DeepSeek releases V4.1 Flash with efficient MoE architecture
ByPulseAugur Editorial·[68 sources]·
DeepSeek has officially released its V4.1 Flash model, a 552 billion parameter Mixture-of-Experts (MoE) model featuring a Causal-Encoder-Decoder (CED) architecture and native multimodal capabilities. This new model is designed for efficiency, activating only a fraction of its parameters during inference, which significantly reduces costs for long-context and agentic workloads. V4.1 Flash reportedly outperforms previous models like GPT 5.6 "Sol" and Anthropic's Opus 5 on various benchmarks, particularly in agentic tasks, while offering a substantial reduction in operational costs.
AI
IMPACT
This efficient MoE architecture could significantly lower the cost of running large context models for agentic tasks, potentially accelerating enterprise adoption.
RANK_REASON
Frontier-lab model release with system card and technical details.
deepseek v4.1 flash has arrived on together ai
it beats gpt-5.6 sol on agentic benchmarks at one third the cost per task
the model supports a 1m context window and native multimodal input, with 552b total parameters https://t.co/XXXv0QRNTm
X — Together (inference / OSS)
TIER_1English(EN)·togethercompute·
Deepseek-V4.1-Flash is available now on Fireworks!
It is a 552B Parameter MoE built for coding, cybersecurity, and agents. It is the ideal workhorse model that outperforms Opus 5 and GPT 5.6 Sol at 1/40th the cost on DeepSWE,CyberGym, and Automation Bench!
Interested in htt…
X — Fireworks (inference infra)
TIER_1English(EN)·FireworksAI_HQ·
Deepseek-V4.1-Flash is available now on Fireworks!
It is a 552B Parameter MoE built for coding, cybersecurity, and agents. It is the ideal workhorse model that outperforms Opus 5 and GPT 5.6 Sol at 1/40th the cost on DeepSWE,CyberGym, and Automation Bench!
Interested in higher …
X — SemiAnalysis
TIER_1Nederlands(NL)·SemiAnalysis_·
Congrats to @deepseek_ai on releasing DeepSeek-V4.1-Flash!
> 552B backbone, with a causal encoder-decoder activating just 8B params at prefill, 16B at decode
> 196B Engram memory accessed through sparse lookups
> ~8x less persistent KV than V4-Flash through bounded repl…
From 04:00 UTC on Sept. 14, DeepSeek serves every deepseek-v4-pro request with V4.1-Flash at Flash pricing until V4.1-Pro ships — an operational cutover for pinned Pro model IDs.
dev.to — Claude Code tag
TIER_1Nederlands(NL)·Owen·
<p><strong>Claude Code can connect to DeepSeek V4.1 Flash through <code>https://api.deepseek.com/anthropic</code>. DeepSeek’s documented setup uses <code>deepseek-flash[1m]</code> for the main, Opus and Sonnet selections, and <code>deepseek-flash</code> for Haiku and subagents.</…
DeepSeek has released V4.1 Flash for general availability, a 552B MoE model with Causal-Encoder-Decoder architecture and native vision. New Flash pricing started September 10, V4 Pro routes to Flash on September 14, and the app merges modes into one smart mode.
<p>Long-horizon agents have turned LLM serving into an input-heavy workload. Repeated prefills and million-token contexts leave KV caches that strain HBM, SSD capacity, and bandwidth. DeepSeek AI built its newest release around that exact bottleneck. DeepSeek-V4.1-Flash is a mult…
DeepSeek has launched a short V4.1 Flash internal beta with a new architecture and native multimodal support. The API ID deepseek-v4.1-flash-expires-on-0910 is priced like V4 Flash, capped at 20 concurrent requests, and set to expire around September 10.
📄 DeepSeek-V4.1-Flash paper pushes KV cache compression—key for faster long-context inference. 93 upvotes on Hugging Face show the community is paying attention. https:// huggingface.co/papers/2609.199 69 # AI # MachineLearning # Research
<h4>Plus, prompt caching, smarter LLM routing, agent context, and shared research budgets.</h4><p>Good morning, AI enthusiasts!</p><p>This week, I tested <strong>DeepSeek V4.1 Flash</strong>, including its changes to KV-cache memory and how well it performs on our own writing ben…
DeepSeek has launched V4.1 Flash, a time-boxed beta of its AI model with native multimodal support. The preview offers the same pricing as V4 Flash with 20 concurrent requests, and runs until around September 10. Unlike the earlier vision experiment that attached a separate exten…
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1wj4qj4/cactus_needle_3_a_sliceable_829mb_automation/"> <img alt="Cactus Needle 3: A Sliceable 8-29MB Automation Foundation Model That Matches DeepSeek v4 Flash" src="https://preview.redd.it/wypizqswz4qh1.gif?…
dev.to — LLM tag
TIER_1English(EN)·张洲诚（Zack.ZHANG）·
<p>On September 2, Alibaba put the 0902 snapshot of qwen3.8-max on Bailian, and it took the top spot on Arena.ai's Code Arena WebDev leaderboard on launch day, ahead of Claude Opus 5 and Kimi K3. Eight days later DeepSeek shipped V4.1-Flash: KV cache 437x smaller than the first g…
🤗 deepseek-ai/DeepSeek-V4.1-Flash hits 288.4k downloads in 30 days—MIT licensed image-text model with real traction. See its usage rank now. https:// olud.ai/#leaderboard # OpenSource # AI # HuggingFace
<!-- SC_OFF --><div class="md"><p>I reallise this is not exactly a locally hostable model, at least definitly not on my hardware. But I figured some of you might have some insight on this.<br /> I run an agentic coding benchmark. The model gets tools (bash, file editing, a headle…
<p>I got some new hardware.</p> <p>Two NVIDIA DGX Sparks. I connected them to each other with two QSFP cables, and put both of them on my Tailscale network.</p> <p><a class="article-body-image-wrapper" href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down…
<p>DeepSeek V4.1 Flash just processed 2.15 billion tokens for $19.22 across three days of continuous agent operation. That works out to $0.00892 per million tokens processed. The previous benchmark on DeepSeek V4 Pro delivered 612.9 million tokens for $28.35. This is not incremen…
<p><a href="https://synthorai.io/models/deepseek-v4-1-flash/" rel="noopener noreferrer">DeepSeek V4.1 Flash</a> costs $0.30 per million input tokens and $1.20 per million output at peak, half that off-peak, and on 11 tasks with a checkable answer, run three times each, it got 33 …
<p><strong>DeepSeek V4.1 Flash</strong> shipped on September 10, 2026, and the spec sheet reads like a typo: a 552-billion-parameter MoE backbone with a 196-billion-parameter <strong>Engram</strong> conditional-memory module attached. Despite the "Flash" name, DeepSeek's own eval…
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1wfty27/3d_viz_of_how_deepseek_flash_v41_is_different/"> <img alt="3D viz of how Deepseek Flash v4.1 is different from a typical decode only transformer" src="https://preview.redd.it/13x867s33fph1.png?width=64…
<p><a href="index.html">← All guides</a></p> <h1>DeepSeek V4.1 Flash: one OpenAI-compatible call to the model that upgrades you and cuts your bill</h1> <p>DeepSeek announced V4.1 Flash on September 9, 2026, with general availability around September 10. It beats the older V4 Pro …
<h1> DeepSeek releases V4.1-Flash </h1> <p>DeepSeek shipped V4.1-Flash on September 10. Open weights on Hugging Face, MIT license. It is the smallest model in a new architecture family, and DeepSeek's claim is that it beats the flagship V4-Pro on performance, cost, speed and tota…
<h1> DeepSeek V4.1 Flash Released: V4 Pro Retires September 14 with Automatic Routing </h1> <p>DeepSeek has announced <strong>DeepSeek V4.1 Flash</strong>. At the same time, <strong>the V4 Pro service is being retired on September 14, 2026 at 12:00 Beijing time</strong> — from th…
<!-- SC_OFF --><div class="md"><p>Title says it all. </p> <p><a href="https://github.com/gjabdelnoor/Day1DeepseekV4.1-CPU">https://github.com/gjabdelnoor/Day1DeepseekV4.1-CPU</a></p> <p>The goal is pretty simple, I like having infinite slow tokens from the bioinformatics machine …
<p>DeepSeek published <strong>DeepSeek-V4.1-Flash</strong> on Hugging Face on <strong>10 September 2026 at 02:17 UTC</strong>, MIT licensed and ungated. I spent the day reading the files and poking the model through an OpenAI compatible endpoint instead of reading the announcemen…
HackerNewsTop5 (@hackernewstop5) DeepSeek v4.1 Flash가 언급되었습니다. 상세 사양은 트윗에 없지만, DeepSeek의 경량·고속 지향 모델 계열 업데이트로 보이며 비용 및 지연시간에 민감한 LLM 추론·에이전트 워크로드에서 벤치마크와 API/오픈웨이트 제공 범위를 확인할 필요가 있습니다. https:// x.com/hackernewstop5/status/20 97955095157825618 # deepseek # llm # inference # fastmo…
<!-- SC_OFF --><div class="md"><p>People keep on getting confused about this, so I looked at the safetensors on hf. </p> <p>To be precise, the main model about 551.566B parameters with 40 layers. The FFN experts total to 543.582B parameters, and the rest of the model (attention, …
dev.to — LLM tag
TIER_1English(EN)·M. Qasim. Farooqi ⛏️·
<h1> DeepSeek V4.1 Flash: New Architecture, Native Multimodal, and the End of V4 Pro </h1> <p>DeepSeek officially released <strong>V4.1 Flash</strong> on September 10, 2026. It's the smallest model in their new architecture family—and it outperforms the V4 Pro it replaces.</p> <p…
DeepSeek launched V4.1-Flash, a model that offers faster inference and higher throughput. The startup said the model also aims at greater capability and scaling to larger models. Source: The Hindu BusinessLine Info-Tech https://www. thehindubusinessline.com/info- tech/deepseek-la…
<!-- SC_OFF --><div class="md"><p>Maybe 2.6T -2.8T params including 1-1.1 T engrams and fable 5.0 level performance?</p> <p>Maybe v4.2 or 4.5 will have engram gradient modification?</p> </div><!-- SC_ON -->   submitted by   <a href="https://www.reddit.com/user/power97992"…
Nvidia veröffentlicht DeepSeek-V4.1-Flash als NVFP4-quantisiertes Modell. Die FP4-Optimierung senkt Speicherbedarf und Latenz für lokale Inferenz, ohne die Architektur des Basismodells zu ändern. https:// huggingface.co/nvidia/DeepSeek -V4.1-Flash-NVFP4 # KI # AI # LLM # AISyndic…
NVIDIA veröffentlicht DeepSeek-V4.1-Flash als NVFP4-Quantisierung (W4A4) für Blackwell-Hardware. Das multimodale MoE-Modell nutzt Compressed Sparse Attention 2 und unterstützt 1M-Token-Kontext. Deployment erfordert vier GB300 GPUs via SGLang oder vLLM. https:// huggingface.co/nvi…
📰 Why DeepSeek-V4.1-Flash Is Such an Exciting Open Model Release DeepSeek-V4.1-Flash shows how Causal Encoder-Decoder architecture, MoE, KV cache compression, CSA2, cheaper prefill, and efficient decoding can make powerful open-source AI models far more efficien... 📰 Source: KDnu…
DeepSeek V4.1 Flash just landed with 1M token context and open weights. At $0.15 in / $0.6 out per 1M tokens, see how it stacks up against GPT-4o and Claude on the hourly tracker. https:// olud.ai/latest.html # AI # LLM # OpenSource
RT @tianyi: DeepSeek hat einige neue Code-Repositories öffentlich zugänglich gemacht, um die Bereitstellung von V4.1 Flash sowie nachfolgender Open-Source-Modelle zu vereinfachen: mehr auf Arint.info # AI # DeepSeek # GitHub # KünstlicheIntelligenz # MachineLearning # OpenSource …
This open-weight model, deepseek-ai/DeepSeek-V4.1-Flash, is jumping on Hugging Face with 1.5k hearts and only 6 downloads in 30 days—specs don’t always match popularity. https:// olud.ai/#leaderboard # OpenSource # AI # HuggingFace
DeepSeek’s V4.1 Flash targets a less glamorous bottleneck: persistent state. It claims 4× lower HBM and 8× lower SSD demand for KV cache, plus lower API prices. If independently validated, agent economics may depend more on memory traffic than raw FLOPs. Evaluate cache footprint …
DeepSeek V4.1 Flash senkt den KV-Cache-Speicherbedarf auf ein Viertel des Vorgängers. Das ermöglicht effizientere Agenten-Workflows und lokale Inferenz bei 552 Mrd. Parametern. https:// the-decoder.de/neues-deepseek- modell-v4-1-flash-senkt-speicherbedarf-fuer-ki-agenten/ # KI # …
1/4 DeepSeek V4.1 Flash is taking over V4 Pro's API traffic. Released September 10, 2026, it supports native image input and a 1M-token API context window. The model weights use the MIT license. At 04:00 UTC on September 14, requests to deepseek-v4-pro will route to Flash and use…
✨ Deepseek 4.1 Flash offre prestazioni agentiche elevate, prezzi bassi e throughput token mai visti con contesto da un milione di token 👇 https:// gomoot.com/deepseek-4-1-flash- offre-prestazioni-agentiche-elevate-e-prezzi-competitivi/ # AI # deepseek41 # flash # token
DeepSeek has released V4.1-Flash, a new AI model featuring 1M context length, FP4 KV cache and cross-layer attention reuse. The model aims to improve efficiency while maintaining strong performance. https://www. marktechpost.com/2026/09/10/de epseek-ai-released-deepseek-v4-1-flas…
RT @sheriyuo: DeepSeek V4.1 Flash ist ein MoE-Modell mit 552 Milliarden Parametern und nutzt eine neuartige Causal-Encoder-Decoder-Architektur. Bei dieser asymmetrischen Struktur zwischen Eingabe und Ausgabe beträgt die Eingabeaktivierung nur 8 Milliarden Parameter, die Ausgabeak…
🤖 【MarkTechPost AI】DeepSeek AI Released DeepSeek-V4.1-Flash with 1M Context, FP4 KV Cache, and Cross-Layer Attention Reuse Long-horizon agents have turned LLM serving into an input-heavy workload. Repeated prefills and million-token contexts l... # AI # Tec ... 🔗 https://www. mar…
DeepSeek has opened an internal beta for DeepSeek V4.1 Flash, featuring native multimodal support. Source: Pandaily https:// pandaily.com/deepseek-v41-flas h-limited-beta-native-multimodal-0910 # AI # DeepSeek
<table> <tr><td> <a href="https://www.reddit.com/r/singularity/comments/1wcclh3/deepseek_v41_flash_is_getting_surprisingly_close/"> <img alt="DeepSeek V4.1 Flash is getting surprisingly close to GPT-5.6 Sol territory, while being absurdly cheap" src="https://preview.redd.it/yg5ky…
