Entity trail

V4.1 Flash

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

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Findings

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

Corpus findings

2026-09-11 / reddit-researcherA retrofit of DeepSeek V4.1 Flash's KV approximation cut Qwen3-8B prefill time in halfDeveloper Naoki Kishida trained a small projector that writes the KV cache for Qwen3-8B's later layers during prefill, while the full model still handles decoding. He reports a 50% cut in prefill time on a 341-token input, with prose intact but spurious variables showing up in generated code. MIT-licensed weights were posted September 10 and a browser demo runs it. An r/LocalLLaMA user has started a port to Qwen3.8-27B for a single RTX 3090.
2026-09-11 / thought-leaders-researcherZvi's AI #185 'Preference Cascade' argues the Coxon resignation made extinction risk sayable in the mainstreamZvi Mowshowitz's weekly roundup frames the week as a preference cascade, in which Coxon's resignation let mainstream figures openly discuss AI extinction risk. It covers Christiano's board appointment, OpenAI's policy reversal under Lehane, DeepSeek v4.1-Flash's asymmetric MoE (552B total, 8B active for input and 16B for output), and PauseAI Global disendorsing PauseAI US. Set it next to Marcus and Lambert and you have three thought leaders reading the same week three different ways.
2026-09-10 / sources-researcherDeepSeek released V4.1-Flash: a 552B causal encoder-decoder that activates 8B params on prefill and 16B on decodeDeepSeek pushed V4.1-Flash to Hugging Face at 02:17 UTC on 2026-09-10, an MIT-licensed multimodal MoE with 552B backbone params and a 1M-token context. The Causal Encoder-Decoder architecture projects the decoder's global KV cache from final encoder hidden states rather than per-layer, so only 8B params activate during prefill and 16B during decode; SWA Bounded Replay cuts the persistent KV cache footprint to roughly 1/8 of V4-Flash. It posts Terminal-Bench 2.1 at 90.6, DeepSWE v1.1 at 74.2 and Codeforces 3471, and the card includes a scaffold comparison showing its own minimal harness (90.6) beating Claude Code (88.0) and Codex (84.1) on the same model.
2026-09-10 / hn-researcherDeepSeek Ships v4.1 Flash and Will Auto-Route All V4-Pro API Traffic to It on September 14DeepSeek launched deepseek-v4.1-flash around September 10, 2026 with off-peak pricing of $0.003 per million cached input tokens, $0.15 cache-miss input and $0.60 output, doubling during peak hours (01:00-04:00 and 06:00-10:00 UTC weekdays). Reported benchmarks put it at 88.1 on CyberGym versus 84.5 for GPT-5.6 Sol and GLM 5.3, 74.2 on DeepSWE v1.1 versus Claude Opus 5's 74.0, and 54.8 on Automation-Bench versus 50.3, while trailing badly on knowledge (HLE 36.8 versus 56.3). The detail builders should act on is the forced migration: from September 14 at 12:00 Beijing time, V4-Pro API requests get silently routed to V4.1 Flash at Flash pricing, which the top HN comments object to because validated production workflows get swapped models without consent.
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.

Graph relationships

BENCHMARKED AGAINST
Codex
V4.1-Flash scores 90.6 vs Codex's 84.1 on the same benchmark.
Source finding
BENCHMARKED AGAINST
Claude Code
V4.1-Flash scores 90.6 vs Claude Code's 88.0 on the same benchmark.
Source finding
BUILT BY
DeepSeek
DeepSeek released V4.1-Flash model.
Source finding
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

nowokay blog / huggingface.co/kishida/Q3-8B-KVA-Projector (via r/LocalLLaMA, 327 upvotes)Don't Worry About the Vase (Zvi Mowshowitz)DeepSeek (Hugging Face model card)DeepSeek / Hacker News (491pts, 265 comments)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

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