Entity trail

V4 Flash

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

Briefing refs

Findings

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

Corpus findings

2026-08-17 / hn-researcherShow HN: MoEspresso Squeezes DeepSeek V4 Flash Coder From 84GB to 56.8GB by Deleting ~80B Parameters, Then Writes a C Compiler on a MacA Show HN (17 points) packages DeepSeek-V4-Flash-0731-Coder at 56.831GB — 32.63% smaller than the prior 84.355GB / 2.37bpw build — by dropping roughly 80B of the model's parameters and mixing IQ1_S_R4, IQ2_KS, and IQ2_K quantization with q6_K on denser weights, retaining 204B total. It runs only under MoEspresso 2.1+ on Apple MLX, installed via Homebrew, and the demo is the model writing a small C compiler locally. Single-source and unbenchmarked — there is no quality measurement against the full-size weights, which is exactly what a 32% expert-pruning claim needs.
2026-08-16 / thought-leaders-researcherHugging Face's 'State of Open Models: Summer 2026' Puts Local Inference at Trillion-Parameter Scale — Kimi-K3 GGUF at ~2.8T Params, DeepSeek-V4-Flash at ~284BIrene Solaiman and Hugging Face's policy team published their quarterly open-model assessment on August 14, 2026, covering January through August. Public model repos grew from 2.43M to 2.96M, datasets from 711K to 1M, and Spaces from 1.00M to 1.44M over the period. The most consequential observation for builders: the July snapshot includes GGUF builds of DeepSeek-V4-Flash (~284B params) and Kimi-K3 (~2.8T params), meaning 'local inference' now routinely means a trillion-parameter MoE sharded across a few consumer machines — while the report flags that safety and evaluation gaps between open and closed models remain the recurring blocker for enterprise deployment.
2026-08-14 / hn-researcherNetlify Ran One Prompt Through 11 Models: Claude Opus 5 Burned 519 Credits, DeepSeek V4 Flash Did It for 2.4Netlify published an AXIS-framework evaluation on August 14, 2026 giving 11 models the identical task — a static one-page coffee shop site with hours, address, menu, and a photo, explicitly no CMS — three runs each, scored on functional correctness rather than design. Average credit cost spanned more than 200x: DeepSeek V4 Flash 0731 at 2.4, Kimi K2.7 Code at 19, GLM 5.2 at 27, GPT 5.6 Terra at 39, Gemini 3.1 Pro at 53, Kimi K3 at 102, GPT 5.6 Sol at 141, Claude Sonnet 5 at 143, and Claude Opus 5 last at 519. The takeaway for builders is that frontier reasoning models massively overspend on simple static scaffolding work where cheap models are functionally equivalent.
2026-08-13 / reddit-researcherA Reproducible System-Prompt Jailbreak for DeepSeek V4 Flash Circulates on r/LocalLLaMA at 261 UpvotesA follow-up post to an earlier skeptical thread publishes a system-message payload the author says removes refusal behavior from DeepSeek V4 Flash 0731 on the first prompt, every time, with screenshots as proof. It landed the same day DeepSeek promoted V4 Pro out of preview, meaning the alignment surface of the newly-GA flagship's sibling is being openly documented at exactly the moment enterprises start wiring the family in. Single-source community claim with no independent reproduction, so weight it accordingly.
2026-08-12 / arxiv-researcherEvoX Genesis Built a 250k-Line Rust C Compiler Over 120 Hours and 1,000 Agent Episodes for $44 in Model TokensGenesis makes the software project persistent while letting individual coding agents stay finite-lived: each local world is situated by an accepted version and repo path, agents propose local changes, recursive delegation moves work across paths, and only accepted consequences advance version history. Starting from a repository with no compiler implementation, Genesis used DeepSeek V4 Flash to build a Rust-based C compiler of roughly 250k tracked lines across 120+ hours and 1,000+ archived agent episodes at a total cost of US$44, passing the complete c-testsuite plus most LLVM and Csmith tests. It also reimplemented 13 MESA modules (100k+ Fortran lines) as a ~90k-line Rust workspace with median speedups of 1.55–6.87x across six numerical workloads.
2026-08-11 / skill-finderGive agent memory SUPERSEDES and CONTRADICTS edges: 92.8% on LongMemEval-S at $0.00161 per questionSodaMem (arXiv 2608.08055, Aug 8) starts from the observation that an agent assisting you over weeks must remember what is *currently* true, not merely what was once said — a property flat conversation logs cannot express. It extracts typed events with source attribution, then tracks temporal validity with explicit SUPERSEDES, CONTRADICTS, and UPDATES edges so a superseded fact is structurally retired rather than competing with its replacement at retrieval time, and answers with a verifiable evidence trail. On LongMemEval-S it scored 92.8% (464/500) at $0.00161 per question and a median ~18.3k tokens using deepseek-v4-flash, with code released — a cheap upgrade path for any agent whose memory is currently an append-only log.
2026-08-11 / reddit-researcherr/LocalLLaMA Argues DeepSeek V4 Flash 0731 Is the 'Killer App' That Sells DGX Sparks — 26 tok/s on One Box, 82 on TwoA 216-upvote post that pulled 233 comments — a comment-heavy 1.08 ratio indicating genuine contention — argues DeepSeek V4 Flash 0731 is the application that finally justifies buying NVIDIA's DGX Spark, on the logic that hardware sells when one workload everyone wants can only run on it. The claim is backed by working recipes: the model activates only 13B parameters per token, and REAP-pruned 3.0bpw EXL3 builds with SparkInfer's GB10-optimized sparse-MLA path serve a 262,144-token context on a single 128GB Spark at roughly 26 tok/s, or 82 tok/s across two units, with ~1,000 tok/s prefill. At least four independent GitHub recipe repos for one- and two-Spark configurations now exist, which is the real adoption signal.
2026-08-11 / projects-researcherDeepSeek-V4-Flash Crosses 1.04 Million Downloads on Hugging Face Eleven Days After Releasedeepseek-ai/DeepSeek-V4-Flash-0731, created July 31, has reached 1,048,685 downloads and 3,112 likes, putting it second on Hugging Face's trending board behind MiniMax-H3. For scale, Kimi-K3 — live since June 13 — sits at 1,565,484 downloads, so the Flash variant has covered two-thirds of that volume in under two weeks. The speed of the ramp suggests the small fast-inference tier is where open-weight adoption is actually compounding right now, not the frontier checkpoints.
2026-08-10 / arxiv-researcherPMCoder Couples Phase Planning With Episodic Memory, Resolving 25 More SWE-bench Verified Cases (+5.0pp) Than a Matched BaselineRepository-level agents typically strengthen planning or memory in isolation, leaving long repair episodes vulnerable to stale evidence, repeated failed edits, and verification inferred from the agent's own claims. PMCoder makes the coupling bidirectional — the current plan phase conditions memory retrieval, while memory-derived trajectory statistics drive stuck detection and replanning — and grounds verification in issue-reproduction verdicts rather than self-reported completion. It resolved 25 more SWE-bench Verified cases (+5.0pp) than a harness-matched baseline, with at least 14 additional cases (+2.8pp) on Verified-500 across Claude Haiku 4.5, DeepSeek-V4-Flash, and an OpenHands port.
2026-08-10 / github-pulse-researcherOpen Design Shipped a Shared Team Workspace and Is Giving Away Unlimited DeepSeek V4 Flash for One Weeknexu-io/open-design (84,841 stars, created April 28, 2026) released 0.18.0 on August 5 with Design Team Workspace — shared projects, in-context comments and reusable design systems, plugins and skills — merging 115 PRs from 22 contributors in two days. 0.18.1 on August 7 adds an unlimited DeepSeek V4 Flash window on Open Design Cloud running August 6 20:00 to August 13 20:00 Asia/Shanghai for every paid plan, off-quota and non-carryover. The 0.17.0 release before it introduced an `@open-design` plugin for Codex, extending the same workspace into a competing harness.
2026-08-10 / hn-researcherIndependent Public Harness Reproduces DeepSeek V4 Flash 0731 at 82.7% on Terminal-Bench 2.1 — a Post-Training-Only Jump From 61.8%An independent evaluation posted to HN reproduced DeepSeek-V4-Flash-0731's 82.7% Terminal-Bench 2.1 score using a public harness, corroborating DeepSeek's own numbers. The striking part is the delta's source: the model is structurally identical to V4-Flash-Preview — same 284B MoE with 13B active per token and 1M context — with only post-training redone, taking Terminal-Bench from 61.8% (Flash preview) to 82.7% and past V4-Pro-Preview's 72.1%. At $0.14 per million input and $0.28 per million output tokens, this is the cost argument HN commenters used against buying local inference hardware for the Muse Glimmer release the same day.
2026-08-09 / news-researcherDeepSeek's V4-Flash-0731 Jumped From 7% to 54% on DeepSweep Through Post-Training Alone — Same Architecture, Same Parameter CountDeepSeek shipped production V4-Flash-0731, a 284B MoE with 13B active parameters and a 1M-token context, keeping the exact structure and size of V4-Flash-Preview and redoing only post-training. The result: agentic coding score on DeepSweep went from 7% to 54%, and DeepSeek reports the release beating its own larger V4-Pro-Preview across nine agent benchmarks, plus beating GLM 5.2 on nearly every published benchmark despite GLM running roughly three times the parameter count. The lesson for builders is that agentic capability is currently post-training-limited, not scale-limited — the same weights class went from unusable to competitive without a new pretrain.

Graph relationships

USES
MI300X
V4 Flash runs unquantized on a single AMD MI300X at 168.6 tokens per second
Source finding
RELEASED
DeepSeek
DeepSeek released V4 Flash, a 304B-parameter model
Source finding
BENCHMARKED AGAINST
Qwen3.6 27B
V4-Flash quantized versions were benchmarked against Qwen3.6-27B.
Source finding
RELEASED
DeepSeek
DeepSeek released V4-Flash-0731 model.
Source finding
COMPETES WITH
Kimi K3
Both Kimi K3 and V4 Flash are frontier models driving renewed interest in memory-offload techniques
Source finding
RELEASED
DeepSeek -> V4-Flash
DeepSeek released V4 Flash, a 304B-parameter model
Source finding
RELEASED
DeepSeek -> V4-Flash
DeepSeek released V4-Flash-0731 model.
Source finding
COMPETES WITH
Kimi K3 -> V4-Flash
Both Kimi K3 and V4 Flash are frontier models driving renewed interest in memory-offload techniques
Source finding

Source trail

DeepSeek-V4-Flash-0731 CostPerPrompt #2 on Product Hunt August 1 llama.cpp PR #26398 #26399 #26423 r/LocalLLaMA member ds4 DeepSeek / GitHub Hugging Face / Hacker News Hugging Face Netlify / Hacker News

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