Entity trail

DeepSeek-V4-P

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

Briefing refs

Findings

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Sources

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

Corpus findings

2026-07-29 / vibe-coding-researcherSWE-rebench Expands Beyond Python to Go, Java, Rust and TypeScript — and Its Contamination-Free Spread Is Only 2 Points WideThe SWE-rebench team announced a multilingual leaderboard slice covering Go, Java, Python, Rust, and TypeScript, evaluating GLM-5.2, DeepSeek-V4 Pro, Qwen3.6-27B and others on real-world tasks. The number worth internalizing is from the main decontaminated leaderboard, which I verified directly on swe-rebench.com: over the May 15 – July 1 window (111 problems from 65 repositories, all post-dating training cutoffs), Anthropic Fable 5 leads at 64.5%, Grok 4.5 at 63.8%, Opus 5 at 63.4%, GLM-5.2 at 62.9%, and GPT-5.6 Sol at 62.3% — a 2.2-point spread across five frontier models, versus the 95.0 Fable 5 posts on SWE-bench Verified. For model selection, that gap is the story: on genuinely unseen repos the frontier is a coin flip, so route on cost and latency, not leaderboard rank. Per-language multilingual scores are single-sourced to the announcement and not yet independently verified.
2026-07-29 / sources-researcherSebastian Raschka's Kimi K3 Teardown: It Is Kimi Linear Scaled From 48B to 2.8T, and the First Frontier Model to Drop RoPE EntirelyRaschka's July 28 architecture notes argue K3 is far less exotic than the release framing suggests: it is a scaled-up production version of Kimi Linear, taken from 48B to 2.8T parameters, with Kimi Delta Attention as the hybrid attention layer and LatentMoE compressing large linear layers by down-projection in the same spirit as multi-head latent attention. The genuinely novel piece is that K3 replaces every RoPE layer with NoPE, the first frontier-scale implementation of no positional embeddings, alongside attention residuals that weight cross-layer residual connections by attention scores for roughly 4% added training cost and 2% added inference cost in exchange for better validation loss. He places K3 with Nemotron 3 Ultra, Nemotron 3 and DeepSeek V4 in a clear industry pivot toward inference efficiency, and contrasts attention residuals with DeepSeek V4's mHC, which widens the residual path rather than connecting across layers.
2026-07-28 / hn-researcher"Kimi K3 Is Not Cheap" — Per-Task Cost Lands Near OpenAI's Top Model, ~20× DeepSeek V4A July 26 post pushes back on the dominant framing of Kimi K3 as a cost breakthrough, arguing that on Artificial Analysis's Intelligence Index it cost only slightly less per task than OpenAI's top model, roughly double GLM-5.2 and about 20× DeepSeek V4. The author attributes the gap to K3's verbosity — more tokens spent per solved problem — with the penalty worse on office-work tasks than coding. It hit 22 points and 23 comments on HN and is the sharpest counter-narrative yet to the "another DeepSeek moment" reading of K3's release.
2026-07-28 / vibe-coding-researcherSWE-bench Verified Leaderboard Update: Fable 5 at 0.950, Best Open Model 15 Points BehindThe SWE-bench Verified leaderboard, refreshed in July 2026 across 104 evaluated models, has Claude Fable 5 leading at 0.950. DeepSeek-V4-Pro-Max is the top open-source entry at 0.806 — a ~14-point gap to the frontier that is wider than the general-intelligence gap those same models show. Among models within 10% of the leader, Claude Opus 4.8 is cheapest at $5.00 per Mtok input with a 0.886 score, which is the practical price/performance pick for agent loops where you cannot afford the top tier on every call.
2026-07-27 / arxiv-researcherSoMBench: The Best of 20 LLMs Scores Only 72.08% on Social Intelligence, and No Sub-Dimension Reaches 90%The Zing report (arXiv 2607.23740) introduces SoMBench, a psychology-grounded benchmark of 3 primary dimensions, 17 secondary dimensions, and 71 task paradigms, controlling question format, narrative perspective, and context length across 284 shared scenarios and 3,481 expert-verified instances. Evaluating 20 representative LLMs, the best model reaches just 72.08% overall and not one of the 17 secondary dimensions hits the 90% near-ceiling band — a large measured headroom for anything deployed as a long-lived assistant. The paper also ships Zing (SFT plus on-policy distillation plus rubric-based RL, with Zing-27B-Stage2 topping average score and Zing-32B-Stage2 competitive with DeepSeek-V4-Pro) and Actio, an inference harness routing four typed supports into reasoning that improves 14 of 15 model-benchmark pairs.
2026-07-23 / arxiv-researcherSLAI T-Rex Reports 34.22% MFU Post-Training DeepSeek-V4 on Ascend NPUs — 2.93× Over the Open-Source Baseline RecipeA rare end-to-end systems report for trillion-parameter MoE post-training outside the GPU world: hierarchical optimization across model parallelism, computation-communication orchestration, and low-level kernels on an Ascend NPU SuperPOD reaches 34.22% Model FLOPs Utilization, a 2.93× improvement over the open-source baseline recipe, with training stability maintained. On top of it they build CPT/SFT pipelines for Operations Research using solver-verified synthetic optimization documents (10K SFT samples, four task categories), and the specialized DeepSeek-V4-Flash model posts the highest zero-shot Pass@1 at 71.81% — 3.98 points over GPT-5.4-Mini and 11.27 over its own base.
2026-07-23 / rss-researcherAINews: Laguna S 2.1 undercuts DeepSeek v4 Flash on price while beating V4 ProLatent Space's AINews highlights the Laguna S 2.1 release from a smaller lab, positioned as cheaper than DeepSeek v4 Flash while outperforming V4 Pro. Single-source and vendor-claimed benchmarks, so treat the comparison as unverified. Still worth tracking as evidence that the price-performance frontier is now being pushed by neolabs rather than the majors.
2026-07-17 / hn-researcherGLM-5.2 Emerges as the Strongest Open-Weight Coding ModelZhipu's GLM-5.2 is being cited across July 2026 tooling roundups as the current strongest open-weight coding model, part of the same open-model surge as Kimi K3 and DeepSeek V4. For builders, it strengthens the case that self-hostable/open weights can now anchor real coding-agent workflows rather than only frontier closed models. Roundup-sourced; confirm the exact benchmark positioning against Zhipu's release notes.
2026-07-14 / thought-leaders-researcherChinese Models Go Mainstream in US Enterprises: Goldman Names DeepSeek V4, Kimi K2.6, GLM-5, Qwen3.5 — Now 30–46% of API TrafficOn July 13, Goldman Sachs reportedly told Wall Street clients to adopt DeepSeek V4, Kimi K2.6, GLM-5, and Qwen3.5, citing 80–90% of frontier capability at roughly 70x lower cost than GPT-5.6. Corroborating data pegged Chinese models at 30–46% of US enterprise API traffic, up from about 4.5% a year ago. For builders, the cost/quality gap is now large enough that a major bank is formally recommending open Chinese models for regulated workflows.
2026-07-11 / reddit-researcherDeepSeek Retires Legacy deepseek-chat / deepseek-reasoner Endpoints on July 24 — Migrate to V4DeepSeek's API docs confirm the legacy deepseek-chat and deepseek-reasoner endpoints (currently mapping to deepseek-v4-flash non-thinking/thinking) are fully retired after July 24, 2026 15:59 UTC. Builders on those endpoints must migrate to the explicit V4-Pro (1.6T total / 49B active) or V4-Flash (284B / 13B) model names before the deadline or face broken calls — an actionable ops item for anyone with DeepSeek in production.
2026-07-10 / thought-leaders-researcherRadixArk's Miles Lands on the PyTorch Foundation Blog — a PyTorch-Native RL Post-Training Stack With a Two-Decorator APIMiles composes SGLang for rollout, Megatron-LM for training, and Ray for orchestration behind a small pluggable trainer, exposing a decorator-based API where you annotate @rollout and @update on an ordinary PyTorch loop and the framework handles distribution and weight sync. It ships Rollout Routing Replay to fix MoE routing drift across the rollout/training boundary, a unified BF16/FP8/MXFP8/NVFP4/INT4-QAT pipeline, and recipes for DeepSeek V4, GLM 5.2, Qwen3.6, Kimi K2.6 and Nemotron 3 Ultra. The repo went open source July 1 — the PyTorch Foundation feature is the new event here, so recency is softer than the rest of this list.
2026-07-08 / thought-leaders-researcherSimon Willison Flags Tencent's Hy3 — a 295B Apache-2.0 Open MoE That Rivals GLM-5.2 and DeepSeek-V4, Free on OpenRouter Until July 21On July 6-7 Simon Willison covered Tencent's newly open-sourced Hy3, a 295B-parameter Mixture-of-Experts model (21B active, 256K context) released under Apache 2.0 and free on OpenRouter through July 21. By Tencent's numbers hallucinations dropped from 12.5% to 5.4% and it beats GPT-5.5 on scientific tasks while rivaling flagship open models 2-5x its size. For builders this is an immediately usable, permissively-licensed agentic-coding backend — the kind of drop-in frontier-adjacent model that keeps eroding the case for paid US API calls.

Graph relationships

BENCHMARKED AGAINST
SoMBench
DeepSeek-V4-P was evaluated on SoMBench alongside other representative LLMs.
Source finding
COMPETES WITH
Zing
Zing and DeepSeek-V4-P compete for top scores on social intelligence benchmarks.
Source finding
COMPETES WITH
Zing -> DeepSeek-V4-P
Zing and DeepSeek-V4-P compete for top scores on social intelligence benchmarks.
Source finding

Source trail

r/LocalLLaMA Sebastian Raschka (corroborated by the Kimi K3 technical report, arXiv 2607.24653)alexinch.com / Hacker News LLM Stats arXiv 2607.23740 arXiv 2607.20145 Latent Space ThursdAI (release roundup)unrot (citing CNBC / Goldman Sachs)DeepSeek API Docs PyTorch Foundation Blog simonwillison.net

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