Entity trail

DeepSeek V4-Flash-0731

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

Briefing refs

Findings

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Sources

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.
2026-08-14 / 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.
2026-08-11 / 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 / 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.
2026-08-08 / reddit-researcherDeepSeek V4 Flash 0731 Posts 89.0% on ARC-AGI-1 at 2 Cents a Task — Verified Semi-Private Scores Put It on the Cost-Efficiency FrontierARC Prize published verified semi-private results for DeepSeek V4 Flash 0731, tested July 31, 2026: 89.0% on ARC-AGI-1 at $0.02 per task and 61.4% on ARC-AGI-2 at $0.04 per task at max effort, with three reasoning variants scored (low effort still hits 84.0% / 46.0%). Artificial Analysis separately scored the model 50-52 on its Intelligence Index and called it the least expensive well-known model to run globally at roughly 3 cents per benchmark test. For builders the takeaway is the ARC-AGI-2 gap: cost doubles and accuracy falls 28 points versus ARC-AGI-1, so cheap high scores do not transfer to the harder set.
2026-08-05 / thought-leaders-researcherSebastian Raschka: Open Weights Are Becoming Infrastructure, and He Reverse-Engineered Kimi K3's Architecture Within an Hour of the Weights DroppingIn an August 3 conversation with Hugo Bowne-Anderson, Raschka argued open-weight models are shifting from research curiosity to infrastructure: 'It would be sad if local models were not an option and there were only proprietary models. It's good to have alternatives.' Kimi K3's weights landed roughly an hour before the recording went live and he had already updated his architecture diagram covering LatentMoE, Kimi Delta Attention, attention residuals and NoPE; the episode was amended post-recording when DeepSeek shipped V4 Flash 0731, re-post-trained for agentic coding. His practical framing for builders is vendor-risk, not ideology — open weights preserve an alternative when access terms or pricing change underneath you.
2026-08-04 / reddit-researcherPractitioner Report: DeepSeek V4-Flash-0731 Falls Apart Under Quantization in a Way the Preview Build Did NotAn r/LocalLLaMA weekend writeup (189 upvotes, 86 comments) reports that quantization 'hits this thing like a truck' — Q2 and Q3 weights of V4-Flash-0731 behave like a different model, reasoning traces change shape, and results land a full tier below the officially served version. The practical thresholds given: Q3 finally replaces Qwen3.6-27B and pulls ahead in large repos and Claude Code-style harnesses with 30k+ token system prompts, while Q2 loses outright to Qwen3.6-27B at Q8. A commenter posted Unsloth KL-divergence charts showing the earlier DS4 Flash preview was forgiving of quantization while 0731 is not, with poor KLD even at IQ4_XS — and the poster flags the model is weak on general knowledge, fine if you're tool-calling but not for airgapped use.
2026-08-02 / saas-disruption-researcherDeepSeek-V4-Flash-0731 Took #2 on Product Hunt With the Pitch "Frontier Agent Intelligence at Flash Prices"The 0731 release hit #2 on Product Hunt's August 1 leaderboard with 299 upvotes under API/Open Source/AI, and is simultaneously the top trending item across HN and r/LocalLLaMA, where operators are posting real throughput numbers (IQ2_M quant on dual 3060s + 96GB RAM benchmarking around 3.5 tok/s). Reported specs are 284B parameters with 13B active and roughly 60% below proprietary pricing, with a claimed six-fold measured improvement in agent ability over the prior Flash tier. For anyone pricing an AI feature, this is the floor moving again: the cost basis a SaaS vendor budgeted for agent inference last quarter is now the premium option.
2026-08-02 / sources-researcherDeepSeek-V4-Flash-0731 Ships MIT-Licensed at 304B Params, Beats DeepSeek-V4-Pro, and Costs $0.14 per Million Input TokensDeepSeek released V4-Flash-0731 on July 31 under the MIT license with a DSpark speculative-decoding module attached, scoring Terminal Bench 2.1 82.7, Toolathlon-Verified 70.3, DSBench-FullStack 68.7, DeepSWE 54.4 and NL2Repo 54.2 — the model card states it outperforms DeepSeek-V4-Pro (Preview) 'despite its far smaller activated parameter count.' The accompanying technical report is titled 'Towards Highly Efficient Million-Token Context Intelligence,' and API pricing lands at $0.14/$0.28 per million tokens with a 98% cache discount. The combination that matters for builders: frontier-adjacent agentic scores, a million-token window, open weights you can actually redistribute commercially, and a price roughly two orders of magnitude below flagship closed models.

Graph relationships

RELEASED
DeepSeek
DeepSeek released DeepSeek-V4-Flash-0731 model
Source finding
BENCHMARKED AGAINST
Terminal-Bench 2.1
DeepSeek-V4-Flash-0731 scored 82.7 on Terminal Bench 2.1.
Source finding
RELEASED
DeepSeek
DeepSeek released V4-Flash-0731 under MIT license on July 31 with 304B parameters.
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BENCHMARKED AGAINST
GLM 5.2
DeepSeek V4-Flash-0731 costs 60% less per task than GLM-5.2 at comparable intelligence.
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BENCHMARKED AGAINST
GPT-5.6 Luna
DeepSeek V4-Flash-0731 scored 50 on Artificial Analysis Intelligence Index, one point behind GPT-5.6 Luna at roughly one-third the cost.
Source finding
RELEASED
DeepSeek
DeepSeek released DeepSeek V4-Flash-0731 as open weights under MIT license on Hugging Face.
Source finding
RELEASED
DeepSeek -> DeepSeek V4-Flash-0731
DeepSeek released DeepSeek-V4-Flash-0731 model
Source finding
RELEASED
DeepSeek -> DeepSeek V4-Flash-0731
DeepSeek released V4-Flash-0731 under MIT license on July 31 with 304B parameters.
Source finding
RELEASED
DeepSeek -> DeepSeek V4-Flash-0731
DeepSeek released DeepSeek V4-Flash-0731 as open weights under MIT license on Hugging Face.
Source finding

Source trail

Hugging Face / Hacker News Netlify / Hacker News r/LocalLLaMA (261up/85c) — single unverified source r/LocalLLaMA (216up/233c), corroborated by GitHub recipe repos and Local Inference Lab SparkInfer docs Hugging Face (surfaced via r/LocalLLaMA, 97 upvotes / 47 comments)Antigma Eval / Hacker News DeepSeek ARC Prize (surfaced via r/LocalLLaMA, 140 upvotes / 52 comments)Hugo Bowne-Anderson (Substack)r/LocalLLaMA (189 upvotes / 86 comments)Product Hunt r/LocalLLaMA (66 upvotes / 43 comments)

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