DeepSeek-v4.1-Flash
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Headlines · 16
- DeepSeek-Led Qwen Team Matches Solo Results at Higher Cost
In an experiment reported on Reddit, DeepSeek-V4.1-Flash coordinated four Qwen3.5-9B workers to reverse engineer two small C binaries and rewrite their functions in Python. The team completed all 38 and 56 tested functions, but required 86 and 100 minutes and cost $2.97 and $3.60, versus 14 and 54 minutes and $0.87 and $3.28 for DeepSeek working alone. The lead revised worker instructions and added a signature-and-offset lookup tool, while about 40% of functions still went back to it.
r/LocalLLaMA · 🔥 0 - EC²-Bench Evaluates Edge-Cloud Collaboration for Long-Horizon AI Agents
Northeastern University, Tsinghua University and China Mobile researchers released EC²-Bench, an open benchmark for edge-cloud collaboration in long-horizon AI agent tasks. It contains 128 executable digital-workspace tasks across six scenarios, with 100 tasks carrying fine-grained privacy annotations covering 3,385 sensitive information units, and evaluates task performance, resource costs and privacy exposure. The study compares four collaboration strategies; with Qwen3.5-9B, the best strategy raised the completion score from 39.12 under edge-only execution to 60.46 while using cloud resources costing 15% of the cloud-only setup.
DoNews · 🔥 0 - MEMCUT tests whether AI agents truly forget deleted secrets
Researchers from Hong Kong University of Science and Technology (Guangzhou), the University of Science and Technology of China, and Hong Kong University of Science and Technology built MEMCUT to evaluate user-requested forgetting in AI agent memory. Across five memory architectures and four large language models, 85%–99% of secrets remained recoverable after only direct disclosures were deleted, while LLM-assisted cleanup still left 39%–76% derivable from residual evidence. The benchmark distinguishes native recovery through a system’s retriever from residual recovery when all surviving memory records are provided.
36氪 人工智能 · 🔥 0 - llama.cpp PR Proposes DeepSeek-V4.1-Flash Support
llama.cpp Pull Request #31086, titled “model: DSV4.1 flash by am17an,” links to the DeepSeek-V4.1-Flash model page and technical report. The submitter described the model as having 763 billion parameters, but the supplied report does not independently verify that figure.
r/LocalLLaMA · 🔥 0 - SGLang Reports Matching or Beating DeepInfer
DeepSeek open-sourced DeepInfer, the inference engine behind its own service, along with related kernels. In project-run tests using DeepInfer’s benchmarks and the same hardware, SGLang reached up to 1.04x prefill throughput on four GB300 or four B300 GPUs and cut decode step time by as much as 7.4% on 64 GB300 GPUs, according to the SGLang report. A 36氪 report said vLLM also approached or partly exceeded DeepInfer under its disclosed configurations. The comparisons used project-reported results and differing workloads and measurement methods.
LMSYS Blog · 🔥 0 - Kimi K3.1 Pro Ranks Eighth in WebDev Arena
Arena.ai reported that Kimi K3.1 Pro ranked eighth in the Code Arena: WebDev with 1,692 points, nine places and 37 points above Kimi K3. It also said the model matched Claude Opus-5 (Max) at 70% lower cost. In Agent Arena, Kimi K3.1 Pro (Max) placed 11th after a reported 7.6% net improvement across more than 4,000 sessions, with a six-percentage-point gain over Kimi K3 (Max).
arena (X) · 🔥 0 - GPT-6 Luna and GPT-6.1 Sol Report Six Agent Arena Frontier Positions
Arena reported that GPT-6 Luna and GPT-6.1 Sol occupy six of the 11 positions on the Agent Arena Pareto frontier across different reasoning efforts. It said Luna holds three of four frontier positions below $0.10 per task, while GPT-6.1 Sol occupies positions between $0.22 and $0.65 per task. Adding GPT-6 Astra at Max reasoning gives OpenAI seven of the 11 positions. The report was based on a single Arena post.
arena (X) · 🔥 0 - DeepSeek Open-Sources the DeepInfer Production Inference Framework
DeepSeek has open-sourced DeepInfer, the inference framework used for high-throughput, low-latency production serving of DeepSeek-V4.1-Flash. The release includes distributed inference, speculative decoding, SWA Bounded Replay, structured output and the deepseek-recipe component for converting between API protocols and model tokens. DeepSeek said the system supports NVIDIA SM100 Blackwell GPUs and Huawei Ascend 950 NPUs; older NVIDIA architectures were not listed. According to DeepSeek, one cluster processes 155 trillion tokens per day and generates $38 in revenue per GPU-hour at off-peak pricing. The release does not include all of DeepSeek’s online infrastructure, as KVCache management and cross-replica load-balancing code were not fully open because of their coupling with internal systems.
InfoQ 中文 AI&大模型 · 🔥 0 - SGLang adds Low-Latency Mode with plugin kernels for NVIDIA and AMD GPUs
The SGLang, NVIDIA and AMD teams introduced SGLang Low-Latency Mode, which brings specialized low-latency kernels into the SGLang serving stack through a plugin framework, with NVIDIA kernels in sgl-nvidia-mega and AMD kernels in sgl-amd-mono. In AgentX trace replay with DeepSeek-V4.1-Flash with DSpark, the teams report P90 per-user token generation rising from 671.2 to 928.4 tokens/s (1.38x) on four NVIDIA GB300 GPUs at one concurrent session, and from 331.6 to 516.0 tokens/s (1.56x) on four AMD MI355X GPUs at four concurrent sessions, each against the SGLang baseline on the same hardware. The NVIDIA plugin also improved per-user decode speed by 12-38% at 1-8 concurrent requests on an 8K-input / 1K-output workload, and the teams say megakernel work is underway for GLM-5.3, K3 and MiniMax M3.
LMSYS Blog · 🔥 0 - Tencent reportedly tests “Dongleya” AI learning community
IT之家 reported, citing user feedback, that Tencent is beta-testing “懂了鸭,” an AI interactive-content community for children and teenagers. The website and WeChat mini program reportedly offer limited testing to parents with children and frontline teachers. Its built-in AI agents can reportedly turn natural-language prompts into 3D demonstrations, dynamic lessons, interactive picture books, lightweight games and memory cards without requiring programming skills. Users can browse community works and adapt others’ creations. IT之家 also said the model list includes DeepSeek-V4.1-Flash, DeepSeek V4 Pro, DeepSeek V4 Flash, Hy3, Hy4 preview, MiniMax M3, Kimi K3 and GLM-5.3. The supplied reports do not include direct confirmation from Tencent, and they describe the test’s access arrangements differently.
DoNews · 🔥 0 - Xolotl engine runs 552B DeepSeek-V4.1-Flash on a single RTX 4090, developer reports
Reddit user indrasmirror released Xolotl, a Strata-based inference engine, claiming it runs the full 552B DeepSeek-V4.1-Flash on one gaming PC: a single RTX 4090 (24GB), a Ryzen 7 5700X, 90GB of DDR4 and an NVMe drive, with weights spread across VRAM, pinned RAM and NVMe and only the experts each token needs pulled in. The developer self-reports about 27 tok/s decode, 180–320 tok/s prefill, 512K context, and 21/21 HumanEval, 10/10 tool calls and 3/3 agentic bug-fix loops through the real server with thinking on; none of these figures has been independently verified. The post also says the same engine runs GLM-5.3-Flash, Qwen3.8-Flash-Next and Qwen3.8-27B, that a 152 GiB uncensored V4.1 quant is on Hugging Face, and that the 510GB DeepSeek-V4.1-Flash-1017 update will be abliterated and requantized within a couple of days.
r/LocalLLaMA · 🔥 0 - GPT-6 Astra solves 49 of 50 problems on TheoPhysEval physics benchmark
Thirty-five researchers from institutions including ICTS, Harvard, KU Leuven and Google DeepMind released TheoPhysEval, a 50-problem benchmark of unpublished theoretical physics questions with verifiable answers. GPT-6 Astra, running in OpenAI's Codex environment, answered 49 of 50 correctly (98%) at under $3 per problem and about $116 for the full run, while Anthropic's Opus 5.5 in Claude Code scored 92%, Gemini 3.8 Flash 68%, DeepSeek-V4.1-Flash 54% and GLM 5.3 42%. The authors said several apparent model misses were traced to small errors in their reference answers, and that without web access Astra and Opus each scored 92%; 36Kr additionally reported an initial Astra score of about 88% before corrections and cited a separate MIT study on gains in theoretical science problem solving.
The Decoder · 🔥 0 - DeepSeek-V4.1-Flash: 552B multimodal MoE with 1M context
DeepSeek published DeepSeek-V4.1-Flash on Hugging Face, a natively multimodal mixture-of-experts model with 552B backbone parameters and up to a 1M-token context, built on a Causal Encoder-Decoder design (20 encoder plus 20 decoder layers) that activates only 8B parameters per token in prefill and 16B in decode. It combines Compressed Sparse Attention 2 with FP4 main-KV caching (E2M1 format, one E4M3 scale per 16 channels) to cut the global KV cache to about 890 bytes per token — roughly a quarter of DeepSeek-V4-Flash — and offers a continuously controllable reasoning effort from 1 to 100, with weights under the MIT license. The vendor's own model card reports Terminal-Bench 2.1 at 90.6 (versus Opus-5.0 at 89.1 and GPT-5.6 Sol at 88.8), DeepSWE v1.1 at 74.2 and a Codeforces rating of 3471, but these figures are self-reported and not independently verified; the release also omits a Jinja chat template and ships a deepseek-recipe toolkit for prompt encoding instead.
DeepSeek · Hugging Face · 🔥 0 - Lenovo's TianxiCode tops SWE-bench-Live with 71% resolve rate
TianxiCode, a code-agent framework developed by Lenovo's Tianxi AI, ranked first on the SWE-bench-Live (Lite split) with a 71% issue-resolution rate and passed the benchmark's official Verified review. According to Lenovo-supplied material, the result was achieved by pairing TianxiCode with DeepSeek-v4.1-Flash, with the framework providing multi-hop cross-file retrieval, autonomous planning and multi-turn tool calling, and closed-loop patching with test-driven self-correction. Lenovo says it plans to bring TianxiCode into its AI hardware products.
量子位（原生 RSS） · 🔥 0 - ByteDance Seed paper ties DeepSeek long-context swings to KV-cache phase sensitivity
Per DoNews, ByteDance's Seed team posted an arXiv paper in late September 2026 arguing that "phase sensitivity" introduced by chunked KV-cache compression is the main driver of long-context retrieval fluctuations in DeepSeek-V4 series models. Covering open-weight models including DeepSeek-V4-Flash, V4-Pro and V4.1-Flash, the work says compression windows impose token phase coordinates, so the same information can be retrieved with accuracy differing by as much as 40 percentage points across phases — a periodic degradation that averaged benchmark scores tend to hide. The team calls for better cache-compression designs to improve long-context stability.
DoNews · 🔥 0 - vLLM v0.31.0 expands serving, speculation, and model support
vLLM v0.31.0 adds broad serving and model-support updates, including DeepSeek-V4.1-Flash optimizations, Model Runner V2 speculative decoding, larger-scale expert parallelism, and improved multimodal support. It also introduces the `vllm preload` weight-cache daemon for faster engine restarts and experimental CRIU-based initialized-engine snapshots, alongside several security and breaking changes.
vLLM Releases · 🔥 0
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