#DeepSeek

Moonshot AI’s first update, the new Kimi K3.1 Pro > Kimi K3.1 Pro scores 65.7% on Terminal-Bench 4.0, 0.7 points behind Claude Opus 5.5 and 7.5 ahead of GPT-6 Astra, at a lower average cost per task than either rival’s top runs. > Moonshot saw exploit skill climb as post-training scaled, so it removed cyber-related data from mid-training and RL; on its internal xdev-bench-hard, pass@3 fell from 90.5% to 81.0%. > The filtered model still solves 81.0% of those exploitation tasks against K3’s 33.3%, which Moonshot reads as general coding gains carrying into cyber work, while a third-party run of GPT-5.6 Sol solved 100%. > On Moonshot’s internal hallucination stress test with tools, false claims fell from 23.4% for K3 to 8.3%, still above GPT-6 Astra’s 5.5% but well below Claude Opus 5.5’s 15.0%. > The biggest jump is on Moonshot’s simulated business-operation test, where scores rose from 8.9 to 37, and K3.1 Pro also tops OfficeQA Pro v2 at 64.4 against Opus 5.5’s 62.2. > Gains are uneven: K3.1 Pro scores 38.1 on Terminal-Bench Science against GPT-6 Astra’s 68.1 and Opus 5.5’s 63.3, while leading Agents’ Last Exam at 43.2 and Humanity’s Last Exam with tools at 70.0. > K3.1 Pro and a Highspeed variant running 2-3x faster are live in Kimi’s apps and API, with full weights due by November 4 for independent reruns.

MiniMax M3.1 Flash is now #1 on our search capabilities leaderboard for efficiency. It delivers web research performance with Parallel Search similar to DeepSeek V4.1 Flash (1017) at ~3x lower cost.

激动人心的消息：@deepseek_ai 的 DeepSeek-V4.1-Flash-20261017 (Max) 现已成为 Code Arena: WebDev 中排名第一的开源模型，以低 10 倍的成本超越 Kimi K3 (Max)！ 亮点： - 排名第一的开源模型，领先于 Kimi K3 (Max) - 以 1670 分位列总榜第 12，领先于 Claude Opus 5 (High)，与 GPT-6 Sol (Max) 相差约 10 分以内 - 以每百万输入/输出 token $0.3/$1.2 的价格推动帕累托前沿移动 - 相比上一版 DeepSeek-V4.1-Flash-20260910 提升 52 分 祝贺 @deepseek_ai 团队为开源生态又做出了一项强有力的贡献！

🚀 DeepSeek-V4.1-Flash-1017 来了！ 与上一版本相比，基准测试表现更强、任务完成更快、输出更短——在性能与成本上实现了帕累托改进。现已在我们的网站、应用和 API 上线。 🛠️ 针对 @DeepSeekHarness、Tencent CodeBuddy、WorkBuddy 等进行了优化。升级到 DeepSeek Harness v0.2.1-rc.1 或更高版本，以获得新模型的最佳体验。 1/4

🚀 DeepSeek-V4.1-Flash-1017 来了！ 与上一版本相比，基准测试表现更强、任务完成更快、输出更短——在性能与成本上实现了帕累托改进。现已在我们的网站、应用和 API 上线。 🛠️ 针对 @DeepSeekHarness、Tencent CodeBuddy、WorkBuddy 等进行了优化。升级到 DeepSeek Harness v0.2.1-rc.1 或更高版本，以获得新模型的最佳体验。 1/4

DeepSeek V4.1 Flash from @deepseek_ai on ARC-AGI (Verified): - ARC-AGI-2: 72.9%, $0.13/task - ARC-AGI-1: 94.5%, $0.07/task DeepSeek V4.1 Flash beats V4 Flash's best scores by 5.5 points on ARC-AGI-1 and 11.5 on ARC-AGI-2, but costs about 250% more per task.

StepFun's Step 5 Preview scores 44 on the Artificial Analysis Intelligence Index, matching Kimi K3 (max) at ~2.8x lower cost per task, but trails peers on agentic evaluations Step 5 Preview is @StepFun_ai's new flagship model, with 600B total and 27B active parameters, succeeding Step 3.7 Flash (released May 2026). It scores 44 on the Intelligence Index, level with Kimi K3 (max) and just behind GLM-5.3 (max, 45) and Qwen3.8 Max (45) Key takeaways: ➤ Step 5 Preview costs ~2.8x less per Intelligence Index task than models at the same score. It costs ~$0.72 per task, against ~$2.00 for Kimi K3 (max) at the same score of 44 and ~$2.01 for GLM-5.3 (max) at 45. This is driven by pricing: at $1/$2.70 per 1M input/output tokens, it is priced below both on input and output. MiMo-V2.6-Pro is the one model that scores higher (46) at a lower cost per task ($0.13) ➤ Frontier reasoning is the standout strength, and where the jump from Step 3.7 Flash is largest. Step 5 Preview scores 46% on Humanity's Last Exam, in line with Kimi K3 (max, 47%), and 21% on CritPt, between Kimi K3 (23%) and GLM-5.3 (max, 19%). Both are up sharply from Step 3.7 Flash: +25 points on HLE and +19 points on CritPt ➤ Higher AA-Omniscience accuracy than GLM-5.3 at fewer parameters, but with more hallucination. At 600B total parameters, Step 5 Preview reaches 42% accuracy on AA-Omniscience, our benchmark measuring factual recall and hallucination, ahead of GLM-5.3 (max, 34%, 753B) and behind Kimi K3 (max, 48%, 2.8T). It attempts more questions than GLM-5.3 (68% vs 55%) and hallucinates more often when it does (43% vs 30%), landing at 16 on the AA-Omniscience Index, between GLM-5.3 (14) and Kimi K3 (20) ➤ Agentic evaluations are where Step 5 Preview lags peers at a similar Intelligence Index score. It scores 1,566 Elo on GDPval-AA, our primary evaluation for agentic performance, behind Qwen3.8 Max (1,668) and GLM-5.3 (max, 1,646). The gap holds on Terminal-Bench 4.0 (33% vs 39% and 42%), AA-Briefcase (1,432 Elo vs 1,640 and 1,525) and AutomationBench-AA (51% vs 56% and 62%) Key model details: ➤ Model Size: 600B total parameters, 27B active MoE model ➤ Context window: 1M tokens ➤ Multimodality: Text, image and video input, text output ➤ Pricing: $1/$2.70 per 1M input/output tokens, with cached input at $0.05/M ➤ Availability: StepFun first-party API, with open weights release planned for October 15th ➤ Licensing: Closed weights currently, with weights release planned for October 15th