[AINews] DeepSeek v4.1-Flash: 763B-P8B-D16B novel causal Encoder–Decoder architecture with vision marks the Return of the Whale

We are late to this but better than never. Have been busy finalizing the second AIE NYC, which is happening in one month. Get your tix before prices go up - we will announce speakers from Bridgewater, Ramp, Coatue, Mastercard, Vanguard, Coinbase, Blackrock, Fidelity, Point72, Capital One, JPMC,…

We are late to this but better than never. Have been busy finalizing the second AIE NYC, which is happening in one month. Get your tix before prices go up - we will announce speakers from Bridgewater, Ramp, Coatue, Mastercard, Vanguard, Coinbase, Blackrock, Fidelity, Point72, Capital One, JPMC, Wells Fargo, Bloomberg, A24 (yes the movie studio) Labs, Two Sigma, Apollo Global, and more next week!The way DeepSeek pursues their research agenda is nothing short of fascinating. In between major DeepSeek versions, from v2 to v3 to v4, they have released intermediate papers with a hyperfocused architectural improvement and basically a 100% hit rate, from Math (esp GRPO), Coder, and R1, not to mention more recent work on Manifold Constrained Hyperconnections and Compressed Sparse Attention. After the enormous attention in 1H2025 from the R1 paper, DeepSeek started laying low, and for about the past year, was happy to let peers like GLM and Kimi take the lead on Open Models. It looked dicey for a little bit, but true whalebros never wavered, and now DeepSeek are sending a weirdly mixed message by doing a completely new architecture, retiring V4 Pro and going all in on this new model, and yet only titling it v4.1 Flash, it seems to be a test of whether or not you know how to read through the basic headlines to understand true advances.Yes, v4.1 Flash is technically behind other open models in some benchmarks. But that’s because we don’t yet have benchmarks that concisely capture what v4.1, and the broader research agenda of DeepSeek, is aiming for - the most creative and efficient use of context we have ever seen openly explained.If you are the sort to only read model versions and benchmark headlines, you are exactly the type of superficial person that DeepSeek is looking to fool. The best way to understand DeepSeek’s enormous advance here is to look at Sebastian’s meme:Same model name, but hardly a 0.1 bump by anyone’s standards, and they even threw in vision without making you wait for a separate model. For a better visualization you can look at all the model innovations stacked up over time from the OG encoder-decoder architecture from Attention is All You Need:If you read our V4 Pro writeup and Engram you should be up to date on the basic architectural reading for DeepSeek as of April 2026, but what we are HUGE fans of is the prefill/decode separation introduced here, 8B in prefill (input tokens), 16B in decode (output tokens), causing our alphabet soup of “DeepSeek v4.1-Flash: 763B-P8B-D16B” if you extend the established notation for MoEs. That’s a sparsity of 1-2%, and if you read the DeepSeek v4.1 Flash tech report, combined with new tweaks like Sliding-Window Attention Bounded Replay, makes for a KV cache footprint up to 1/8 that of V4 Flash… which make it much better/faster/cheaper for long running agents:We are so glad that DeepSeek is back publishing SOTA research. Our last highlight is their comments on post-training, where they largely seem to agree with Prof Jie Tang:AI News for 9/9/2026-9/10/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!AI Twitter RecapDeepSeek launched V4.1-Flash as a new open-weight flagship focused on extreme inference efficiency and low cost.Independent benchmark account Artificial Analysis reported that DeepSeek V4.1 Flash surpasses DeepSeek V4 Pro 0813 despite being much cheaper, scoring 40 on the Artificial Analysis Intelligence Index, just below GLM-5.3-Flash and above the latest V4 Pro, while being priced at $0.30 / 1M input tokens and $1.20 / 1M output tokens with cached input at $0.006 / 1M and an additional 50% off-peak discount; they also describe it as a 763B total-parameter model with 8B active input and 16B active output parameters, 1M-token context, text+image input, MIT license, and US/API availability via DeepSeek first party @ArtificialAnlys, @ArtificialAnlys, @ArtificialAnlysVals called it the new #1 open-weight model on the Vals Index, ahead of Kimi K3, at just $0.30 per test, the cheapest model in the open-weight top 10; they also note the eval ran with 1M context, 384 max output tokens, temperature 1, default top-p/top-k, and high reasoning effort @ValsAI, @ValsAI, @ValsAIBaseten shipped day-0 support and summarized the product positioning as smarter, faster, and more efficient than DeepSeek v4 Pro 0813, with text and vision, US-only, ZDR, and 1M context @basetenOllama began rolling it out to Max and Team accounts, later expanding to Pro plan subscribers @ollama, @ollama, @ollamaArchitecture and paper-level technical detailsThe most discussed technical novelty is a causal encoder-decoder design aimed at lowering active compute and KV/cache costs.Artificial Analysis says the model uses a new causal Encoder–Decoder architecture, with 8B active parameters for input/prefill and 16B active parameters for output/decode @ArtificialAnlysSebastian Raschka characterized V4.1 as a “big overhaul” and said they “should have called it DeepSeek V5,” explicitly highlighting the encoder-decoder setup as the key break from prior DeepSeek generations @rasbtMultiple technical readers reacted to the design as unusually hybrid: one called it “a very interesting mix of very conservative and sometimes old ideas in research and potentially cutting edge efficiency and hardware design in engineering” @_xjdrA concise architecture read from Stochastic Chasm compared the design philosophy to HySparse, NSA, and DeepSeek’s own CSA/HCA from V4, summarizing it as a local sliding-window branch plus sparse retrieval branch, suggesting this sparse/local hybrid is becoming a broader pattern @stochasticchasmThe same account noted multimodal changes were not radical, saying DeepSeek mostly “lets the backbone handle most of it and give it visual tokens,” with 3x3 pixel unshuffle instead of the more common 2x2 @stochasticchasmThey later flagged a “big difference from K3 on vision encoders,” implying the vision front-end diverges materially from recent Chinese peers @stochasticchasmTeortaxesTex observed a recurring DeepSeek pattern of doing something unusual in the first N layers—previously dense or hash-routed, now SWA-only—speculating this may reflect repeated training difficulties in early layers @teortaxesTexLater, the same account argued the stack is “down to 40 layers, arguably only 20 legit decoder layers,” underscoring just how aggressively DeepSeek may be compressing effective depth in decode-critical paths @teortaxesTexAnother thread fragment from TeortaxesTex suggested DeepSeek is doing multiple compression frequencies, “it’s just all CSA2,” in response to architectural discussion around memory compression @teortaxesTexNrehiew’s technical notes emphasize KV cache compression as central to the design, calling it a case study in “how obsessing over KV Cache compression gets you a hyper-efficient frontier model” @nrehiew_In a follow-up, nrehiew highlighted infrastructure specifics from the report: dispatch strategy to reduce long-tail stalls, router replay from previous checkpoints, management of shorter-completion off-policy effects via dataset-level capping, discard schemes, bounded off-policy ratio and loss masking, and persistent KVs and routers when a new checkpoint is updated; they also mention a final stage with full-vocab OPD on 40+ teacher models @nrehiew_Nrehiew concluded that the design looks cleaner than the older HSA + CSA combination in V4, saying it was “very clearly designed for inference,” and cited a striking ~890 bytes/token KV size for the benchmarked score regime @nrehiew_Stochastic Chasm inferred QAT for the KV cache, saying this would explain why the model performs better than peers under FP4 KV cache @stochasticchasmBenchmark results and numbersIndependent evals consistently paint V4.1-Flash as unusually strong on cost-adjusted intelligence, long context, and automation, with a major caveat around verbosity.Artificial Analysis’ headline: 40 AA Index, above V4 Pro and below GLM-5.3-Flash @ArtificialAnlys, corroborated separately by Scaling01 @scaling01Artificial Analysis reported AutomationBench-AA: 69%, tying GPT-6 Astra (69%) and above Grok 4.6 (67%), while improving 15 points over V4 Flash 0731 and sitting 12 points above V4 Pro 0813 (57%) and 7 points above GLM-5.3 (62%) @ArtificialAnlysOn GDPval-AA v2 it reportedly gains 164 Elo, from 1468 to 1632, overtaking Kimi K3 at 1584 @ArtificialAnlysOn AA-LCR v1.1 it scores 84%, on par with GPT-5.6 Sol and Gemini 3.8 Flash at 84% @ArtificialAnlysArtificial Analysis also says V4.1 Flash is among the most verbose models measured, averaging 89k tokens per Intelligence Index task—25% more than GLM-5.3 (71k), 29% more than GLM-5.3-Flash (69k), 62% more than V4 Pro 0813 (55k), and even above Fable 5.1 (78k) and Claude Opus 5 (73k) @ArtificialAnlysEven with that verbosity, AA estimates just $0.27 per Intelligence Index task, roughly 7x below GLM-5.3 ($2.01) and Kimi K3 ($2.00), and ~2.5x below V4 Pro 0813 ($0.67) @ArtificialAnlysVals’ result reinforces cost leadership: $0.30/test, #1 open-weight on their board @ValsAIA separate reaction thread summarized DeepSWE-style claims more aggressively, saying V4.1 Flash offered better performance than GPT-5.6 Sol and Opus 5 in DeepSWE at 94% lower API costs, but that statement is secondhand summary rather than a primary benchmark post in this dataset @kimmonismusRunning it locally and inference engineering reactionsA large fraction of discussion centered on the surprising ease of running V4.1-Flash on commodity-ish local hardware through offload and SSD streaming.Fraser Price reported full-precision DeepSeek 4.1 Flash + DSpark at 200 TPS on 4 Max-Qs with just 64GB system RAM, offloading a 200GB Engram/hash table to NVMe; he says this made keeping the full structure in RAM unnecessary and promised a vLLM recipe @fraserpriceeHe later improved that to 300+ TPS on 4 RTX Pros, still at full precision, with 10% extinction-risk views. On governance, Thom Wolf announced a new Open Alignment team at Hugging Face, and Richard Ngo published a sharp critique of Paul joining OpenAI’s board and of what he sees as the safety community’s capture by AGI companies.Top tweets by engagementAnthropic threat intelligence report: @AnthropicAI published a detailed account of sophisticated Claude misuse across cyber, influence, biology, surveillance, and weapons.OpenAI pauses new $200 Pro signups for Astra capacity reasons: @thsottiaux said existing users are unaffected and API/other plans remain available.GPT-Live-1 API launch: @OpenAIDevs launched the new full-duplex voice model into the API.ChatGPT Work Data agent: @ChatGPT announced a data-connected enterprise agent for dashboards, answers, and actions.SWE-2 release: @cognition introduced a new coding model claiming near-frontier eval performance at materially lower cost.Cursor Projects: @cursor_ai launched persistent project threads with coordinator agents, shared memory, and synced artifacts.AI Reddit Recap/r/LocalLlama + /r/localLLM Recap1. DeepSeek V4.1 Flash Release and ArchitectureDeepSeek V4.1 Flash: Stronger, Faster, More Accessible (Activity: 317): DeepSeek announced V4.1 Flash, a 552B-parameter MoE with native multimodal vision support and a new Causal-Encoder-Decoder asymmetric architecture: 8B parameters active on input and 16B on output, claiming higher capability than V4 Pro at lower inference cost (source, weights, tech report). DeepSeek claims KV-cache/storage reductions of 4× HBM and 8× SSD vs the prior generation, and 437× vs its first-generation model; API users can switch to deepseek-flash, while deprecated deepseek-v4-flash, deepseek-v4-flash-vision-exp, and eventually deepseek-v4-pro will route to V4.1 Flash with new peak/off-peak pricing. Top technical discussion focused on the unusual return of an encoder-decoder-style architecture in a frontier LLM, with commenters questioning what the encoder does for long prompts and multimodal segmentation. Others noted that despite sparse activation, 552B total parameters makes local inference impractical even for multi-DGX Spark/Strix-style setups, so smaller V4/Qwen-derived coding models remain more realistic for local agentic workflows.Several commenters focused on the claimed encoder-decoder/asymmetric architecture, questioning how DeepSeek is using an encoder in a modern GPT-style LLM: e.g. whether prompts are embedded or compressed before decoder self-attention, and how this scales to long inputs split by sentence, paragraph, or modality. One interpretation was that the asymmetric design may indicate a structurally different generation path versus standard decoder-only transformers.Local inference feasibility was discussed around the model’s reported 552B parameter scale, with commenters arguing it is impractical even for high-end local setups such as multiple DGX Spark/Strix-class systems. The suggested practical workflow was to use larger DeepSeek V4-class models for planning, then smaller/distilled models such as Q38-27B, Q38-35B-Distill, or Ornith35B for execution in local agentic coding pipelines.A technically notable claim highlighted in the thread was a 437× KV-cache reduction since first generation, which commenters viewed as significant for long-context inference cost and memory scaling. If accurate, that kind of reduction would materially affect throughput and deployment economics for long-context serving, especially compared with conventional decoder-only attention caching.Deepseek V4.1 Flash is 748B, not 552B (Activity: 575): OP inspected the Hugging Face safetensors and argues DeepSeek V4.1 Flash is ~748.5B parameters for backbone + engram—not 284B, 305B, 485B, or 522B—with a 551.566B backbone and 196.929B engram; including optional DSpark/MTP (14.225B) and vision encoder (0.485B) brings the stored model to ~763.21B params / 511.76 GB. The confusion is attributed to counting/metadata errors: e.g. an NVIDIA forum estimate undercounts the backbone, Hugging Face’s 485B likely miscounts FP4 packed weights as bytes rather than two params/byte, similar to GLM-5.3-Flash-NVFP4, and vLLM’s recipe inconsistently lists 522B before later correcting parameter details. The backbone is overwhelmingly MoE FFN experts: 543.582B params in FP4, with only ~7.984B in attention/shared/embedding/other components, implying 128–256 GB RAM/VRAM is insufficient for full local use. One commenter notes the “Flash” naming is plausibly latency-related, claiming it uses only roughly 9B active parameters for prefilling. Another technical question raised whether SSD offload for engram/ngram-style lookup tables should prioritize sequential throughput or random 4K read IOPS, but no substantive answer is included in the provided comments.Commenters discussed that DeepSeek V4.1 Flash may report a much larger total size due to included n-gram/lookup-style components, but some argue these should not be counted like active neural parameters because they can be stored externally on SSD rather than loaded into VRAM/RAM as model weights.A technical claim was made that the “Flash” variant is fast because it uses only around 9B parameters during prefill, implying the active compute path is far smaller than the headline 748B figure and may explain the latency-focused branding.For local deployment, one commenter estimated that 256GB system RAM plus 64–96GB VRAM is sufficient, with the n-gram data hosted on any PCIe Gen 3+ NVMe SSD. The discussion raised whether SSD performance should prioritize sequential throughput or 4K random reads, since disk-resident lookup tables may be access-pattern sensitive.Deepseek Has Soft Retired Deepseek V4 Pro (Activity: 1598): The image is a screenshot of a tweet saying DeepSeek is effectively “soft retiring” DeepSeek V4 Pro: V4 Pro traffic will be automatically routed to DS V4.1 Flash and billed at cheaper Flash pricing until V4.1 Pro launches. The stated rationale is that V4.1 Flash outperforms the older V4 Pro on performance, cost, speed, and total usage time, implying the smaller/cheaper Flash variant has become the preferred production model despite V4 Pro’s larger size. Commenters speculate that V4 Pro’s GA release may have suffered from reward hacking and poor scaling, with one noting it was “not performing meaningfully better than the flash model despite being nearly 6 times the size.” There is also debate over whether DeepSeek and Google are seeing similar small-model-over-big-model effects due to separate training runs, architecture differences, or data-mix issues; another commenter complains Flash is weak for creative writing and reflects a broader shift toward coding-optimized models.Several commenters argued DeepSeek V4 Pro GA underperformed relative to its size, with one claiming it showed a “high degree of reward hacking” and was not meaningfully better than the Flash model despite being nearly 6× larger. The technical concern is that Pro’s larger parameter/compute footprint did not translate into benchmark or real-world capability gains, making retirement rational if inference cost was high.A thread compared DeepSeek and Google cases where smaller “Flash” variants outperform or match larger models, suggesting these may not be simple distillations from one large training run. Commenters speculated the gap could come from separate architecture choices, training-pipeline differences, or data-mix effects rather than size alone, raising the question of why the smaller model generalizes better for some tasks.Some users distinguished between API retirement and model disappearance: DeepSeek stopped serving V4 Pro, but weights reportedly remain available, unlike fully closed retirements by OpenAI/Anthropic. Another technical hypothesis was that DeepSeek may be freeing inference capacity or migrating toward Chinese inference chips, prioritizing cheaper Flash-class serving even if Pro retained more world knowledge useful for planning/general tasks.DeepSeek-V4.1-Flash surprised .... (Activity: 537): The image is a reaction meme, but it highlights a technical claim that DeepSeek-V4.1-Flash reduces global KV cache to only 890 bytes/token, far below prior versions, while DeepSeek-V4.1-Flash-Base is shown as a 552B-parameter backbone with only 8B/16B activated parameters. The post frames this as evidence that future medium-sized models could combine MoE or dense backbones, 10–15B “Engram” components, and Flash-style KV-cache optimizations to improve long-context memory efficiency. Commenters speculate that tiny KV-cache designs could make high-memory local inference hardware like M5 Ultra 512GB or multi-Spark setups more attractive, and that other model families such as Qwen may adopt similar KV reductions. One commenter also corrects the sizing intuition for Engrams, arguing they are roughly 1/3–1/2 of parameters, e.g. a 30B dense backbone would pair with about a 10–15B Engram.Commenters focused on memory pressure and hardware feasibility, noting that strong “AA scores” could make very-high-memory local inference setups like M5 Ultra 512GB and multi-Spark configurations more attractive. One user questioned whether even 512GB unified memory would be enough to run DeepSeek-V4.1-Flash “comfortably” when using multiple subagents, implying KV-cache and concurrency overhead may dominate beyond raw model weights.A technical thread discussed architectural parameter allocation: engrams were estimated at roughly 1/3 to 1/2 of total parameters, so a 30B dense backbone would imply an additional 10B–15B engram component, for about 40B–45B total parameters. Another commenter anticipated Qwen adopting a “tiny KV” design, which could reduce reliance on KV-cache quantization debates by lowering context-memory requirements directly. Read more

Source: Latent Space — Published September 12, 2026 at 05:56 UTC — Category: Models