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New DeepSeek V4.1 Flash 1017 tested for finance agents: independent benchmarks, real agent costs, and the price gap with Claude Sonnet 5.5 and GPT-6.1 ($1.20 against $10 per million output tokens). Full article, every number, chart and source: https://davidariasfinance.com/research/deepseek-v4-1-flash-deep-analysis/ What is inside: 1. What changed in V4.1 Flash 1017, and why it is so cheap: a mixture of experts model with open weights under the MIT license 2. What Hacker News and OpenRouter users are saying about it 3. What a benchmark is, and what a harness is 4. Vals Index: 14th of 50 at 4% of the leader's cost, 88 points per dollar against 19 for GPT-6.1 and 3 for Claude Sonnet 5.5 5. DeepSWE coding: 77% in DeepSeek's own run for about $0.20 a task, and why independent tests come in lower 6. DeepSeek Harness, the open source agent that runs on your own machine 7. Real finance agent costs: a 12 agent TradingAgents committee for about 6 cents a run (78 saved runs for $3.49), and the AI Hedge Fund for about 1 cent a session 8. Finance benchmarks: Vals Finance Agent 17th of 80, ahead of GPT-6.1 and 1.1 points behind Claude Sonnet 5.5 at a fourteenth of the cost; Tax Agent Bench 5th of 71 9. MiniMax M3.1 Flash, released on 19 October at half the price per token: the scores both companies report, and weights coming later 10. DeepSeek Search API: $0.75 per 1,000 searches, free through DeepSeek Harness, Claude Code or Codex until 1 November, and point in time news for backtests 11. The AI race: all five of OpenRouter's most used models come from Chinese labs, and open weights now sit a few points from the frontier Honest limits: the 77% on DeepSWE is DeepSeek's own run, and independent coding tests with a standard harness score it lower. On the hardest work it still trails the top closed models. MiniMax's numbers are mostly company reported so far. The finding: a model close to the frontier at a small fraction of the price. Links DeepSeek release note: https://api-docs.deepseek.com/news/news261017 Model weights, MIT license: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017 DeepSeek Harness: https://github.com/deepseek-ai/deepseek-harness DeepSeek Search API: https://api-docs.deepseek.com/guides/search/ MiniMax M3.1 Flash: https://www.minimax.io/blog/minimax-m31f Vals AI benchmarks: https://www.vals.ai/ LiveBench: https://livebench.ai/ Artificial Analysis: https://artificialanalysis.ai/ OpenRouter rankings: https://openrouter.ai/rankings Hacker News launch thread: https://news.ycombinator.com/item?id=50129045 TradingAgents on DeepSeek, the full video: https://youtu.be/Bvucb9BpJ1U AI Hedge Fund on DeepSeek, the full video: https://youtu.be/Ap4NZYbihZU TradingAgents repository: https://github.com/TauricResearch/TradingAgents AI Hedge Fund repository: https://github.com/virattt/ai-hedge-fund Glassbench, the workbench used for the agent runs: https://github.com/davidalmeida90/glassbench Chapters 0:00 New DeepSeek V4.1 Flash: the benchmarks and the price 0:56 In this video 1:22 What changed in V4.1 Flash 1017 1:34 Why so cheap: mixture of experts and open weights 2:00 What people are saying: Hacker News and OpenRouter 3:04 What a benchmark and a harness are 3:44 Vals Index: accuracy per dollar 4:05 DeepSWE coding 4:28 DeepSeek Harness 4:55 What finance agents cost on DeepSeek 5:17 Finance benchmarks: Finance Agent and Tax Agent 5:45 MiniMax M3.1 Flash answers 6:14 DeepSeek Search API 6:37 News for backtests, without look ahead 7:08 The AI race: Chinese open models 8:05 Does the hype hold up? Not investment advice. Educational content only. #DeepSeek #AIAgents #FinanceAI #MiniMax #OpenSource #LLM #Claude

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