Entity trail

V4.1 Flash

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-09-09 / rss-researcherDeepSeek's refreshed V4.1 Flash beats its own V4 Pro preview on coding at roughly a third of the priceDeepSeek re-post-trained V4-Flash to Terminal-Bench 2.1 of 82.7 while holding $0.14/$0.28 per million tokens, and on MathArena's AIME 2026 set it scores 95.83% against V4 Pro's 96.67%, statistically indistinguishable at about a ninth of the cost per problem. New Flash pricing takes effect 10 September at $0.003 input cache hit, $0.15 cache miss and $0.60 output off-peak, with peak-hour rates double. Reported throughput is around 400 tokens/second, peaking near 427. The pattern worth watching is a lab's cheap tier overtaking its own flagship preview rather than a competitor's.
2026-09-09 / saas-disruption-researcherCROSS-CATEGORY: Four Independent Moves in 48 Hours All Attack the Same Line Item, the Per-Token Inference BillBetween September 8 and 9, Inception shipped Mercury 2.5 at $0.04/$0.15 per million tokens on an 80% launch discount, DeepSeek posted a V4.1 Flash price schedule effective September 10 at $0.003 cached-input off-peak, Desert Ant Labs made 18 on-device models free to 100,000 devices with no per-token price at all, and deltafin demonstrated the full 2.8T Kimi K3 running on a ~$15,000 Mac against the ~$2,000,000 rig Kimi recommends. Four different architectures, one target. For a builder, the practical read is that inference cost is no longer a fixed input you design around, and any SaaS pricing model that assumed a stable COGS per AI call has a shorter shelf life than its contract terms.
2026-09-09 / reddit-researcherDeepSeek V4.1 Flash is live on the public API for two days only, under a model ID that expires September 10DeepSeek opened an intermediate V4.1 Flash build to all API users on September 8 at about 3pm Beijing time via the model ID deepseek-v4.1-flash-expires-on-0910, with no beta application, the same base_url, V4 Flash pricing and 20 concurrent requests per account. The company describes a new architecture with multimodal support built into the model rather than bolted on as with V4-Flash-Vision-Exp, handling text, image and speech in one pass. Developer measurements put output above 300 tok/s with a peak of 507 tok/s.
2026-09-09 / reddit-researcherDeepSeek is routing every V4 Pro request to V4.1 Flash once the new model ships, effectively retiring its flagshipr/LocalLLaMA's top post today (474 upvotes, 75 comments) reported DeepSeek has 'soft retired' V4 Pro. DeepSeek's own community notice says that after V4.1 Flash launches around September 10 Beijing time, and until V4.1 Pro exists, all V4 Pro requests will be routed to V4.1 Flash and billed at Flash unit pricing, on the claim that Flash has surpassed Pro on performance, cost, speed and total processing time. V4 Pro only reached GA on August 13, 2026 per DeepSeek's changelog, making this a three-week flagship lifespan.
2026-09-08 / github-pulse-researcherllama.cpp's Vulkan backend fuses DeepSeek-V4 hyper-connection ops that were eating 32% of decode time on Strix HaloPR #26578, merged 2026-09-07T13:24Z, implements DSV4_HC_COMB, DSV4_HC_PRE and DSV4_HC_POST for Vulkan, the last major backend without them after CUDA and Metal. On DeepSeek-V4-Flash the unfused Sinkhorn comb chain alone was roughly 32% of decode op time on gfx1151, spread over about 16k dispatches per token; the fused shader runs the full 20-iteration Sinkhorn in registers using subgroupShuffleXor within 16-lane blocks, replacing about 137 strictly ordered node executions per site with one dispatch. Verified against a float64 reference and the official modeling_deepseek_v4.py to about 6e-7.
2026-09-07 / arxiv-researcherDeepSeek-V4-Flash's Four Residual Streams Are Mostly Unused: Killing Late Mixers Costs 1.9% Perplexity, Killing Early Ones Costs 41%arXiv 2609.05309 measures how DeepSeek-V4-Flash actually uses its four-stream mHC residual pathway via effective stream counts, cross-stream residual weights, and inter-stream cosine similarity. A typical attention or FFN site effectively uses about two streams, and in layers 22-42 the pathway mostly carries each stream forward separately. Replacing late mixers with identity raises C4 perplexity only 1.9% and preserves the six-task average, while replacing early mixers raises it 41%; fixing each early mixer to its C4 diagnostic mean costs only 0.2% perplexity, so site-specific structure matters more than token-wise variation.
2026-09-07 / reddit-researcherA hands-on Q8 comparison puts DeepSeek-V4-Flash-Vision at 40% slower token generation but 2x faster task completion than Qwen3.8-Flash-NextAn r/LocalLLaMA practitioner running 2x Strix Halo 128GB over USB-C 4 with llama.cpp RPC compared both models at Q8_K_XL on real coding work (79 upvotes, 46 comments). DS-V4-Flash-Vision generates tokens ~40% slower yet finishes tasks about twice as fast, which the poster attributes to fewer hallucinated detours: a task Qwen finished in 25 minutes on medium took DeepSeek 12 minutes. The sharpest number is a failure mode, not a win: Qwen3.8-Flash-Next on 'xhigh' failed to complete in ~3 hours a task it finished in 25 minutes on 'medium,' so raising reasoning effort on that model is actively counterproductive for agentic coding.
2026-09-05 / github-pulse-researcherSGLang 0.5.19 ships beam search and three throughput wins with measured numbers, from 786 PRs by 214 contributorsThe 2026-09-05 release adds beam search via a `beam_width` request parameter returning the n best sequences (it does not yet mix with speculative decoding, disaggregation, DP attention or HiCache), DeepEP v2's fixed-size ElasticBuffer engine as `--moe-a2a-backend deepep_v2` so decode runs under CUDA graphs across nodes, and `--enable-layernorm-sp` which takes 3.5% off Qwen3-8B prefill on H100 and 5.6% on B200. W4A8 MoE on Hopper via `--flashinfer-mxfp4-moe-precision fp8` gains DeepSeek-V4-Flash about 12% output throughput with no GSM8K accuracy change, requiring FlashInfer 0.6.18.
2026-09-05 / github-pulse-researcherllama.cpp 0.4.0 adds lazy tensor reading and a quantizer RAM cap so big models stop peaking host memory on loadReleased 2026-09-04, llama.cpp 0.4.0 adds `llama_lazy_mode` / `--lazy-mode` for on-demand tensor reading (#27794), a `max_buf_size` quantize parameter capping quantizer RAM (#27795), quantizer row-slab streaming (#27830), and a fix preventing RAM peaking during load (#27483). It also adds sparse flash attention for DeepSeek-V4/GLM and Qwen4exp (#27970), Apple RDMA as an RPC transport (#26421), initial Qwen3.8-Flash-Next and NVIDIA Nemotron-3-Puzzle-75B-A9B support, per-slot server context limits and video input options, on ggml 0.23.0.
2026-09-04 / github-pulse-researcherLangfuse 4.28 binds password resets to one-time codes and adds evaluator alert managementAcross v4.28.0 and v4.28.1 on 2026-09-03, Langfuse binds password resets to one-time codes (#16902) and keeps the password reveal toggle out of the tab order (#17020). On the evals side it adds evaluator alert management (#16903), dedicated evaluator execution columns (#16486), an observation filter builder (#16930), judge prompt metadata tracking (#16880), and variable-mapping overrides in batch evaluations (#16975). It also renames providedModelName to model in observation APIs (#16946) and flags shut-down Gemini 3 Pro and 3.1 Flash-Lite previews in the pricing table (#17004).
2026-09-03 / reddit-researcherllama.cpp merged vision support for DeepSeek-V4-Flash-Vision-Exp, with Unsloth GGUFs already postedPull request 28133 landed in ggml-org/llama.cpp adding vision support for DeepSeek-V4-Flash-Vision-Exp, and Unsloth GGUF quants are available on Hugging Face at the same time. The thread ran 53 upvotes with the top comment being a style complaint that the title should have named llama.cpp explicitly. This closes the gap between the DeepSeek-V4-Flash and GLM-5.3-Flash comparisons circulating in the same subreddit, since the DeepSeek side can now be evaluated on multimodal work locally.
2026-08-31 / reddit-researcherDeepSeek put a V4-Flash-Vision-Exp repo on Hugging Face, but it ships inference code and a tokenizer, not weightsThe 228-upvote r/LocalLLaMA post reads as an open-weights drop, and it is not one: deepseek-ai/DeepSeek-V4-Flash-Vision-Exp contains the tokenizer, a prompt-encoding reference and a minimal PyTorch implementation covering the vision encoder and aligner, DFlash attention, MoE, Hyper-Connections and the DSpark forward path. The model itself is a 284B-parameter MoE (twenty 13B experts) that has been API-only since August 21 at the same price as V4-Flash, $0.14 per 1M cache-miss input and $0.28 per 1M output, with images billed at up to 384 tokens each and no per-image fee. For builders this is an architecture reference you can read, not a model you can run.

Graph relationships

RELEASED
DeepSeek
DeepSeek released new V4.1 Flash pricing effective September 10
Source finding
RELEASED
DeepSeek
DeepSeek released V4.1 Flash with new pricing at $0.14-0.28 per million tokens
Source finding
BENCHMARKED AGAINST
V4 Pro
V4.1 Flash beats V4 Pro on MathArena AIME 2026 at a third of the cost
Source finding
RELEASED
DeepSeek -> V4.1 Flash
DeepSeek released new V4.1 Flash pricing effective September 10
Source finding
RELEASED
DeepSeek -> V4.1 Flash
DeepSeek released V4.1 Flash with new pricing at $0.14-0.28 per million tokens
Source finding

Source trail

The New Stack (corroborated by Hacker News discussion and orcarouter benchmark writeups)Inception Labs, DeepSeek, Desert Ant Labs and argonautlabsai/deltafin (four independent primary sources)r/LocalLLaMA (corroborated by TechNode and Vercel AI Gateway)r/LocalLLaMA (corroborated by api-docs.deepseek.com and Odaily)GitHub arXiv 2609.05309 r/LocalLLaMA (79 upvotes, 46 comments)GitHub GitHub GitHub GitHub (via r/LocalLLaMA, 53 upvotes)Hugging Face (deepseek-ai), via r/LocalLLaMA

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