#DeepSeek

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

Mozilla just published a 91-page report and it says open-weight AI is now only about 4 months behind the frontier. - 8 of OpenRouter's 10 most-used models by August token volume were open-weight, and 7 were Chinese-built, while DeepSeek became the first open model to lead the platform in weekly requests. - However, the economics are almost upside down. Open models handled roughly 20% of measured OpenRouter usage but captured only about 4% of model-layer revenue in the cited 2025 window. Mozilla attributes much of that mismatch to pricing, with closed models costing roughly 6x more per call at about 90% capability parity. The comparison shows why high usage does not necessarily translate into high revenue when one class of models is dramatically cheaper. Mozilla also warns that the revenue measurement is older than its 2026 usage data, so the current revenue split may be different. - Open models are spreading faster than they reach production: 79% of surveyed developers use them, but only 51% of open-model deployments reach production versus 63% for closed models. - DeepSeek showed that a new pretraining run may no longer be necessary for a major capability jump, gaining roughly 10 index points and then another 8 through post-training passes. -Even model diversification may provide less protection than assumed: Kimi K3 and Claude Fable 5 had a 0.72 per-task failure correlation, meaning supposed backup models often fail on the same problems.

Exciting news: DeepSeek-V4.1-Flash (Max) by @deepseek_ai just landed in Agent Arena at #3 among open models! With +4.87% net improvement and a median cost per task of $0.07 it reshaped the Pareto frontier. Among the top 3 open models, DeepSeek-V4.1-Flash (Max) has the lowest median cost per task. Its +4.87% net improvement is within 0.09 percentage points of Hy4 preview (ranked #2) at 68% lower cost, and within 1.52 percentage points of Kimi K3 (Max) (ranked #1) at 91% lower cost. - Kimi K3 (Max): +6.39% | $0.77/task - Hy4 preview: +4.96% | $0.22/task - DeepSeek-V4.1-Flash (Max): +4.87% | $0.07/task See the full Pareto Frontier below. DeepSeek-V4.1-Flash (Max) is ranked #12 overall, and by signal landed #4 Confirmed Success with +13.75%! Congrats to the @deepseek_ai team on this release!

🚀 推出 DeepSeek-V4.1-Flash：更智能、更快速、更高效。 🔹 推出我们新架构家族中最小的模型，具备原生视觉理解能力。 🔹 旨在实现更强大的能力、更快的推理、更高的吞吐量，并可扩展至更大的模型。 1/6

⚡ V4.1-Flash 现已在 DeepSeek API 上线，原生支持多模态。 将你的模型设置为 deepseek-flash。 🔹 V4-Flash 和 V4-Flash-Vision-Exp 已退役。为保持兼容，deepseek-v4-flash 和 deepseek-v4-flash-vision-exp 暂时路由至 V4.1-Flash。 🔹 多方测试表明，V4.1-Flash 在性能、成本、速度和总运行时间上均领先于 V4-Pro。我们正在逐步淘汰 V4-Pro。 🔹 自 2026 年 9 月 14 日 04:00 UTC 起，所有 deepseek-v4-pro 请求将按 V4.1-Flash 的费率路由至 V4.1-Flash。此安排将持续至 V4.1-Pro 发布。 🤝 官方合作伙伴 @WorkBuddy_AI（包括 Codebuddy）和 @opencode 现已全面支持 V4.1-Flash。今天就试试吧！ 4/6

🚀 推出 DeepSeek-V4.1-Flash：更智能、更快速、更高效。 🔹 推出我们新架构家族中最小的模型，具备原生视觉理解能力。 🔹 旨在实现更强大的能力、更快的推理、更高的吞吐量，并可扩展至更大的模型。 1/6

🚀 推出 DeepSeek-V4.1-Flash：更智能、更快速、更高效。 🔹 推出我们新架构家族中最小的模型，具备原生视觉理解能力。 🔹 旨在实现更强大的能力、更快的推理、更高的吞吐量，并可扩展至更大的模型。 1/6

🚀 推出 DeepSeek-V4.1-Flash：更智能、更快速、更高效。 🔹 推出我们新架构家族中最小的模型，具备原生视觉理解能力。 🔹 旨在实现更强大的能力、更快的推理、更高的吞吐量，并可扩展至更大的模型。 1/6