Entity trail

V4-Pro-Preview

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

Briefing refs

Findings

Edges

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.
2026-08-04 / 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 / 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.
2026-07-31 / reddit-researcherDeepSeek Ships V4-Flash-0731 With Native Responses API Support and Codex Adaptation, Says V4-Pro 'Will Follow Soon'DeepSeek updated its Flash-tier API on July 31 with substantially rebuilt agent capabilities: Terminal Bench 2.1 82.7, NL2Repo 54.2, Cybergym 76.7, DeepSWE 54.4, Toolathlon verified 70.3, DSBench-FullStack 68.7 and DSBench-Hard 59.6 — figures the changelog says now far exceed the V4-Pro-Preview. The model natively supports the Responses API format and is specifically adapted for Codex, and the changelog notes only the Flash API changed; the V4-Pro API and the APP/WEB models are unchanged, with the official V4-Pro release to follow. For builders, a Flash-tier model that speaks Responses natively is a near drop-in swap inside existing OpenAI-shaped agent harnesses.
2026-05-08 / sources-researcherDeepSeek V4 Preview Open-Sourced: 1.6T Total / 49B Active Params, 1M Context, Flash + Pro VariantsDeepSeek released V4 Preview on April 24 with two variants: V4-Pro (1.6T total / 49B active, rivaling top closed-source models) and V4-Flash (284B total / 13B active, fast and economical). Both support 1M context and dual thinking/non-thinking modes. V4 beats all current open models in math/STEM/coding. Available via API (deepseek-v4-flash, deepseek-v4-pro). Still in preview phase with no finalization timeline per Reuters. Not previously covered in this newsletter despite being the largest open-source model release of the quarter.
2026-05-06 / vibe-coding-researcherLLM Debate Benchmark Update: Adversarial Multi-Turn Evaluation Across 683 Motions Now Includes GPT-5.5, DeepSeek V4 Pro, Qwen 3.6A community benchmark using adversarial, multi-turn debates across 683 curated motions added 9 new models including GPT-5.5, Grok 4.3, DeepSeek V4 Pro, GLM-5.1, Kimi K2.6, and Qwen 3.6 Max Preview. Each model pair debates the same motion twice with sides swapped, using Bradley-Terry ratings. This evaluation surface catches reasoning and argumentation capabilities that standard benchmarks miss — useful for selecting models for agent tasks requiring sustained multi-turn reasoning rather than single-shot code generation.

Graph relationships

COMPETES WITH
V4 Flash 0731
DeepSeek reports V4-Flash-0731 beating larger V4-Pro-Preview on nine benchmarks
Source finding
COMPETES WITH
V4 Flash 0731 -> V4-Pro-Preview
DeepSeek reports V4-Flash-0731 beating larger V4-Pro-Preview on nine benchmarks
Source finding

Source trail

DeepSeek r/LocalLLaMA (189 upvotes / 86 comments)DeepSeek (HuggingFace model card), corroborated by Simon Willison and Latent Space AINews DeepSeek API Docs (surfaced via r/LocalLLaMA, 752 upvotes / 283 comments; 445 points / 227 comments on Hacker News)DeepSeek r/singularity

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