The Hacker News thread (417 points, 217 comments) reproduces DeepSeek's official announcement text: V4.1 Flash is targeted for official release around 2026-09-10 Beijing Time, has "comprehensively surpassed" V4 Pro across performance, cost, speed, and task completion time, and will see all `deepseek-v4-pro` requests ro Beyond the release quotes, the thread collects independent user reports of pathologies in the prior V4 Flash web build (language-following drift, thinking chains in the wrong language) and coding-harness issues such as infinite loops, hallucinations, premature termination, and invalid tool calls. These are anecdotal co

eesel AI's piece confirms the 2026-09-10 release and quotes DeepSeek's framing that V4.1 Flash is "the smallest model in our new architecture family, with native visual understanding," then lays out the consolidation: V4 Flash, V4 Flash Vision Exp, and eventually V4 Pro all collapse onto V4.1 Flash. It highlights the 2 The post is otherwise a vendor-flavored summary aimed at customer-support automation buyers, with thinner first-party technical detail than DeepSeek's change log. It treats architecture, benchmarks, and pricing at a higher level and adds eesel's product framing rather than independent measurements of V4.1 Flash itself.

BenchLM's tracker page lists DeepSeek V4.1 Flash as released 2026-09-10 with 1M context, open weights, reasoning support, and 22 sourced benchmark rows covering agentic (8/8 verified), coding (6/6), knowledge (3/3), math (1/1), and multimodal (3/3) categories, with reasoning, multilingual, and instruction-following sti The page also surfaces coverage gaps and decision caveats: no public comparative rank yet because eligible evidence is thin, independent runtime speed not measured, time-to-first-token not measured, maximum output length tracked separately, and knowledge cutoff not sourced. Maximum output length is explicitly flagged a

Coursiv's comparison table attributes to DeepSeek's official model card a generational shift from V4 Flash to V4.1 Flash: backbone parameters roughly double from 284B to 552B, active parameters per token move from 13B to 8B on input and 16B on output, the architecture changes from a MoE decoder to a causal encoder-deco The post also reports agentic benchmark jumps DeepSeek attributes to the model card (Terminal-Bench 2.1 82.7 → 90.6, Terminal-Bench 4.0 7.0 → 31.2, DeepSWE v1.1 54.4 → 74.2), a KV cache roughly four times larger per token on V4 Flash than on V4.1 Flash (890 bytes), and an effective price drop of about 77% on cache-miss

Requesty's analysis treats the V4.1 Flash launch as both a model release and a lifecycle event, flagging that from 2026-09-14 the pinned `deepseek-v4-pro` model ID will silently serve a different model. It reproduces architectural claims from DeepSeek: a 552B-parameter MoE backbone, an asymmetric causal encoder-decoder The piece adds community context: launch-tweet engagement (~21,110 likes, ~2.78M impressions), a r/LocalLLaMA reading of the safetensors putting on-disk weight nearer ~748B (552B main plus ~197B engram and ~14B speculative decoding weights), and pricing reference points of $0.30 input / $1.20 output per million tokens.

DeepSeek's own API change log dated 2026-09-10 announces DeepSeek-V4.1-Flash as the smallest model in a new architecture family with native multimodal visual understanding, and documents a full benchmark block including GPQA Diamond 90.9, HLE 36.8 (39.1 on the pure-text subset), Codeforces 3471, Terminal-Bench 2.1 90.6 The same entry formalizes the V4 Pro lifecycle change: after 12:00 Beijing Time on 2026-09-14, all requests to `deepseek-v4-pro` will be routed to V4.1 Flash and billed at Flash pricing until a future V4.1 Pro ships, on DeepSeek's claim that V4.1 Flash has surpassed V4 Pro across performance, cost, speed, and total tim