", "metadata": null } }, { "timestamp": "2025-01-28T16:24:12.553747", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://fireworks.ai/blog/deepseek-r1-deepdive\n", "metadata": "https://fireworks.ai/blog/deepseek-r1-deepdive" } }, { "timestamp": "2025-01-28T16:24:12.573197", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://www.tomsguide.com/ai/deepseek-r1-is-the-chinese-ai-model-disrupting-openai-and-anthropic-what-you-need-to-know\n", "metadata": "https://www.tomsguide.com/ai/deepseek-r1-is-the-chinese-ai-model-disrupting-openai-and-anthropic-what-you-need-to-know" } }, { "timestamp": "2025-01-28T16:24:12.583207", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://www.geeksforgeeks.org/deepseek-r1-rl-models-whats-new/\n", "metadata": "https://www.geeksforgeeks.org/deepseek-r1-rl-models-whats-new/" } }, { "timestamp": "2025-01-28T16:24:12.596040", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://www.business-standard.com/world-news/deepseek-r1-chinese-ai-research-breakthrough-challenging-openai-explained-125012700327_1.html\n", "metadata": "https://www.business-standard.com/world-news/deepseek-r1-chinese-ai-research-breakthrough-challenging-openai-explained-125012700327_1.html" } }, { "timestamp": "2025-01-28T16:24:12.602667", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://www.cnn.com/2025/01/27/tech/deepseek-ai-explainer/index.html\n", "metadata": "https://www.cnn.com/2025/01/27/tech/deepseek-ai-explainer/index.html" } }, { "timestamp": "2025-01-28T16:24:12.616303", "type": "event", "data": { "type": "logs", "content": "researching", "output": "\ud83e\udd14 Researching for relevant information across multiple sources...\n", "metadata": null } }, { "timestamp": "2025-01-28T16:24:12.623705", "type": "event", "data": { "type": "logs", "content": "scraping_urls", "output": "\ud83c\udf10 Scraping content from 5 URLs...", "metadata": null } }, { "timestamp": "2025-01-28T16:24:13.832133", "type": "event", "data": { "type": "logs", "content": "scraping_content", "output": "\ud83d\udcc4 Scraped 4 pages of content", "metadata": null } }, { "timestamp": "2025-01-28T16:24:13.843224", "type": "event", "data": { "type": "logs", "content": "scraping_images", "output": "\ud83d\uddbc\ufe0f Selected 4 new images from 7 total images", "metadata": [ "https://media.cnn.com/api/v1/images/stellar/prod/gettyimages-2196223475.jpg?c=16x9&q=w_1280,c_fill", "https://media.cnn.com/api/v1/images/stellar/prod/jon-stewart-01-27.jpg?c=16x9&q=h_144,w_256,c_fill", "https://media.cnn.com/api/v1/images/stellar/videothumbnails/32276036-68347919-generated-thumbnail.jpg?c=16x9&q=h_144,w_256,c_fill", "https://media.cnn.com/api/v1/images/stellar/prod/still-21319316-26194-119-still.jpg?c=16x9&q=h_144,w_256,c_fill" ] } }, { "timestamp": "2025-01-28T16:24:13.848362", "type": "event", "data": { "type": "logs", "content": "scraping_complete", "output": "\ud83c\udf10 Scraping complete", "metadata": null } }, { "timestamp": "2025-01-28T16:24:13.866742", "type": "event", "data": { "type": "logs", "content": "fetching_query_content", "output": "\ud83d\udcda Getting relevant content based on query: What is DeepSeek R1...", "metadata": null } }, { "timestamp": "2025-01-28T16:24:16.619032", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://bottr.me/blog/deepseek\n", "metadata": "https://bottr.me/blog/deepseek" } }, { "timestamp": "2025-01-28T16:24:16.663501", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://blog.promptlayer.com/openai-vs-deepseek-an-analysis-of-r1-and-o1-models/\n", "metadata": "https://blog.promptlayer.com/openai-vs-deepseek-an-analysis-of-r1-and-o1-models/" } }, { "timestamp": "2025-01-28T16:24:16.674804", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://docsbot.ai/models/compare/o1/deepseek-r1\n", "metadata": "https://docsbot.ai/models/compare/o1/deepseek-r1" } }, { "timestamp": "2025-01-28T16:24:16.681995", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://www.tysoolen.com/story/deepseek-r1-openai-o1-ultimate-benchmark-showdown\n", "metadata": "https://www.tysoolen.com/story/deepseek-r1-openai-o1-ultimate-benchmark-showdown" } }, { "timestamp": "2025-01-28T16:24:16.698932", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://www.geeky-gadgets.com/deepseek-r1-vs-openai-o1/\n", "metadata": "https://www.geeky-gadgets.com/deepseek-r1-vs-openai-o1/" } }, { "timestamp": "2025-01-28T16:24:16.702861", "type": "event", "data": { "type": "logs", "content": "researching", "output": "\ud83e\udd14 Researching for relevant information across multiple sources...\n", "metadata": null } }, { "timestamp": "2025-01-28T16:24:16.717686", "type": "event", "data": { "type": "logs", "content": "scraping_urls", "output": "\ud83c\udf10 Scraping content from 5 URLs...", "metadata": null } }, { "timestamp": "2025-01-28T16:24:17.945502", "type": "event", "data": { "type": "logs", "content": "scraping_content", "output": "\ud83d\udcc4 Scraped 5 pages of content", "metadata": null } }, { "timestamp": "2025-01-28T16:24:17.951621", "type": "event", "data": { "type": "logs", "content": "scraping_images", "output": "\ud83d\uddbc\ufe0f Selected 1 new images from 1 total images", "metadata": [ "https://www.geeky-gadgets.com/wp-content/uploads/2024/11/deepseek-r1-vs-openai-o1-comparison.webp" ] } }, { "timestamp": "2025-01-28T16:24:18.024255", "type": "event", "data": { "type": "logs", "content": "scraping_complete", "output": "\ud83c\udf10 Scraping complete", "metadata": null } }, { "timestamp": "2025-01-28T16:24:18.035654", "type": "event", "data": { "type": "logs", "content": "fetching_query_content", "output": "\ud83d\udcda Getting relevant content based on query: DeepSeek R1 capabilities comparison OpenAI o1...", "metadata": null } }, { "timestamp": "2025-01-28T16:24:18.211132", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://aipapersacademy.com/deepseek-r1/\n", "metadata": "https://aipapersacademy.com/deepseek-r1/" } }, { "timestamp": "2025-01-28T16:24:18.221778", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://medium.com/@namnguyenthe/deepseek-r1-architecture-and-training-explain-83319903a684\n", "metadata": "https://medium.com/@namnguyenthe/deepseek-r1-architecture-and-training-explain-83319903a684" } }, { "timestamp": "2025-01-28T16:24:18.238625", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://unfoldai.com/deepseek-r1/\n", "metadata": "https://unfoldai.com/deepseek-r1/" } }, { "timestamp": "2025-01-28T16:24:18.252234", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://www.vellum.ai/blog/the-training-of-deepseek-r1-and-ways-to-use-it\n", "metadata": "https://www.vellum.ai/blog/the-training-of-deepseek-r1-and-ways-to-use-it" } }, { "timestamp": "2025-01-28T16:24:18.264721", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://arxiv.org/abs/2501.12948\n", "metadata": "https://arxiv.org/abs/2501.12948" } }, { "timestamp": "2025-01-28T16:24:18.269085", "type": "event", "data": { "type": "logs", "content": "researching", "output": "\ud83e\udd14 Researching for relevant information across multiple sources...\n", "metadata": null } }, { "timestamp": "2025-01-28T16:24:18.285878", "type": "event", "data": { "type": "logs", "content": "scraping_urls", "output": "\ud83c\udf10 Scraping content from 5 URLs...", "metadata": null } }, { "timestamp": "2025-01-28T16:24:19.708141", "type": "event", "data": { "type": "logs", "content": "scraping_content", "output": "\ud83d\udcc4 Scraped 4 pages of content", "metadata": null } }, { "timestamp": "2025-01-28T16:24:19.721635", "type": "event", "data": { "type": "logs", "content": "scraping_images", "output": "\ud83d\uddbc\ufe0f Selected 4 new images from 14 total images", "metadata": [ "https://unfoldai.com/storage/2025/01/lm-studio-deepseek-r1.jpg", "https://unfoldai.com/storage/2025/01/DeepSeek-R1-performance.jpg", "https://unfoldai.com/storage/2025/01/distill-models-deepseek-r1-performance.jpg", "https://aipapersacademy.com/wp-content/uploads/2025/01/image-6.png" ] } }, { "timestamp": "2025-01-28T16:24:19.732623", "type": "event", "data": { "type": "logs", "content": "scraping_complete", "output": "\ud83c\udf10 Scraping complete", "metadata": null } }, { "timestamp": "2025-01-28T16:24:19.745468", "type": "event", "data": { "type": "logs", "content": "fetching_query_content", "output": "\ud83d\udcda Getting relevant content based on query: DeepSeek R1 reinforcement learning training process XAI...", "metadata": null } }, { "timestamp": "2025-01-28T16:24:19.874537", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://www.prompthub.us/blog/deepseek-r-1-model-overview-and-how-it-ranks-against-openais-o1\n", "metadata": "https://www.prompthub.us/blog/deepseek-r-1-model-overview-and-how-it-ranks-against-openais-o1" } }, { "timestamp": "2025-01-28T16:24:19.882371", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://arxiv.org/html/2501.12948v1\n", "metadata": "https://arxiv.org/html/2501.12948v1" } }, { "timestamp": "2025-01-28T16:24:19.898152", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://techcrunch.com/2025/01/27/deepseek-claims-its-reasoning-model-beats-openais-o1-on-certain-benchmarks/\n", "metadata": "https://techcrunch.com/2025/01/27/deepseek-claims-its-reasoning-model-beats-openais-o1-on-certain-benchmarks/" } }, { "timestamp": "2025-01-28T16:24:19.909597", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://pub.towardsai.net/inside-deepseek-r1-the-amazing-model-that-matches-gpt-o1-on-reasoning-at-a-fraction-of-the-cost-e314561ca12c\n", "metadata": "https://pub.towardsai.net/inside-deepseek-r1-the-amazing-model-that-matches-gpt-o1-on-reasoning-at-a-fraction-of-the-cost-e314561ca12c" } }, { "timestamp": "2025-01-28T16:24:19.917756", "type": "event", "data": { "type": "logs", "content": "researching", "output": "\ud83e\udd14 Researching for relevant information across multiple sources...\n", "metadata": null } }, { "timestamp": "2025-01-28T16:24:19.931140", "type": "event", "data": { "type": "logs", "content": "scraping_urls", "output": "\ud83c\udf10 Scraping content from 4 URLs...", "metadata": null } }, { "timestamp": "2025-01-28T16:24:21.903361", "type": "event", "data": { "type": "logs", "content": "scraping_content", "output": "\ud83d\udcc4 Scraped 3 pages of content", "metadata": null } }, { "timestamp": "2025-01-28T16:24:21.914524", "type": "event", "data": { "type": "logs", "content": "scraping_images", "output": "\ud83d\uddbc\ufe0f Selected 2 new images from 2 total images", "metadata": [ "https://techcrunch.com/wp-content/uploads/2024/04/GettyImages-1652364481.jpg?w=1024", "https://techcrunch.com/wp-content/uploads/2025/01/Screenshot-2025-01-20-at-11.31.39AM.png?w=680" ] } }, { "timestamp": "2025-01-28T16:24:21.920194", "type": "event", "data": { "type": "logs", "content": "scraping_complete", "output": "\ud83c\udf10 Scraping complete", "metadata": null } }, { "timestamp": "2025-01-28T16:24:21.935227", "type": "event", "data": { "type": "logs", "content": "fetching_query_content", "output": "\ud83d\udcda Getting relevant content based on query: DeepSeek R1 performance benchmarks reasoning tasks datasets...", "metadata": null } }, { "timestamp": "2025-01-28T16:24:22.052042", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://dataguy.in/deepseek-r1-open-source-ai/\n", "metadata": "https://dataguy.in/deepseek-r1-open-source-ai/" } }, { "timestamp": "2025-01-28T16:24:22.065616", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://opentools.ai/news/deepseek-r1-the-open-source-ai-champion-giving-openai-a-run-for-its-money\n", "metadata": "https://opentools.ai/news/deepseek-r1-the-open-source-ai-champion-giving-openai-a-run-for-its-money" } }, { "timestamp": "2025-01-28T16:24:22.072972", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://c3.unu.edu/blog/deepseek-r1-pioneering-open-source-thinking-model-and-its-impact-on-the-llm-landscape\n", "metadata": "https://c3.unu.edu/blog/deepseek-r1-pioneering-open-source-thinking-model-and-its-impact-on-the-llm-landscape" } }, { "timestamp": "2025-01-28T16:24:22.087330", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://apidog.com/blog/deepseek-r1-review-api/\n", "metadata": "https://apidog.com/blog/deepseek-r1-review-api/" } }, { "timestamp": "2025-01-28T16:24:22.107351", "type": "event", "data": { "type": "logs", "content": "added_source_url", "output": "\u2705 Added source url to research: https://decrypt.co/302161/chinese-open-source-ai-deepseek-r1-openai-o1\n", "metadata": "https://decrypt.co/302161/chinese-open-source-ai-deepseek-r1-openai-o1" } }, { "timestamp": "2025-01-28T16:24:22.124203", "type": "event", "data": { "type": "logs", "content": "researching", "output": "\ud83e\udd14 Researching for relevant information across multiple sources...\n", "metadata": null } }, { "timestamp": "2025-01-28T16:24:22.137356", "type": "event", "data": { "type": "logs", "content": "scraping_urls", "output": "\ud83c\udf10 Scraping content from 5 URLs...", "metadata": null } }, { "timestamp": "2025-01-28T16:24:23.483499", "type": "event", "data": { "type": "logs", "content": "scraping_content", "output": "\ud83d\udcc4 Scraped 5 pages of content", "metadata": null } }, { "timestamp": "2025-01-28T16:24:23.498196", "type": "event", "data": { "type": "logs", "content": "scraping_images", "output": "\ud83d\uddbc\ufe0f Selected 4 new images from 5 total images", "metadata": [ "https://assets.apidog.com/blog-next/2025/01/image-51.png", "https://assets.apidog.com/blog-next/2025/01/image-53.png", "https://assets.apidog.com/blog-next/2025/01/image-50.png", "https://assets.apidog.com/blog-next/2025/01/image-52.png" ] } }, { "timestamp": "2025-01-28T16:24:23.511877", "type": "event", "data": { "type": "logs", "content": "scraping_complete", "output": "\ud83c\udf10 Scraping complete", "metadata": null } }, { "timestamp": "2025-01-28T16:24:23.525810", "type": "event", "data": { "type": "logs", "content": "fetching_query_content", "output": "\ud83d\udcda Getting relevant content based on query: DeepSeek R1 open-source license cost advantages...", "metadata": null } }, { "timestamp": "2025-01-28T16:24:34.540057", "type": "event", "data": { "type": "logs", "content": "subquery_context_window", "output": "\ud83d\udcc3 Source: https://www.tysoolen.com/story/deepseek-r1-openai-o1-ultimate-benchmark-showdown\nTitle: DeepSeek R1 vs OpenAI o1: The Ultimate Benchmark Comparison\nContent: lead, outperforming o1 in terms of processing speed and result quality. These benchmarks highlight DeepSeek\u2019s focus on real-world applications where hybrid data types dominate. OpenAI o1 Comparison: A Legacy of Excellence OpenAI o1 isn\u2019t without its merits. With years of refinement and optimization, o1 excels in tasks requiring extensive context processing, such as summarizing lengthy documents or handling intricate conversational flows. Moreover, OpenAI o1 is backed by a robust ecosystem, making it an attractive choice for developers. From APIs to documentation, OpenAI\u2019s infrastructure remains a key differentiator in the DeepSeek vs OpenAI debate. R1 vs o1 Showdown: Performance Metrics Language Processing In language processing benchmarks, R1 scored higher in nuanced tasks like sarcasm detection and idiomatic expressions, highlighting its improved contextual understanding. Meanwhile, o1 maintained its edge in handling large-scale datasets more efficiently, underscoring its\n\nSource: https://www.tysoolen.com/story/deepseek-r1-openai-o1-ultimate-benchmark-showdown\nTitle: DeepSeek R1 vs OpenAI o1: The Ultimate Benchmark Comparison\nContent: lead, outperforming o1 in terms of processing speed and result quality. These benchmarks highlight DeepSeek\u2019s focus on real-world applications where hybrid data types dominate. OpenAI o1 Comparison: A Legacy of Excellence OpenAI o1 isn\u2019t without its merits. With years of refinement and optimization, o1 excels in tasks requiring extensive context processing, such as summarizing lengthy documents or handling intricate conversational flows. Moreover, OpenAI o1 is backed by a robust ecosystem, making it an attractive choice for developers. From APIs to documentation, OpenAI\u2019s infrastructure remains a key differentiator in the DeepSeek vs OpenAI debate. R1 vs o1 Showdown: Performance Metrics Language Processing In language processing benchmarks, R1 scored higher in nuanced tasks like sarcasm detection and idiomatic expressions, highlighting its improved contextual understanding. Meanwhile, o1 maintained its edge in handling large-scale datasets more efficiently, underscoring its\n\nSource: https://www.tysoolen.com/story/deepseek-r1-openai-o1-ultimate-benchmark-showdown\nTitle: DeepSeek R1 vs OpenAI o1: The Ultimate Benchmark Comparison\nContent: lead, outperforming o1 in terms of processing speed and result quality. These benchmarks highlight DeepSeek\u2019s focus on real-world applications where hybrid data types dominate. OpenAI o1 Comparison: A Legacy of Excellence OpenAI o1 isn\u2019t without its merits. With years of refinement and optimization, o1 excels in tasks requiring extensive context processing, such as summarizing lengthy documents or handling intricate conversational flows. Moreover, OpenAI o1 is backed by a robust ecosystem, making it an attractive choice for developers. From APIs to documentation, OpenAI\u2019s infrastructure remains a key differentiator in the DeepSeek vs OpenAI debate. R1 vs o1 Showdown: Performance Metrics Language Processing In language processing benchmarks, R1 scored higher in nuanced tasks like sarcasm detection and idiomatic expressions, highlighting its improved contextual understanding. Meanwhile, o1 maintained its edge in handling large-scale datasets more efficiently, underscoring its\n\nSource: https://www.tysoolen.com/story/deepseek-r1-openai-o1-ultimate-benchmark-showdown\nTitle: DeepSeek R1 vs OpenAI o1: The Ultimate Benchmark Comparison\nContent: lead, outperforming o1 in terms of processing speed and result quality. These benchmarks highlight DeepSeek\u2019s focus on real-world applications where hybrid data types dominate. OpenAI o1 Comparison: A Legacy of Excellence OpenAI o1 isn\u2019t without its merits. With years of refinement and optimization, o1 excels in tasks requiring extensive context processing, such as summarizing lengthy documents or handling intricate conversational flows. Moreover, OpenAI o1 is backed by a robust ecosystem, making it an attractive choice for developers. From APIs to documentation, OpenAI\u2019s infrastructure remains a key differentiator in the DeepSeek vs OpenAI debate. R1 vs o1 Showdown: Performance Metrics Language Processing In language processing benchmarks, R1 scored higher in nuanced tasks like sarcasm detection and idiomatic expressions, highlighting its improved contextual understanding. Meanwhile, o1 maintained its edge in handling large-scale datasets more efficiently, underscoring its\n\nSource: https://www.tysoolen.com/story/deepseek-r1-openai-o1-ultimate-benchmark-showdown\nTitle: DeepSeek R1 vs OpenAI o1: The Ultimate Benchmark Comparison\nContent: lead, outperforming o1 in terms of processing speed and result quality. These benchmarks highlight DeepSeek\u2019s focus on real-world applications where hybrid data types dominate. OpenAI o1 Comparison: A Legacy of Excellence OpenAI o1 isn\u2019t without its merits. With years of refinement and optimization, o1 excels in tasks requiring extensive context processing, such as summarizing lengthy documents or handling intricate conversational flows. Moreover, OpenAI o1 is backed by a robust ecosystem, making it an attractive choice for developers. From APIs to documentation, OpenAI\u2019s infrastructure remains a key differentiator in the DeepSeek vs OpenAI debate. R1 vs o1 Showdown: Performance Metrics Language Processing In language processing benchmarks, R1 scored higher in nuanced tasks like sarcasm detection and idiomatic expressions, highlighting its improved contextual understanding. Meanwhile, o1 maintained its edge in handling large-scale datasets more efficiently, underscoring its\n\nSource: https://www.tysoolen.com/story/deepseek-r1-openai-o1-ultimate-benchmark-showdown\nTitle: DeepSeek R1 vs OpenAI o1: The Ultimate Benchmark Comparison\nContent: lead, outperforming o1 in terms of processing speed and result quality. These benchmarks highlight DeepSeek\u2019s focus on real-world applications where hybrid data types dominate. OpenAI o1 Comparison: A Legacy of Excellence OpenAI o1 isn\u2019t without its merits. With years of refinement and optimization, o1 excels in tasks requiring extensive context processing, such as summarizing lengthy documents or handling intricate conversational flows. Moreover, OpenAI o1 is backed by a robust ecosystem, making it an attractive choice for developers. From APIs to documentation, OpenAI\u2019s infrastructure remains a key differentiator in the DeepSeek vs OpenAI debate. R1 vs o1 Showdown: Performance Metrics Language Processing In language processing benchmarks, R1 scored higher in nuanced tasks like sarcasm detection and idiomatic expressions, highlighting its improved contextual understanding. Meanwhile, o1 maintained its edge in handling large-scale datasets more efficiently, underscoring its\n\nSource: https://www.tysoolen.com/story/deepseek-r1-openai-o1-ultimate-benchmark-showdown\nTitle: DeepSeek R1 vs OpenAI o1: The Ultimate Benchmark Comparison\nContent: lead, outperforming o1 in terms of processing speed and result quality. These benchmarks highlight DeepSeek\u2019s focus on real-world applications where hybrid data types dominate. OpenAI o1 Comparison: A Legacy of Excellence OpenAI o1 isn\u2019t without its merits. With years of refinement and optimization, o1 excels in tasks requiring extensive context processing, such as summarizing lengthy documents or handling intricate conversational flows. Moreover, OpenAI o1 is backed by a robust ecosystem, making it an attractive choice for developers. From APIs to documentation, OpenAI\u2019s infrastructure remains a key differentiator in the DeepSeek vs OpenAI debate. R1 vs o1 Showdown: Performance Metrics Language Processing In language processing benchmarks, R1 scored higher in nuanced tasks like sarcasm detection and idiomatic expressions, highlighting its improved contextual understanding. Meanwhile, o1 maintained its edge in handling large-scale datasets more efficiently, underscoring its\n\nSource: https://www.tysoolen.com/story/deepseek-r1-openai-o1-ultimate-benchmark-showdown\nTitle: DeepSeek R1 vs OpenAI o1: The Ultimate Benchmark Comparison\nContent: lead, outperforming o1 in terms of processing speed and result quality. These benchmarks highlight DeepSeek\u2019s focus on real-world applications where hybrid data types dominate. OpenAI o1 Comparison: A Legacy of Excellence OpenAI o1 isn\u2019t without its merits. With years of refinement and optimization, o1 excels in tasks requiring extensive context processing, such as summarizing lengthy documents or handling intricate conversational flows. Moreover, OpenAI o1 is backed by a robust ecosystem, making it an attractive choice for developers. From APIs to documentation, OpenAI\u2019s infrastructure remains a key differentiator in the DeepSeek vs OpenAI debate. R1 vs o1 Showdown: Performance Metrics Language Processing In language processing benchmarks, R1 scored higher in nuanced tasks like sarcasm detection and idiomatic expressions, highlighting its improved contextual understanding. Meanwhile, o1 maintained its edge in handling large-scale datasets more efficiently, underscoring its\n\nSource: https://www.tysoolen.com/story/deepseek-r1-openai-o1-ultimate-benchmark-showdown\nTitle: DeepSeek R1 vs OpenAI o1: The Ultimate Benchmark Comparison\nContent: lead, outperforming o1 in terms of processing speed and result quality. These benchmarks highlight DeepSeek\u2019s focus on real-world applications where hybrid data types dominate. OpenAI o1 Comparison: A Legacy of Excellence OpenAI o1 isn\u2019t without its merits. With years of refinement and optimization, o1 excels in tasks requiring extensive context processing, such as summarizing lengthy documents or handling intricate conversational flows. Moreover, OpenAI o1 is backed by a robust ecosystem, making it an attractive choice for developers. From APIs to documentation, OpenAI\u2019s infrastructure remains a key differentiator in the DeepSeek vs OpenAI debate. R1 vs o1 Showdown: Performance Metrics Language Processing In language processing benchmarks, R1 scored higher in nuanced tasks like sarcasm detection and idiomatic expressions, highlighting its improved contextual understanding. Meanwhile, o1 maintained its edge in handling large-scale datasets more efficiently, underscoring its\n\nSource: https://www.tysoolen.com/story/deepseek-r1-openai-o1-ultimate-benchmark-showdown\nTitle: DeepSeek R1 vs OpenAI o1: The Ultimate Benchmark Comparison\nContent: Conclusion: R1 Dethrones o1?\nSo, can DeepSeek R1 dethrone OpenAI o1? The answer isn\u2019t straightforward. While R1 outperforms o1 in certain benchmarks, o1\u2019s robustness and ecosystem remain compelling. The R1 vs o1 showdown is far from over, but one thing is clear\u2014DeepSeek R1 has firmly established itself as a worthy competitor.\nWhat are the key differences between DeepSeek R1 and OpenAI o1?DeepSeek R1 focuses on efficiency and innovation, while OpenAI o1 offers scalability and a robust ecosystem.\nWhat are the key differences between DeepSeek R1 and OpenAI o1?DeepSeek R1 focuses on efficiency and innovation, while OpenAI o1 offers scalability and a robust ecosystem.\nHow do DeepSeek R1 benchmarks compare to OpenAI o1?R1 outperforms o1 in language comprehension and multi-modal tasks, while o1 excels in large-scale data processing.\n", "metadata": null } }, { "timestamp": "2025-01-28T16:24:37.685996", "type": "event", "data": { "type": "logs", "content": "subquery_context_window", "output": "\ud83d\udcc3 Source: https://opentools.ai/news/deepseek-r1-the-open-source-ai-champion-giving-openai-a-run-for-its-money\nTitle: DeepSeek R1: The Open-Source AI Champion Giving OpenAI a Run for Its Money | AI News\nContent: The public reception of DeepSeek R1 has been overwhelmingly positive, fueled by its open-source nature and the stark cost advantages it presents. Users across the globe, particularly in smaller enterprises and academic circles, have praised its accessibility and the democratization of AI capabilities it represents. However, as with any emerging technology, concerns have surfaced regarding potential biases inherent in its training data and the security implications of an open-source model.\n\nSource: https://opentools.ai/news/deepseek-r1-the-open-source-ai-champion-giving-openai-a-run-for-its-money\nTitle: DeepSeek R1: The Open-Source AI Champion Giving OpenAI a Run for Its Money | AI News\nContent: The accessibility of DeepSeek R1 is further highlighted by its MIT license, allowing developers to freely use and modify the model without restrictions. This open-source approach not only democratizes access to advanced AI tools but also encourages innovation by enabling developers to build upon the existing framework. Such accessibility not only fosters a collaborative environment but also accelerates the adoption of AI technology across various sectors, from small businesses to academic institutions.\nMoreover, the development community is abuzz with enthusiasm, as the availability of R1 on platforms like Github and Hugging Face lowers the barrier for entry. This, coupled with its low-cost structure, makes sophisticated AI capabilities more attainable for developers worldwide, fostering an environment where experimentation and customization thrive.\n\nSource: https://opentools.ai/news/deepseek-r1-the-open-source-ai-champion-giving-openai-a-run-for-its-money\nTitle: DeepSeek R1: The Open-Source AI Champion Giving OpenAI a Run for Its Money | AI News\nContent: The model's substantial cost reduction has not only drawn commendation but also stimulated a broader discussion on the sustainability and accessibility of AI technologies. Stakeholders, ranging from developers to academic researchers, have expressed enthusiasm for the open-source nature and economic viability of R1, which could spur further innovations in AI applications and broaden its accessibility to a wider audience.Amidst these advantages, the implications for the AI industry are profound, signifying a shift toward more collaborative, open-source developmental methodologies. As open-source AI models like DeepSeek R1 continue to gain traction, they are likely to spur a wave of innovation, enhance collaborative research, and give rise to new market opportunities focused on AI optimization and customization services. These dynamics indicate a vibrant future where cost efficiency becomes central to AI advancement and adoption.Accessibility and Developer EngagementAs the digital\n\nSource: https://opentools.ai/news/deepseek-r1-the-open-source-ai-champion-giving-openai-a-run-for-its-money\nTitle: DeepSeek R1: The Open-Source AI Champion Giving OpenAI a Run for Its Money | AI News\nContent: The model's substantial cost reduction has not only drawn commendation but also stimulated a broader discussion on the sustainability and accessibility of AI technologies. Stakeholders, ranging from developers to academic researchers, have expressed enthusiasm for the open-source nature and economic viability of R1, which could spur further innovations in AI applications and broaden its accessibility to a wider audience.Amidst these advantages, the implications for the AI industry are profound, signifying a shift toward more collaborative, open-source developmental methodologies. As open-source AI models like DeepSeek R1 continue to gain traction, they are likely to spur a wave of innovation, enhance collaborative research, and give rise to new market opportunities focused on AI optimization and customization services. These dynamics indicate a vibrant future where cost efficiency becomes central to AI advancement and adoption.Accessibility and Developer EngagementAs the digital\n\nSource: https://opentools.ai/news/deepseek-r1-the-open-source-ai-champion-giving-openai-a-run-for-its-money\nTitle: DeepSeek R1: The Open-Source AI Champion Giving OpenAI a Run for Its Money | AI News\nContent: Learn to use AI like a Pro\nGet the latest AI workflows to boost your productivity and business performance, delivered weekly by expert consultants. Enjoy step-by-step guides, weekly Q&A sessions, and full access to our AI workflow archive.\nLearn More (And Unlock 50% off!)\nFurthermore, the public reaction to DeepSeek R1 has underscored its economic impact. The model's substantial cost reduction has not only drawn commendation but also stimulated a broader discussion on the sustainability and accessibility of AI technologies. Stakeholders, ranging from developers to academic researchers, have expressed enthusiasm for the open-source nature and economic viability of R1, which could spur further innovations in AI applications and broaden its accessibility to a wider audience.\n\nSource: https://opentools.ai/news/deepseek-r1-the-open-source-ai-champion-giving-openai-a-run-for-its-money\nTitle: DeepSeek R1: The Open-Source AI Champion Giving OpenAI a Run for Its Money | AI News\nContent: Furthermore, the public reaction to DeepSeek R1 has underscored its economic impact. The model's substantial cost reduction has not only drawn commendation but also stimulated a broader discussion on the sustainability and accessibility of AI technologies. Stakeholders, ranging from developers to academic researchers, have expressed enthusiasm for the open-source nature and economic viability of R1, which could spur further innovations in AI applications and broaden its accessibility to a wider audience.\n\nSource: https://opentools.ai/news/deepseek-r1-the-open-source-ai-champion-giving-openai-a-run-for-its-money\nTitle: DeepSeek R1: The Open-Source AI Champion Giving OpenAI a Run for Its Money | AI News\nContent: Furthermore, the public reaction to DeepSeek R1 has underscored its economic impact. The model's substantial cost reduction has not only drawn commendation but also stimulated a broader discussion on the sustainability and accessibility of AI technologies. Stakeholders, ranging from developers to academic researchers, have expressed enthusiasm for the open-source nature and economic viability of R1, which could spur further innovations in AI applications and broaden its accessibility to a wider audience.\n\nSource: https://opentools.ai/news/deepseek-r1-the-open-source-ai-champion-giving-openai-a-run-for-its-money\nTitle: DeepSeek R1: The Open-Source AI Champion Giving OpenAI a Run for Its Money | AI News\nContent: The public reception of DeepSeek R1 has been overwhelmingly positive, fueled by its open-source nature and the stark cost advantages it presents. Users across the globe, particularly in smaller enterprises and academic circles, have praised its accessibility and the democratization of AI capabilities it represents. However, as with any emerging technology, concerns have surfaced regarding potential biases inherent in its training data and the security implications of an open-source model.\nThe public reception of DeepSeek R1 has been overwhelmingly positive, fueled by its open-source nature and the stark cost advantages it presents. Users across the globe, particularly in smaller enterprises and academic circles, have praised its accessibility and the democratization of AI capabilities it represents. However, as with any emerging technology, concerns have surfaced regarding potential biases inherent in its training data and the security implications of an open-source model.\n\nSource: https://opentools.ai/news/deepseek-r1-the-open-source-ai-champion-giving-openai-a-run-for-its-money\nTitle: DeepSeek R1: The Open-Source AI Champion Giving OpenAI a Run for Its Money | AI News\nContent: Amidst these advantages, the implications for the AI industry are profound, signifying a shift toward more collaborative, open-source developmental methodologies. As open-source AI models like DeepSeek R1 continue to gain traction, they are likely to spur a wave of innovation, enhance collaborative research, and give rise to new market opportunities focused on AI optimization and customization services. These dynamics indicate a vibrant future where cost efficiency becomes central to AI advancement and adoption.\n\nSource: https://opentools.ai/news/deepseek-r1-the-open-source-ai-champion-giving-openai-a-run-for-its-money\nTitle: DeepSeek R1: The Open-Source AI Champion Giving OpenAI a Run for Its Money | AI News\nContent: Amidst these advantages, the implications for the AI industry are profound, signifying a shift toward more collaborative, open-source developmental methodologies. As open-source AI models like DeepSeek R1 continue to gain traction, they are likely to spur a wave of innovation, enhance collaborative research, and give rise to new market opportunities focused on AI optimization and customization services. These dynamics indicate a vibrant future where cost efficiency becomes central to AI advancement and adoption.\n", "metadata": null } }, { "timestamp": "2025-01-28T16:24:37.947209", "type": "event", "data": { "type": "logs", "content": "subquery_context_window", "output": "\ud83d\udcc3 Source: https://unfoldai.com/deepseek-r1/\nTitle: DeepSeek-R1 \u2014 Training Language Models to reason through Reinforcement Learning | UnfoldAI\nContent: handle sequences up to 128K tokens in length. The architecture\u2019s efficiency becomes apparent in the model\u2019s ability to generate thousands of reasoning tokens per response while maintaining coherence and accuracy throughout extended chains of thought. Implementation overview The core innovation in DeepSeek-R1 lies in its training approach. Instead of relying on supervised fine-tuning, the initial model (DeepSeek-R1-Zero) uses pure reinforcement learning to develop reasoning capabilities. This approach begins with the base model and employs Group Relative Policy Optimization (GRPO), eliminating the need for a separate critic model. The GRPO implementation uses a reward function that combines accuracy and format adherence: def compute_reward(response, ground_truth): accuracy_reward = evaluate_correctness(response, ground_truth) format_reward = check_formatting(response) return accuracy_reward + format_reward * format_weight Training pipeline The training process consists of four distinct\n\nSource: https://unfoldai.com/deepseek-r1/\nTitle: DeepSeek-R1 \u2014 Training Language Models to reason through Reinforcement Learning | UnfoldAI\nContent: handle sequences up to 128K tokens in length. The architecture\u2019s efficiency becomes apparent in the model\u2019s ability to generate thousands of reasoning tokens per response while maintaining coherence and accuracy throughout extended chains of thought. Implementation overview The core innovation in DeepSeek-R1 lies in its training approach. Instead of relying on supervised fine-tuning, the initial model (DeepSeek-R1-Zero) uses pure reinforcement learning to develop reasoning capabilities. This approach begins with the base model and employs Group Relative Policy Optimization (GRPO), eliminating the need for a separate critic model. The GRPO implementation uses a reward function that combines accuracy and format adherence: def compute_reward(response, ground_truth): accuracy_reward = evaluate_correctness(response, ground_truth) format_reward = check_formatting(response) return accuracy_reward + format_reward * format_weight Training pipeline The training process consists of four distinct\n\nSource: https://unfoldai.com/deepseek-r1/\nTitle: DeepSeek-R1 \u2014 Training Language Models to reason through Reinforcement Learning | UnfoldAI\nContent: handle sequences up to 128K tokens in length. The architecture\u2019s efficiency becomes apparent in the model\u2019s ability to generate thousands of reasoning tokens per response while maintaining coherence and accuracy throughout extended chains of thought. Implementation overview The core innovation in DeepSeek-R1 lies in its training approach. Instead of relying on supervised fine-tuning, the initial model (DeepSeek-R1-Zero) uses pure reinforcement learning to develop reasoning capabilities. This approach begins with the base model and employs Group Relative Policy Optimization (GRPO), eliminating the need for a separate critic model. The GRPO implementation uses a reward function that combines accuracy and format adherence: def compute_reward(response, ground_truth): accuracy_reward = evaluate_correctness(response, ground_truth) format_reward = check_formatting(response) return accuracy_reward + format_reward * format_weight Training pipeline The training process consists of four distinct\n\nSource: https://unfoldai.com/deepseek-r1/\nTitle: DeepSeek-R1 \u2014 Training Language Models to reason through Reinforcement Learning | UnfoldAI\nContent: of reasoning tokens per response while maintaining coherence and accuracy throughout extended chains of thought. Implementation overview The core innovation in DeepSeek-R1 lies in its training approach. Instead of relying on supervised fine-tuning, the initial model (DeepSeek-R1-Zero) uses pure reinforcement learning to develop reasoning capabilities. This approach begins with the base model and employs Group Relative Policy Optimization (GRPO), eliminating the need for a separate critic model. The GRPO implementation uses a reward function that combines accuracy and format adherence: def compute_reward(response, ground_truth): accuracy_reward = evaluate_correctness(response, ground_truth) format_reward = check_formatting(response) return accuracy_reward + format_reward * format_weight Training pipeline The training process consists of four distinct phases. The initial phase applies RL directly to the base model, generating DeepSeek-R1-Zero. This model achieves a 71.0% accuracy on\n\nSource: https://medium.com/@namnguyenthe/deepseek-r1-architecture-and-training-explain-83319903a684\nTitle: DeepSeek-R1: Architecture and training explain | by The Nam | Jan, 2025 | Medium\nContent: DeepSeek-R1 aims to improve from the Zero by incorporating a multi-stage post-training process.\nIn contrast to R1-Zero, R1 began with Supervised Fine-Tuning (SFT) to overcome the cold-start phase of RL. The labels were first generated by directly prompting R1-Zero for answers using a few-shot demonstration. These labels were then refined through post-processing by human annotators. Thousands of cold-start samples were collected for fine-tuning during this step.\nAfter fine-tuning DeepSeek-V3-Base on the cold-start data, the authors applied the same large-scale reinforcement learning training process used in R1-Zero. This phase focused on enhancing the model\u2019s reasoning capabilities. To address the language mixing issue, they introduced a language consistency reward during RL training, which is calculated as the proportion of target language words in the Chain-of-Thought (CoT).\n\nSource: https://aipapersacademy.com/deepseek-r1/\nTitle: DeepSeek-R1 Paper Explained - A New RL LLMs Era in AI?\nContent: To address these issues, DeepSeek-R1 is trained in a four phases pipeline:\nCold Start (Phase 1): Starting with the pre-trained model DeepSeek-V3-Base, the model undergoes supervised fine-tuning on a small dataset of results collected from DeepSeek-R1-Zero. These results were validated as high-quality and readable. This dataset contains thousands of samples, making it relatively small. Incorporating a supervised fine-tuning phase on this small, high-quality dataset helps DeepSeek-R1 mitigate the readability issues observed in the initial model.\nReasoning Reinforcement Learning (Phase 2): This phase applies the same large-scale reinforcement learning we\u2019ve reviewed for the previous model to enhance the model\u2019s reasoning capabilities. Specifically, in tasks such as coding, math, science and logic reasoning, where clear solutions can define rewarding rules for the reinforcement learning process.\n\nSource: https://www.vellum.ai/blog/the-training-of-deepseek-r1-and-ways-to-use-it\nTitle: How DeepSeek-R1 Was Built; For dummies\nContent: these challenges. In the case of training the DeepSeek-R1 model, a lot of training methods were used:Here\u00e2\u0080\u0099s a quick explanation of each training stage and what it was done:Step 1: They fine-tuned a base model (DeepSeek-V3-Base) with thousands of cold-start data points to lay a solid foundation. FYI, thousands of cold-start data points is a tiny fraction compared to the millions or even billions of labeled data points typically required for supervised learning at scale.Step 2: Applied pure RL (similar to R1-Zero) to enhance reasoning skills.Step 3: Near RL convergence, they used rejection sampling where the model created it\u00e2\u0080\u0099s own labeled data (synthetic data) by selecting the best examples from the last successful RL run. Those rumors you've heard about OpenAI using smaller model to generate synthetic data for the O1 model? This is basically it.Step 4: The new synthetic data was merged with supervised data from DeepSeek-V3-Base in domains like writing, factual QA, and\n\nSource: https://www.vellum.ai/blog/the-training-of-deepseek-r1-and-ways-to-use-it\nTitle: How DeepSeek-R1 Was Built; For dummies\nContent: these challenges. In the case of training the DeepSeek-R1 model, a lot of training methods were used:Here\u00e2\u0080\u0099s a quick explanation of each training stage and what it was done:Step 1: They fine-tuned a base model (DeepSeek-V3-Base) with thousands of cold-start data points to lay a solid foundation. FYI, thousands of cold-start data points is a tiny fraction compared to the millions or even billions of labeled data points typically required for supervised learning at scale.Step 2: Applied pure RL (similar to R1-Zero) to enhance reasoning skills.Step 3: Near RL convergence, they used rejection sampling where the model created it\u00e2\u0080\u0099s own labeled data (synthetic data) by selecting the best examples from the last successful RL run. Those rumors you've heard about OpenAI using smaller model to generate synthetic data for the O1 model? This is basically it.Step 4: The new synthetic data was merged with supervised data from DeepSeek-V3-Base in domains like writing, factual QA, and\n\nSource: https://aipapersacademy.com/deepseek-r1/\nTitle: DeepSeek-R1 Paper Explained - A New RL LLMs Era in AI?\nContent: presents a state-of-the-art, open-source reasoning model and a detailed recipe for training such models using large-scale reinforcement learning techniques. DeepSeek-R1 paper title (Source) Recap: LLMs Training Process LLMs Training Process Before we dive into the paper itself, let\u2019s briefly recap the training process for LLMs. Typically, LLMs undergo three main stages of training: Pre-training: In this stage, LLMs are pre-trained on vast amounts of text and code to learn general-purpose knowledge. This step helps the model become proficient at predicting the next token in a sequence. For example, given an input like \u201cwrite a bedtime _,\u201d the model can complete it with a reasonable word, such as \u201cstory.\u201d However, after pre-training, the model still struggles to follow human instructions. The next stage addresses this. Supervised Fine-tuning: In this stage, the model is fine-tuned on an instruction dataset. Each sample from the dataset consists of an instruction-response pair, where the\n\nSource: https://unfoldai.com/deepseek-r1/\nTitle: DeepSeek-R1 \u2014 Training Language Models to reason through Reinforcement Learning | UnfoldAI\nContent: The training pipeline combines pure RL (DeepSeek-R1-Zero) with cold-start data and iterative fine-tuning, enabling deployment on consumer hardware through distilled versions as small as 1.5B parameters. Important links: https://huggingface.co/deepseek-ai/DeepSeek-R1 (original model card) https://www.deepseek.com/ (Official website) https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf (technical paper) https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B (Distilled model, based on Qwen \u2013 1.5B) https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B (Distilled model, based on Qwen \u2013 7B) https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B (Distilled model, based on Qwen \u2013 32B) https://ollama.com/library/deepseek-r1 (Ollama DeepSeek R1) https://unsloth.ai/blog/deepseek-r1 (DeepSeek R1 in Unsloth) Model architecture DeepSeek-R1 builds upon the Mixture of Experts (MoE) architecture from its base model DeepSeek-V3, employing a sparse activation\n", "metadata": null } }, { "timestamp": "2025-01-28T16:24:39.492109", "type": "event", "data": { "type": "logs", "content": "subquery_context_window", "output": "\ud83d\udcc3 Source: https://www.prompthub.us/blog/deepseek-r-1-model-overview-and-how-it-ranks-against-openais-o1\nTitle: DeepSeek R-1 Model Overview and How it Ranks Against OpenAI's o1\nContent: Performance Benchmarks DeepSeek\u00e2\u0080\u0099s R1 model performs on par with OpenAI\u00e2\u0080\u0099s A1 models across many reasoning benchmarks: Reasoning and Math Tasks: R1 rivals or outperforms A1 models in accuracy and depth of reasoning. Coding Tasks: A1 models generally perform better in LiveCode Bench and CodeForces tasks. Simple QA: R1 often outpaces A1 in structured QA tasks (e.g., 47% accuracy vs. 30%). One notable finding is that longer reasoning chains generally improve performance. This aligns with insights from Microsoft\u00e2\u0080\u0099s Med-Prompt framework and OpenAI\u00e2\u0080\u0099s observations on test-time compute and reasoning depth. Challenges and Observations Despite its strengths, R1 has some limitations: Mixing English and Chinese responses due to a lack of supervised fine-tuning. Less polished responses compared to chat models like OpenAI\u00e2\u0080\u0099s GPT. These issues were addressed during R1\u00e2\u0080\u0099s refinement process, including supervised fine-tuning and human feedback. Prompt Engineering Insights A fascinating takeaway\n\nSource: https://www.prompthub.us/blog/deepseek-r-1-model-overview-and-how-it-ranks-against-openais-o1\nTitle: DeepSeek R-1 Model Overview and How it Ranks Against OpenAI's o1\nContent: Performance Benchmarks DeepSeek\u00e2\u0080\u0099s R1 model performs on par with OpenAI\u00e2\u0080\u0099s A1 models across many reasoning benchmarks: Reasoning and Math Tasks: R1 rivals or outperforms A1 models in accuracy and depth of reasoning. Coding Tasks: A1 models generally perform better in LiveCode Bench and CodeForces tasks. Simple QA: R1 often outpaces A1 in structured QA tasks (e.g., 47% accuracy vs. 30%). One notable finding is that longer reasoning chains generally improve performance. This aligns with insights from Microsoft\u00e2\u0080\u0099s Med-Prompt framework and OpenAI\u00e2\u0080\u0099s observations on test-time compute and reasoning depth. Challenges and Observations Despite its strengths, R1 has some limitations: Mixing English and Chinese responses due to a lack of supervised fine-tuning. Less polished responses compared to chat models like OpenAI\u00e2\u0080\u0099s GPT. These issues were addressed during R1\u00e2\u0080\u0099s refinement process, including supervised fine-tuning and human feedback. Prompt Engineering Insights A fascinating takeaway\n\nSource: https://www.prompthub.us/blog/deepseek-r-1-model-overview-and-how-it-ranks-against-openais-o1\nTitle: DeepSeek R-1 Model Overview and How it Ranks Against OpenAI's o1\nContent: Benchmarks DeepSeek\u00e2\u0080\u0099s R1 model performs on par with OpenAI\u00e2\u0080\u0099s A1 models across many reasoning benchmarks: Reasoning and Math Tasks: R1 rivals or outperforms A1 models in accuracy and depth of reasoning. Coding Tasks: A1 models generally perform better in LiveCode Bench and CodeForces tasks. Simple QA: R1 often outpaces A1 in structured QA tasks (e.g., 47% accuracy vs. 30%). One notable finding is that longer reasoning chains generally improve performance. This aligns with insights from Microsoft\u00e2\u0080\u0099s Med-Prompt framework and OpenAI\u00e2\u0080\u0099s observations on test-time compute and reasoning depth. Challenges and Observations Despite its strengths, R1 has some limitations: Mixing English and Chinese responses due to a lack of supervised fine-tuning. Less polished responses compared to chat models like OpenAI\u00e2\u0080\u0099s GPT. These issues were addressed during R1\u00e2\u0080\u0099s refinement process, including supervised fine-tuning and human feedback. Prompt Engineering Insights A fascinating takeaway from\n\nSource: https://www.prompthub.us/blog/deepseek-r-1-model-overview-and-how-it-ranks-against-openais-o1\nTitle: DeepSeek R-1 Model Overview and How it Ranks Against OpenAI's o1\nContent: Distillation to Smaller Models:\nDeepSeek-R1\u00e2\u0080\u0099s reasoning capabilities were distilled into smaller, efficient models like Qwen and Llama-3.1-8B, and Llama-3.3-70B-Instruct\nDeepSeek R-1 performance\nThe researchers tested DeepSeek R-1 across a variety of benchmarks and against top models: o1, GPT-4o, and Claude 3.5 Sonnet, o1-mini.\nThe benchmarks were broken down into several categories, shown below in the table: English, Code, Math, and Chinese.\nThe following parameters were applied across all models:\nMaximum generation length: 32,768 tokens.\nSampling configuration:Temperature: 0.6.Top-p value: 0.95.\nTop-p value: 0.95.\nPass@1 estimation: Generated 64 responses per query.\nDeepSeek R1 outperformed o1, Claude 3.5 Sonnet and other models in the majority of reasoning benchmarks\no1 was the best-performing model in four out of the five coding-related benchmarks\nDeepSeek performed well on creative and long-context task task, like AlpacaEval 2.0 and ArenaHard, outperforming all other models\n\nSource: https://www.prompthub.us/blog/deepseek-r-1-model-overview-and-how-it-ranks-against-openais-o1\nTitle: DeepSeek R-1 Model Overview and How it Ranks Against OpenAI's o1\nContent: Distillation into smaller models (LLaMA 3.1 and 3.3 at various sizes).\nDeepSeek\u00e2\u0080\u0099s R1 model performs on par with OpenAI\u00e2\u0080\u0099s A1 models across many reasoning benchmarks:\nReasoning and Math Tasks: R1 rivals or outperforms A1 models in accuracy and depth of reasoning.\nCoding Tasks: A1 models generally perform better in LiveCode Bench and CodeForces tasks.\nSimple QA: R1 often outpaces A1 in structured QA tasks (e.g., 47% accuracy vs. 30%).\nOne notable finding is that longer reasoning chains generally improve performance. This aligns with insights from Microsoft\u00e2\u0080\u0099s Med-Prompt framework and OpenAI\u00e2\u0080\u0099s observations on test-time compute and reasoning depth.\nChallenges and Observations\nDespite its strengths, R1 has some limitations:\nMixing English and Chinese responses due to a lack of supervised fine-tuning.\nLess polished responses compared to chat models like OpenAI\u00e2\u0080\u0099s GPT.\nThese issues were addressed during R1\u00e2\u0080\u0099s refinement process, including supervised fine-tuning and human feedback.\n\nSource: https://www.prompthub.us/blog/deepseek-r-1-model-overview-and-how-it-ranks-against-openais-o1\nTitle: DeepSeek R-1 Model Overview and How it Ranks Against OpenAI's o1\nContent: on R10, R1 added several enhancements: Curated datasets with long Chain of Thought examples. Incorporation of R10-generated reasoning chains. Human preference alignment for polished responses. Distillation into smaller models (LLaMA 3.1 and 3.3 at various sizes). Performance Benchmarks DeepSeek\u00e2\u0080\u0099s R1 model performs on par with OpenAI\u00e2\u0080\u0099s A1 models across many reasoning benchmarks: Reasoning and Math Tasks: R1 rivals or outperforms A1 models in accuracy and depth of reasoning. Coding Tasks: A1 models generally perform better in LiveCode Bench and CodeForces tasks. Simple QA: R1 often outpaces A1 in structured QA tasks (e.g., 47% accuracy vs. 30%). One notable finding is that longer reasoning chains generally improve performance. This aligns with insights from Microsoft\u00e2\u0080\u0099s Med-Prompt framework and OpenAI\u00e2\u0080\u0099s observations on test-time compute and reasoning depth. Challenges and Observations Despite its strengths, R1 has some limitations: Mixing English and Chinese responses due to a\n\nSource: https://www.prompthub.us/blog/deepseek-r-1-model-overview-and-how-it-ranks-against-openais-o1\nTitle: DeepSeek R-1 Model Overview and How it Ranks Against OpenAI's o1\nContent: refine its reasoning capabilities furtherHuman Preference Alignment:A secondary RL stage improved the model\u00e2\u0080\u0099s helpfulness and harmlessness, ensuring better alignment with user needsDistillation to Smaller Models:DeepSeek-R1\u00e2\u0080\u0099s reasoning capabilities were distilled into smaller, efficient models like Qwen and Llama-3.1-8B, and Llama-3.3-70B-Instruct\u00e2\u0080\u008dDeepSeek R-1 performanceThe researchers tested DeepSeek R-1 across a variety of benchmarks and against top models: o1, GPT-4o, and Claude 3.5 Sonnet, o1-mini.The benchmarks were broken down into several categories, shown below in the table: English, Code, Math, and Chinese.SetupThe following parameters were applied across all models:Maximum generation length: 32,768 tokens.Sampling configuration:Temperature: 0.6.Top-p value: 0.95.Pass@1 estimation: Generated 64 responses per query.\u00e2\u0080\u008d\u00e2\u0080\u008dDeepSeek R1 outperformed o1, Claude 3.5 Sonnet and other models in the majority of reasoning benchmarkso1 was the best-performing model in four out of\n\nSource: https://www.prompthub.us/blog/deepseek-r-1-model-overview-and-how-it-ranks-against-openais-o1\nTitle: DeepSeek R-1 Model Overview and How it Ranks Against OpenAI's o1\nContent: refine its reasoning capabilities furtherHuman Preference Alignment:A secondary RL stage improved the model\u00e2\u0080\u0099s helpfulness and harmlessness, ensuring better alignment with user needsDistillation to Smaller Models:DeepSeek-R1\u00e2\u0080\u0099s reasoning capabilities were distilled into smaller, efficient models like Qwen and Llama-3.1-8B, and Llama-3.3-70B-Instruct\u00e2\u0080\u008dDeepSeek R-1 performanceThe researchers tested DeepSeek R-1 across a variety of benchmarks and against top models: o1, GPT-4o, and Claude 3.5 Sonnet, o1-mini.The benchmarks were broken down into several categories, shown below in the table: English, Code, Math, and Chinese.SetupThe following parameters were applied across all models:Maximum generation length: 32,768 tokens.Sampling configuration:Temperature: 0.6.Top-p value: 0.95.Pass@1 estimation: Generated 64 responses per query.\u00e2\u0080\u008d\u00e2\u0080\u008dDeepSeek R1 outperformed o1, Claude 3.5 Sonnet and other models in the majority of reasoning benchmarkso1 was the best-performing model in four out of\n\nSource: https://www.prompthub.us/blog/deepseek-r-1-model-overview-and-how-it-ranks-against-openais-o1\nTitle: DeepSeek R-1 Model Overview and How it Ranks Against OpenAI's o1\nContent: refine its reasoning capabilities furtherHuman Preference Alignment:A secondary RL stage improved the model\u00e2\u0080\u0099s helpfulness and harmlessness, ensuring better alignment with user needsDistillation to Smaller Models:DeepSeek-R1\u00e2\u0080\u0099s reasoning capabilities were distilled into smaller, efficient models like Qwen and Llama-3.1-8B, and Llama-3.3-70B-Instruct\u00e2\u0080\u008dDeepSeek R-1 performanceThe researchers tested DeepSeek R-1 across a variety of benchmarks and against top models: o1, GPT-4o, and Claude 3.5 Sonnet, o1-mini.The benchmarks were broken down into several categories, shown below in the table: English, Code, Math, and Chinese.SetupThe following parameters were applied across all models:Maximum generation length: 32,768 tokens.Sampling configuration:Temperature: 0.6.Top-p value: 0.95.Pass@1 estimation: Generated 64 responses per query.\u00e2\u0080\u008d\u00e2\u0080\u008dDeepSeek R1 outperformed o1, Claude 3.5 Sonnet and other models in the majority of reasoning benchmarkso1 was the best-performing model in four out of\n\nSource: https://www.prompthub.us/blog/deepseek-r-1-model-overview-and-how-it-ranks-against-openais-o1\nTitle: DeepSeek R-1 Model Overview and How it Ranks Against OpenAI's o1\nContent: refine its reasoning capabilities furtherHuman Preference Alignment:A secondary RL stage improved the model\u00e2\u0080\u0099s helpfulness and harmlessness, ensuring better alignment with user needsDistillation to Smaller Models:DeepSeek-R1\u00e2\u0080\u0099s reasoning capabilities were distilled into smaller, efficient models like Qwen and Llama-3.1-8B, and Llama-3.3-70B-Instruct\u00e2\u0080\u008dDeepSeek R-1 performanceThe researchers tested DeepSeek R-1 across a variety of benchmarks and against top models: o1, GPT-4o, and Claude 3.5 Sonnet, o1-mini.The benchmarks were broken down into several categories, shown below in the table: English, Code, Math, and Chinese.SetupThe following parameters were applied across all models:Maximum generation length: 32,768 tokens.Sampling configuration:Temperature: 0.6.Top-p value: 0.95.Pass@1 estimation: Generated 64 responses per query.\u00e2\u0080\u008d\u00e2\u0080\u008dDeepSeek R1 outperformed o1, Claude 3.5 Sonnet and other models in the majority of reasoning benchmarkso1 was the best-performing model in four out of\n", "metadata": null } }, { "timestamp": "2025-01-28T16:24:42.898542", "type": "event", "data": { "type": "logs", "content": "subquery_context_window", "output": "\ud83d\udcc3 Source: https://www.geeksforgeeks.org/deepseek-r1-rl-models-whats-new/\nTitle: DeepSeek Unveils DeepSeek-R1 RL Models: What\u2019s New and How It is better than OpenAI and Google - GeeksforGeeks\nContent: In this article we have gathered all the insights like what\u2019s new in DeepSeek-R1, how to use it, and a comparison with its top competitors in the industry.\nIn this article we have gathered all the insights like what\u2019s new in DeepSeek-R1, how to use it, and a comparison with its top competitors in the industry.\nWhat is DeepSeek R1?\nDeepSeek-R1 is a groundbreaking family of reinforcement learning (RL)-driven AI models developed by Chinese AI firm DeepSeek. Designed to rival industry leaders like OpenAI and Google, it combines advanced reasoning capabilities with open-source accessibility. Unlike traditional models that rely on supervised fine-tuning (SFT), DeepSeek-R1 leverages pure RL training and hybrid methodologies to achieve state-of-the-art performance in STEM tasks, coding, and complex problem-solving.\nDeepSeek-R1 is a groundbreaking family of\n\nSource: https://www.geeksforgeeks.org/deepseek-r1-rl-models-whats-new/\nTitle: DeepSeek Unveils DeepSeek-R1 RL Models: What\u2019s New and How It is better than OpenAI and Google - GeeksforGeeks\nContent: is DeepSeek R1?DeepSeek-R1 is a groundbreaking family of reinforcement learning (RL)-driven AI models developed by Chinese AI firm DeepSeek. Designed to rival industry leaders like OpenAI and Google, it combines advanced reasoning capabilities with open-source accessibility. Unlike traditional models that rely on supervised fine-tuning (SFT), DeepSeek-R1 leverages pure RL training and hybrid methodologies to achieve state-of-the-art performance in STEM tasks, coding, and complex problem-solving.The model is designed to excel in dynamic, complex environments where traditional AI systems often struggle. Its ability to learn and adapt in real-time makes it ideal for applications such as autonomous driving, personalized healthcare, and even strategic decision-making in business.Types of DeepSeek-R1 ModelsThe R1 series includes three primary variants:DeepSeek-R1-Zero: The foundational model trained exclusively via RL (no human-annotated data), excelling in raw reasoning but limited by\n\nSource: https://www.geeksforgeeks.org/deepseek-r1-rl-models-whats-new/\nTitle: DeepSeek Unveils DeepSeek-R1 RL Models: What\u2019s New and How It is better than OpenAI and Google - GeeksforGeeks\nContent: is DeepSeek R1?DeepSeek-R1 is a groundbreaking family of reinforcement learning (RL)-driven AI models developed by Chinese AI firm DeepSeek. Designed to rival industry leaders like OpenAI and Google, it combines advanced reasoning capabilities with open-source accessibility. Unlike traditional models that rely on supervised fine-tuning (SFT), DeepSeek-R1 leverages pure RL training and hybrid methodologies to achieve state-of-the-art performance in STEM tasks, coding, and complex problem-solving.The model is designed to excel in dynamic, complex environments where traditional AI systems often struggle. Its ability to learn and adapt in real-time makes it ideal for applications such as autonomous driving, personalized healthcare, and even strategic decision-making in business.Types of DeepSeek-R1 ModelsThe R1 series includes three primary variants:DeepSeek-R1-Zero: The foundational model trained exclusively via RL (no human-annotated data), excelling in raw reasoning but limited by\n\nSource: https://www.geeksforgeeks.org/deepseek-r1-rl-models-whats-new/\nTitle: DeepSeek Unveils DeepSeek-R1 RL Models: What\u2019s New and How It is better than OpenAI and Google - GeeksforGeeks\nContent: is DeepSeek R1?DeepSeek-R1 is a groundbreaking family of reinforcement learning (RL)-driven AI models developed by Chinese AI firm DeepSeek. Designed to rival industry leaders like OpenAI and Google, it combines advanced reasoning capabilities with open-source accessibility. Unlike traditional models that rely on supervised fine-tuning (SFT), DeepSeek-R1 leverages pure RL training and hybrid methodologies to achieve state-of-the-art performance in STEM tasks, coding, and complex problem-solving.The model is designed to excel in dynamic, complex environments where traditional AI systems often struggle. Its ability to learn and adapt in real-time makes it ideal for applications such as autonomous driving, personalized healthcare, and even strategic decision-making in business.Types of DeepSeek-R1 ModelsThe R1 series includes three primary variants:DeepSeek-R1-Zero: The foundational model trained exclusively via RL (no human-annotated data), excelling in raw reasoning but limited by\n\nSource: https://www.geeksforgeeks.org/deepseek-r1-rl-models-whats-new/\nTitle: DeepSeek Unveils DeepSeek-R1 RL Models: What\u2019s New and How It is better than OpenAI and Google - GeeksforGeeks\nContent: DeepSeek-R1 is a groundbreaking family of\ndeveloped by Chinese AI firm DeepSeek. Designed to rival industry leaders like OpenAI and Google, it combines advanced reasoning capabilities with open-source accessibility. Unlike traditional models that rely on supervised fine-tuning (SFT), DeepSeek-R1 leverages pure RL training and hybrid methodologies to achieve state-of-the-art performance in STEM tasks, coding, and complex problem-solving.\nThe model is designed to excel in dynamic, complex environments where traditional AI systems often struggle. Its ability to learn and adapt in real-time makes it ideal for applications such as autonomous driving, personalized healthcare, and even strategic decision-making in business.\n\nSource: https://www.geeksforgeeks.org/deepseek-r1-rl-models-whats-new/\nTitle: DeepSeek Unveils DeepSeek-R1 RL Models: What\u2019s New and How It is better than OpenAI and Google - GeeksforGeeks\nContent: all the insights like what\u2019s new in DeepSeek-R1, how to use it, and a comparison with its top competitors in the industry.What is DeepSeek R1What is DeepSeek R1?DeepSeek-R1 is a groundbreaking family of reinforcement learning (RL)-driven AI models developed by Chinese AI firm DeepSeek. Designed to rival industry leaders like OpenAI and Google, it combines advanced reasoning capabilities with open-source accessibility. Unlike traditional models that rely on supervised fine-tuning (SFT), DeepSeek-R1 leverages pure RL training and hybrid methodologies to achieve state-of-the-art performance in STEM tasks, coding, and complex problem-solving.The model is designed to excel in dynamic, complex environments where traditional AI systems often struggle. Its ability to learn and adapt in real-time makes it ideal for applications such as autonomous driving, personalized healthcare, and even strategic decision-making in business.Types of DeepSeek-R1 ModelsThe R1 series includes three primary\n\nSource: https://www.geeksforgeeks.org/deepseek-r1-rl-models-whats-new/\nTitle: DeepSeek Unveils DeepSeek-R1 RL Models: What\u2019s New and How It is better than OpenAI and Google - GeeksforGeeks\nContent: all the insights like what\u2019s new in DeepSeek-R1, how to use it, and a comparison with its top competitors in the industry.What is DeepSeek R1What is DeepSeek R1?DeepSeek-R1 is a groundbreaking family of reinforcement learning (RL)-driven AI models developed by Chinese AI firm DeepSeek. Designed to rival industry leaders like OpenAI and Google, it combines advanced reasoning capabilities with open-source accessibility. Unlike traditional models that rely on supervised fine-tuning (SFT), DeepSeek-R1 leverages pure RL training and hybrid methodologies to achieve state-of-the-art performance in STEM tasks, coding, and complex problem-solving.The model is designed to excel in dynamic, complex environments where traditional AI systems often struggle. Its ability to learn and adapt in real-time makes it ideal for applications such as autonomous driving, personalized healthcare, and even strategic decision-making in business.Types of DeepSeek-R1 ModelsThe R1 series includes three primary\n\nSource: https://www.geeksforgeeks.org/deepseek-r1-rl-models-whats-new/\nTitle: DeepSeek Unveils DeepSeek-R1 RL Models: What\u2019s New and How It is better than OpenAI and Google - GeeksforGeeks\nContent: this article we have gathered all the insights like what\u2019s new in DeepSeek-R1, how to use it, and a comparison with its top competitors in the industry.What is DeepSeek R1What is DeepSeek R1?DeepSeek-R1 is a groundbreaking family of reinforcement learning (RL)-driven AI models developed by Chinese AI firm DeepSeek. Designed to rival industry leaders like OpenAI and Google, it combines advanced reasoning capabilities with open-source accessibility. Unlike traditional models that rely on supervised fine-tuning (SFT), DeepSeek-R1 leverages pure RL training and hybrid methodologies to achieve state-of-the-art performance in STEM tasks, coding, and complex problem-solving.The model is designed to excel in dynamic, complex environments where traditional AI systems often struggle. Its ability to learn and adapt in real-time makes it ideal for applications such as autonomous driving, personalized healthcare, and even strategic decision-making in business.Types of DeepSeek-R1 ModelsThe R1\n\nSource: https://www.geeksforgeeks.org/deepseek-r1-rl-models-whats-new/\nTitle: DeepSeek Unveils DeepSeek-R1 RL Models: What\u2019s New and How It is better than OpenAI and Google - GeeksforGeeks\nContent: the boundaries of artificial intelligence. Developerd as a solution for complex decision-making and optimization problems, DeepSeek-R1 is already earning attention for its advanced features and potential applications.In this article we have gathered all the insights like what\u2019s new in DeepSeek-R1, how to use it, and a comparison with its top competitors in the industry.What is DeepSeek R1What is DeepSeek R1?DeepSeek-R1 is a groundbreaking family of reinforcement learning (RL)-driven AI models developed by Chinese AI firm DeepSeek. Designed to rival industry leaders like OpenAI and Google, it combines advanced reasoning capabilities with open-source accessibility. Unlike traditional models that rely on supervised fine-tuning (SFT), DeepSeek-R1 leverages pure RL training and hybrid methodologies to achieve state-of-the-art performance in STEM tasks, coding, and complex problem-solving.The model is designed to excel in dynamic, complex environments where traditional AI systems often\n\nSource: https://www.geeksforgeeks.org/deepseek-r1-rl-models-whats-new/\nTitle: DeepSeek Unveils DeepSeek-R1 RL Models: What\u2019s New and How It is better than OpenAI and Google - GeeksforGeeks\nContent: the boundaries of artificial intelligence. Developerd as a solution for complex decision-making and optimization problems, DeepSeek-R1 is already earning attention for its advanced features and potential applications.In this article we have gathered all the insights like what\u2019s new in DeepSeek-R1, how to use it, and a comparison with its top competitors in the industry.What is DeepSeek R1What is DeepSeek R1?DeepSeek-R1 is a groundbreaking family of reinforcement learning (RL)-driven AI models developed by Chinese AI firm DeepSeek. Designed to rival industry leaders like OpenAI and Google, it combines advanced reasoning capabilities with open-source accessibility. Unlike traditional models that rely on supervised fine-tuning (SFT), DeepSeek-R1 leverages pure RL training and hybrid methodologies to achieve state-of-the-art performance in STEM tasks, coding, and complex problem-solving.The model is designed to excel in dynamic, complex environments where traditional AI systems often\n", "metadata": null } }, { "timestamp": "2025-01-28T16:24:42.916169", "type": "event", "data": { "type": "logs", "content": "research_step_finalized", "output": "Finalized research step.\n\ud83d\udcb8 Total Research Costs: $0.01807272", "metadata": null } }, { "timestamp": "2025-01-28T16:24:42.948978", "type": "event", "data": { "type": "logs", "content": "writing_report", "output": "\u270d\ufe0f Writing report for 'What is DeepSeek R1'...", "metadata": null } }, { "timestamp": "2025-01-28T16:25:31.263950", "type": "event", "data": { "type": "logs", "content": "report_written", "output": "\ud83d\udcdd Report written for 'What is DeepSeek R1'", "metadata": null } } ], "content": { "query": "", "sources": [], "context": [], "report": "", "costs": 0.0, "type": "report", "content": "selected_images", "output": " January 28, 2025, from https://www.vellum.ai/blog/the-training-of-deepseek-r1-and-ways-to-use-it", "metadata": [ "https://media.cnn.com/api/v1/images/stellar/prod/gettyimages-2196223475.jpg?c=16x9&q=w_1280,c_fill", "https://media.cnn.com/api/v1/images/stellar/prod/jon-stewart-01-27.jpg?c=16x9&q=h_144,w_256,c_fill", "https://media.cnn.com/api/v1/images/stellar/videothumbnails/32276036-68347919-generated-thumbnail.jpg?c=16x9&q=h_144,w_256,c_fill", "https://media.cnn.com/api/v1/images/stellar/prod/still-21319316-26194-119-still.jpg?c=16x9&q=h_144,w_256,c_fill", "https://www.geeky-gadgets.com/wp-content/uploads/2024/11/deepseek-r1-vs-openai-o1-comparison.webp", "https://unfoldai.com/storage/2025/01/lm-studio-deepseek-r1.jpg", "https://unfoldai.com/storage/2025/01/DeepSeek-R1-performance.jpg", "https://unfoldai.com/storage/2025/01/distill-models-deepseek-r1-performance.jpg", "https://aipapersacademy.com/wp-content/uploads/2025/01/image-6.png", "https://techcrunch.com/wp-content/uploads/2024/04/GettyImages-1652364481.jpg?w=1024" ] } }