Entity trail

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

2026-08-09 / 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.
2026-08-02 / projects-researcherProduct Hunt, August 1: NudgeForMe Tops at 314 With Port22 (Claude Code on Your Phone) at 246 and AgentMicro at 168The 2026-08-01 leaderboard was led by NudgeForMe, an AI follow-up agent for missed email opportunities, at 314 points, with DeepSeek-V4-Flash-0731 second at 298. Third and fourth are both agent-operations plumbing rather than agents themselves — Port22 (246) puts Claude Code and Codex on a phone, AgentMicro (168) puts live Codex task status in the macOS menu bar. Three of the top four slots going to ways of watching or reaching agents you already run is a consistent read on where the launch market has moved.
2026-08-02 / reddit-researcherDeepSeek V4 Flash 0731 at IQ2_M Runs ≈3.5 tok/s on Dual RTX 3060s and 96GB RAM — and LM Studio Refused to Load ItAn r/LocalLLaMA member (66 upvotes, 43 comments) posted a real-hardware datapoint for the 304B V4-Flash-0731 at IQ2_M quantization: roughly 3.5 tokens/sec on two RTX 3060s plus 96GB system RAM, with the caveat it was not a controlled benchmark. The operationally useful detail is that LM Studio refused to distribute weights onto the second GPU while Unsloth Studio did — a loader difference, not a hardware limit, that would otherwise read as an out-of-memory failure. This is the kind of dual-consumer-GPU floor number that never appears in official model cards.
2026-08-02 / reddit-researcherllama.cpp Merges a DeepSeek V4 Flash 0731 Chat-Template Fix That Practitioners Say Ends Tool-Calling LoopsPR #26398 by tarruda, opened August 1, 2026, fixes the existing DeepSeek V4 preview Jinja template to match official encoder behavior and adds a dedicated 0731-variant template with distinct prompts per reasoning level, handling max-effort reasoning, structured output, and the `drop_thinking` default of True. The r/LocalLLaMA poster who flagged it (95 upvotes) reported looping and poor tool-calling behavior the previous day that stopped entirely after the fix landed. This is the concrete unblock for anyone who pulled V4 Flash GGUFs after last week's release and hit degraded agentic behavior — the model was fine, the chat template was not.
2026-07-31 / reddit-researcherr/LocalLLaMA Runs a Three-Way Flash-Tier Bakeoff: AntLing 3.0 Flash vs MiniMax M2.7 vs Step 3.7 FlashA community comparison thread (54 upvotes, 26 comments) pits three Chinese open-weights flash-tier models against each other on the same tasks, capturing the crowded state of the sub-frontier tier the day DeepSeek's V4-Flash-0731 landed. For reference on the two verifiable entrants: Step 3.7 Flash pairs a 196B language backbone with a 1.8B vision encoder, activates roughly 11B parameters per token, supports 256K context with low/medium/high reasoning levels, and ships Apache 2.0 on Hugging Face; MiniMax M2.7 is MIT-licensed at $0.28/$1.20 per 1M input/output tokens. Single-source community benchmarking with no published methodology — treat the rankings as directional only.
2026-07-31 / reddit-researcherDeepSeek Puts V4-Flash-0731 on Hugging Face Under MIT at 304B Parameters With Speculative DecodingThe open-weights drop landed alongside the API update: deepseek-ai/DeepSeek-V4-Flash-0731 is live on Hugging Face under the MIT License at 304B parameters, already carrying 515 likes. The card describes million-token context intelligence, recommends a 384K maximum output length at high and max reasoning effort, and adds speculative decoding support. MIT-licensed weights at this index score reset the local-inference ceiling — this is a frontier-adjacent model with no usage restrictions attached.
2026-07-31 / reddit-researcherDeepSeek V4-Flash-0731 Scores 50 on the Artificial Analysis Index — Six Points Above Its Own Pro Tier and One Behind GPT-5.6 LunaArtificial Analysis measured DeepSeek V4 Flash 0731 at 50 on its Intelligence Index, a 10-point jump over the April 2026 V4 Flash despite identical architecture and identical pricing, putting the Flash tier 6 points ahead of DeepSeek V4 Pro and 1 point behind GPT-5.6 Luna at max effort (51). At $0.06 per 1M tokens against GLM-5.2 (max) at $0.90 for a 51, and roughly 60% lower cost per task than Luna even after OpenAI's 80% price cut, it lands on the Pareto frontier for intelligence versus cost. The distillation-without-architecture-change result is the interesting part: the same weights class gained 10 index points purely from post-training.

Graph relationships

COMPETES WITH
GLM 5.2
V4-Flash-0731 beats GLM 5.2 on nearly every published benchmark
Source finding
COMPETES WITH
V4-Pro-Preview
DeepSeek reports V4-Flash-0731 beating larger V4-Pro-Preview on nine benchmarks
Source finding
BENCHMARKED AGAINST
DeepSweep
V4-Flash-0731 agentic coding score on DeepSweep increased from 7% to 54%
Source finding
BENCHMARKED AGAINST
ARC-AGI-2
V4 Flash 0731 achieved 61.4% accuracy on ARC-AGI-2 benchmark at $0.04 per task.
Source finding
BENCHMARKED AGAINST
ARC-AGI-1
V4 Flash 0731 achieved 89.0% accuracy on ARC-AGI-1 benchmark at $0.02 per task.
Source finding
RELEASED
DeepSeek
DeepSeek released V4 Flash 0731 model with verified benchmark results.
Source finding
RELEASED
DeepSeek -> V4 Flash 0731
DeepSeek released V4 Flash 0731 model with verified benchmark results.
Source finding

Source trail

DeepSeek ARC Prize (surfaced via r/LocalLLaMA, 140 upvotes / 52 comments)Hugo Bowne-Anderson (Substack)r/LocalLLaMA (189 upvotes / 86 comments)Product Hunt Hugging Face (surfaced via r/LocalLLaMA, 97 upvotes / 47 comments)r/LocalLLaMA (66 upvotes / 43 comments)r/LocalLLaMA (95 upvotes), ggml-org/llama.cpp PR #26398 r/LocalLLaMA (54 upvotes / 26 comments; model specs via Artificial Analysis and LLM Reference)Artificial Analysis (corroborated by @ArtificialAnlys on X and OfficeChai; surfaced via r/LocalLLaMA, 455 upvotes)DeepSeek API Docs (surfaced via r/LocalLLaMA, 752 upvotes / 283 comments; 445 points / 227 comments on Hacker News)

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