DeepSeek-V4.1-Flash-1017
DeepSeek-V4.1-Flash-1017 is an update to DeepSeek-V4.1-Flash, released on October 17, 2026, that DeepSeek says scores higher on its benchmarks, finishes tasks faster and writes shorter outputs. It takes text and image input and is served on the DeepSeek API as deepseek-flash, with open weights under the MIT License.
Released
October 17, 2026
Type
multimodal
License
open-weight
MIT
Context
1,000,000 tokens
Pricing
Input
$0.30 / 1M tokens
Output
$1.20 / 1M tokens
List price on the developer's own pages, checked . Peak-hour price for cache-miss input. Off-peak is half ($0.15 input / $0.60 output). Cache-hit input $0.006 at peak, $0.003 off-peak. Peak hours are 01:00-04:00 and 06:00-10:00 UTC, Monday to Friday, excluding Chinese public holidays. Unchanged from V4.1-Flash; the older IDs deepseek-v4-flash and deepseek-v4-flash-vision-exp are billed at the Flash price.
Capabilities
Sources
Every figure on this page was checked against these pages on . A figure we could not source is left out.
- DeepSeek API Docs: DeepSeek-V4.1-Flash-1017 release (2026/10/17) (opens in a new tab)
- DeepSeek API Docs: Change Log (opens in a new tab)
- DeepSeek API Docs: Models & Pricing (opens in a new tab)
- Hugging Face: deepseek-ai/DeepSeek-V4.1-Flash-1017 model card (opens in a new tab)
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Links
In the news: DeepSeek-V4.1-Flash-1017 and DeepSeek
MarkTechPost
DeepSeek Just Released V4.1-Flash-1017, Scores 77.0 on DeepSWE, Opens Search API
DeepSeek has released DeepSeek-V4.1-Flash-1017, a new checkpoint of its V4.1-Flash model, available on its website, app, and API. The company states this version offers stronger scores, faster task completion, and shorter outputs compared to the September release, describing it as a Pareto improvement in performance and cost. Alongside the model update, DeepSeek has opened its Search and Fetch APIs to all developers. This provides agent builders with access to a more cost-effective coding model and a first-party web layer accessible through a single API key. DeepSeek-V4.1-Flash-1017 features a 552 billion backbone parameter count and 196 billion Engram parameters, activating 8 billion parameters per token during prefill and 16 billion during decode. It supports a context window of 1 million tokens with up to 384,000 output tokens. The model, served as deepseek-flash on the DeepSeek API, reportedly beats the previous V4.1-Flash on all 11 reported benchmarks, with improvements ranging from 2.0 to 11.1 points. Its best score is 77.0 on DeepSWE v1.1, surpassing Claude Opus 5's score of 74.0. DeepSeek-V4.1-Flash-1017 is an updated post-training checkpoint of DeepSeek-V4.1-Flash, utilizing reinforcement learning with self-feedback for end-to-end project work, including tasks like game creation and 3D modeling. It is tuned for DeepSeek Harness, Tencent CodeBuddy, and WorkBuddy.
MarkTechPost
openKylin Outlines Its Vision for Reimagining Linux in the AI Era at Open Source Summit Europe 2026
At Open Source Summit Europe 2026, openKylin introduced its vision for reimagining Linux in the AI era. Min Liu, Executive Director of openKylin, presented KART, a unified AI runtime, and a system-level agent execution environment. Jianfeng Li discussed building an AI-ready Linux desktop on RISC-V. openKylin, an open-source operating system community originating in China, aims to develop an AI-integrated OS for global users. The project showcased its latest work on AI and operating system integration through presentations and developer exchanges. The community believes the key challenge for AI innovation is no longer just model capability, but the system infrastructure connecting AI with everyday computing. Current AI stacks are fragmented, and hardware-software integration remains complex. To address this, openKylin proposes a shift in operating system management from "resource management" to "intent management." Traditional operating systems manage CPUs and memory, while AI-era operating systems need to manage AI functionalities more directly.
MarkTechPost
Reflection AI Introduces Beam: A 501B Open-Weight MoE Model With 23B Active Parameters for Coding and Agentic Workloads
Reflection AI has introduced Beam, its inaugural open-weight model, on March 18, 2024. Beam is a sparse Mixture-of-Experts (MoE) architecture boasting a total of 501 billion parameters, with 23 billion active parameters per token. This model is specifically engineered for coding, reasoning, and agentic workloads. According to the Reflection AI team, Beam is positioned to directly compete with larger open models such as GLM 5.2, while demonstrating a significant advantage in inference compute efficiency, using 3 to 4 times less compute on reasoning benchmarks. Currently, Beam is not available for self-hosting but is undergoing final red-teaming. Early access is being managed through a waitlist on the Reflection platform. Beam functions as a general agent model, developed from the ground up by Reflection AI, with a primary focus on enterprise-level coding and agentic applications. Reflection AI frames Beam as a significant advancement in the Western open-weight model landscape. The research team acknowledges that while Kimi K3 may still lead in raw capability, Beam's key selling proposition is its efficiency during inference. Users can adjust a "reasoning effort" parameter, where lower settings yield concise answers and higher settings enable more extensive reasoning for complex tasks. This allows teams to tailor the model's effort to the specific difficulty of a task and their available compute budget. The pretraining of Beam involved an extensive dataset of 23.8 trillion tokens, sourced from the web, public repositories, and proprietary licensed datasets. The Reflection AI team reported that their curation process removed approximately 95% of raw internet tokens, while retaining about 1.8 trillion high-quality tokens that might have been discarded by conventional filtering methods. The model's architecture integrates local and global attention mechanisms with finely routed experts. Its load balancing strategy builds upon DeepSeek-V3's auxiliary-loss-free balancing, incorporating a cosine decay for expert-bias updates. This approach ensured that the most utilized expert experienced a load of only 1.04 times the average by the conclusion of pretraining. Throughout the 52 layers, residual norms were maintained within bounds through depth-based scaling, SandwichNorm, attention gating, and FP32 residual accumulation. The entire pretraining process was completed in under four weeks, utilizing 6,144 NVIDIA GB300 NVL72 GPUs. The reported goodput reached 92.3% near the end of the training period, with nine semi-automatic rewinds implemented. During mid-training, the effective context window was extended to 1 million tokens.
MarkTechPost
DeepSeek Harness v0.2 Brings Official Desktop Apps to Its Open-Source Agent Harness
DeepSeek has released an official desktop application for its open-source agent harness, DeepSeek Harness (dsh), with the v0.2 preview. Installers are available for macOS, specifically for Apple silicon, and for Windows 64-bit operating systems. This preview version is deployable immediately and can be downloaded from deepseek.com/harness or accessed via the command line using 'npx @deepseek-ai/dsh web'. DeepSeek has cautioned that compatibility-breaking changes are expected in future releases. The v0.2 preview enhances the harness's capabilities to target both everyday work and coding tasks. Key built-in features include preloaded, commonly used tools for office productivity and software development. A new plugin management interface allows users to install, configure, enable, and disable plugins through a dedicated page. For code and file review, a right-hand sidebar provides previews of generated files and code changes. The harness now supports document work, enabling users to input documents, spreadsheets, or PDFs and request the generation of charts or slides. An automation task plugin has been introduced, which schedules recurring prompts and includes a run history with editable frequency settings. The desktop build also integrates the dsh command-line interface, eliminating the need for separate Node.js or pnpm installations. Users can authenticate using a DeepSeek account or by providing an API key. According to the release candidate 1 (rc.1) notes, account-based models gain access to web search functionality without requiring an additional API key. The architecture of DeepSeek Harness is designed around a plugin-centric approach, where the model adapter, tool registry, and agent loop are all implemented as plugins, allowing for modularity and replacement of any layer. DeepSeek first open-sourced dsh in August 2026 under the MIT license. The harness runs on Cordis, as detailed in the research paper ‘A Programming Paradigm for Spatiotemporal Composability’. The harness is not restricted to DeepSeek's own models, with a provider guide available for integrating third-party providers and custom OpenAI-compatible endpoints. The project's GitHub repository has garnered significant attention, exceeding 240,000 stars and 24,000 forks. DeepSeek Harness also serves as DeepSeek's internal evaluation framework. The API changelog indicates that Code Agent benchmarks, such as the Terminal Bench 2.1, have been run using DeepSeek Harness in a minimal configuration. For instance, the DeepSeek-V4-Flash-0731 model achieved a score of 82.7 on Terminal Bench 2.1 within this setup. The harness supports four distinct session modes.
The Hacker News
Anthropic Says Seven China-Based AI Labs Ran Industrial-Scale Claude Distillation Attacks
Anthropic announced on Thursday that it has identified and disrupted industrial-scale illicit distillation attacks targeting its Claude AI model. These sophisticated attacks were traced back to seven distinct artificial intelligence laboratories based in China. The identified entities include prominent organizations such as Alibaba, Moonshot, DeepSeek, Z.ai (also known as Zhipu), and MiniMax. The attacks involved knowledge distillation, a legitimate machine learning technique where a larger, more capable AI model acts as a 'teacher' to train a smaller model. However, in this context, the labs were allegedly using Anthropic's powerful Claude models to illicitly train their own AI systems without authorization, bypassing Anthropic's terms of service and potentially infringing on intellectual property rights. Anthropic's security team detected these activities by monitoring for unusual patterns of access and data exfiltration that were indicative of large-scale, systematic attempts to replicate Claude's capabilities. The company stated that these distillation efforts were "industrial-scale," suggesting a significant investment of resources and computational power by the implicated labs. Such attacks aim to extract proprietary knowledge and model parameters from a teacher model to create a student model that mimics its performance, often at a fraction of the training cost and time. By engaging in this unauthorized distillation, the Chinese labs sought to gain a competitive advantage by rapidly developing advanced AI models based on Anthropic's research and development. In response to the discovery, Anthropic took immediate action to mitigate the threat. The company stated that it has taken steps to block the identified actors and prevent further unauthorized access and misuse of its Claude models. This disruption aims to protect Anthropic's intellectual property and maintain the integrity of its AI systems. The company emphasized that knowledge distillation is a valid technique when conducted ethically and with proper licensing, but the actions of these seven labs constituted a violation of their acceptable use policies. The incident highlights the ongoing challenges in securing advanced AI models against sophisticated intellectual property theft and unauthorized replication attempts, particularly in a competitive global AI landscape. Anthropic has not disclosed the specific versions of Claude that were targeted in these attacks, nor the exact methods used by the labs to extract the necessary data for distillation. However, the scale and systematic nature of the attacks suggest a coordinated effort. The company's proactive stance in identifying and reporting these activities underscores the increasing importance of AI security and the need for robust defenses against emerging threats. The involvement of multiple well-known Chinese AI companies also points to the intense competition and rapid development occurring within China's AI sector, where the pursuit of cutting-edge capabilities may sometimes lead to questionable practices. Anthropic's disclosure serves as a warning to other AI developers and a signal of the evolving security landscape in artificial intelligence.