DeepSeek releases DeepSeek-V4.1-Flash-1017

Phoenix TechnologySelected

Report: DeepSeek completes about 100 billion yuan financing, with Tencent and CATL participating

According to Bloomberg, DeepSeek raised about 100 billion yuan (about 15 billion US dollars) in a new round of financing; the financing target was raised twice from an initial 50 billion yuan to 80 billion yuan and finally to 100 billion yuan, with CATL and Tencent among the largest investors. The report said this round of financing paves the way for its future listing; DeepSeek is preparing for an IPO and increasing investment in AI data centers and open-source models. As of publication, DeepSeek, Tencent and CATL had not responded to requests for comment.

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The New StackSelected

DeepSeek open-sources DeepInfer, the inference engine behind its API, with support for both Nvidia and Huawei Ascend

DeepSeek has open-sourced DeepInfer, its in-house inference framework used to serve its models in production. Released under the Apache 2.0 license, it lets developers run DeepSeek-V4.1-Flash on their own infrastructure, with support for subsequent models planned. The framework includes optimized compute kernels and communication libraries such as FlashMLA, DeepGEMM, and DeepEP, supports speculative decoding, asynchronous scheduling, and structured output, and also open-sources the deepseek-recipe library (available in Rust and Python versions) for handling request and response transformations. On the hardware side, it supports Nvidia SM100 (Blackwell) architecture and the Huawei Ascend 950 NPU series from day one; older architectures such as Hopper are not listed. Batch-invariant inference and deterministic sampling are available only on Nvidia hardware, and the published performance data is also based on Nvidia GPUs. The current version supports offline inference but lacks complete production serving components such as KV cache management, cross-machine inference stage separation, and replica load balancing.

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DeepSeek-V4.1-Flash-20261017 Tops the Agent Arena Open-Model Leaderboard

Arena.ai says DeepSeek-V4.1-Flash-20261017 (Max) has become the top-ranked open-source model in Agent Arena, ranking 11th overall, with a net improvement of 5.1% across more than 7.5K real sessions. The model's median cost per task is $0.20, roughly 37% of GPT-6.1 Sol's improvement margin and 27% of its cost; it ranks first among open-source models on all four signals—Confirmed Success, Praise vs. Complaint, Bash Reliability, and Steerability—and shows no tool-hallucination issues.

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Unsloth Enables Local Running of DeepSeek V4.1-Flash-1017

Unsloth announced that DeepSeek V4.1-Flash-1017 now supports local running; its 1-bit quantized version retains about 88.2% accuracy, with size reduced from 508GB to 250GB, a 52% decrease. This version requires a device with more than 128GB of memory, and Unsloth also provided a usage guide and GGUF files. DeepSeek previously stated that V4.1-Flash-1017 improved over the previous version in benchmark performance, task completion speed, and output length.

The New Stack

DeepSeek Opens Up Search and Fetch Search APIs, Charging $0.75 per Thousand Requests

DeepSeek is opening its in-house search engine to external developers through two new APIs, Search and Fetch, which can retrieve real-time web search results and webpage body text. It charges $0.75 per 1,000 successful requests, requires no additional model calls, and can be used in applications powered by other vendors' models. Search returns ranked results with titles, URLs, snippets, and summaries, while Fetch returns the content of a single webpage in Markdown format; the company says the engine handles hundreds of millions of requests per day and its index contains hundreds of billions of webpages. The release coincides with a minor version update, DeepSeek-V4.1-Flash-1017. The API documentation shows that each account is limited to 5 Search and 5 Fetch requests per second, a single search returns at most 50 results, and only requests that return results are billed.

ZhidongxiSelected

DeepSeek-V4.1-Flash-1017 Tops Code Arena WebDev Open-Source Leaderboard

Arena announced that DeepSeek-V4.1-Flash-1017 (Max) surpassed open-source models such as Kimi K3 (Max) with a score of 1670, becoming the highest-ranked open-source model on the Code Arena: WebDev leaderboard, and also surpassed closed-source models such as Claude Opus 5 and Grok 4.7, trailing GPT-6 Sol by only 12 points. The model was launched on October 17, priced at 2 yuan per million tokens for input (cache miss) and 8 yuan for output during peak hours, and 1 yuan for input and 4 yuan for output during idle hours, with a comprehensive cost of about 1/10 that of Kimi K3. Zhidongxi used it to create two cases, an ancient Kaifeng 3D scene and a web-based racing game, both completed in a single attempt, at a total cost of about 6 yuan.

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ArenaSelected

DeepSeek-V4.1-Flash-20261017 Tops the Code Arena WebDev Open-Source Model Leaderboard

Arena says DeepSeek-V4.1-Flash-20261017 (Max) has become the top-ranked open-source model on the Code Arena WebDev leaderboard, surpassing Kimi K3 (Max) at 10 times lower cost. The model ranks 12th overall with 1,670 points, above Claude Opus 5 (High) and about 10 points behind GPT-6 Sol (Max); it is priced at $0.3/$1.2 per million input/output tokens, a 52-point improvement over the previous version DeepSeek-V4.1-Flash-20260910.

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Nathan LambertSelected

DeepSeek-V4.1-Flash-1017 released, with simultaneous improvements in benchmark performance and cost

DeepSeek has released DeepSeek-V4.1-Flash-1017, claiming stronger benchmark performance, faster task completion, and shorter outputs, achieving a Pareto improvement in performance and cost; it is now live on the official website, App, and API. This version has been optimized for DeepSeek Harness, Tencent CodeBuddy, WorkBuddy, and others, and requires upgrading to DeepSeek Harness v0.2.1-rc.1 or later for the best experience. Nathan Lambert thinks its naming approach (a minor version number plus a date tag) is quite absurd, but the benchmark gains are large, and with built-in vision capability it may be stronger than GLM 5.3.

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ZhidongxiSelected

DeepSeek V4.1-Flash updated to V4.1-Flash-1017 and opens search and fetch APIs

DeepSeek V4.1-Flash has been updated to version V4.1-Flash-1017; the official web, App and API have been updated in sync, and the model weights are available for download on HuggingFace and ModelScope. In the DeepSWE v1.1 test, the new model's max tier achieved a single-task pass rate of 77.0%, 2.8 percentage points higher than the previous version, and the average cost per task dropped from about 3.3 yuan to about 1.3 yuan. DeepSeek also opened to the public the Search and Fetch APIs of its self-developed search engine; both interfaces are priced at 5 yuan per thousand calls, and this part of access via server-side tool calls will be charged per call starting from 00:00 Beijing time on November 1.

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DeepSeek Releases Open-Weight Model DeepSeek-V4.1-Flash-1017

DeepSeek releases the open-weight model DeepSeek-V4.1-Flash-1017, scoring 77% on the DeepSWE 1.1 benchmark, surpassing models such as GLM-5.3, Kimi K3, GPT-5.6 Sol, and Opus 5. The model is now available on DeepSeek Chat, the API, and HuggingFace, and the publisher says its performance and cost both improved over the previous version.

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DeepSeek V4.1 Flash 1017 Launches on OpenRouter

DeepSeek V4.1 Flash 1017 is now live on OpenRouter, with DeepSeek as the first provider and more providers to follow. OpenRouter says this update to V4.1 Flash is more token-efficient and shows notable improvements on agent benchmarks.

Tianyi CuiSelected

DeepSeek-V4.1-Flash-1017 Released, Requires Upgrading to DeepSeek Harness 0.2.1-rc.1

DeepSeek has released DeepSeek-V4.1-Flash-1017, stating that it delivers stronger benchmark performance, faster task completion, and shorter outputs than the previous version, representing a Pareto improvement in performance and cost, and it is now live on the official website, App, and API. The model has been optimized for DeepSeek Harness, Tencent CodeBuddy, WorkBuddy, and others; using DeepSeek Harness requires upgrading to v0.2.1-rc.1 or a later version.

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