Entity trail

DeepSeek V4 Pro

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

Briefing refs

Findings

Edges

Sources

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

Corpus findings

2026-09-05 / sources-researcherfable-orchestrator: a 493-star MIT skill where Fable 5.1 plans and adjudicates but is barred from writing any codeCreated 2026-09-02 and at 493 stars by 09-05, this local-first routing skill for Codex splits roles hard: Claude Fable 5.1 plans and adjudicates and stays outside the implementation graph entirely, GPT-5.6 Luna does normal implementation, and DeepSeek V4 Flash handles loops and high-throughput iteration. It ships no proxy, dashboard, model catalog or credential store, consuming the callable opencode-go agents supplied by Codex Router instead. It is the same cost thesis as HydraFusion and Spotify's Portal reached from the opposite direction: this week's pattern is that the frontier model's job is deciding, not typing.
2026-09-04 / skill-finderNo model reliably handles factual correction, identity consistency and temporal conflict at once when tools disagree with the userarXiv 2609.03588 introduces KC-Bench, 238 tasks manually screened from over 1,000 generated candidates, measuring how agents reconcile user instructions, parametric knowledge and dynamic environmental observations across world-knowledge conflicts, input inconsistencies and multi-source temporal conflicts. It combines a user simulator, stateful tools, deterministic environment assertions, an open-source natural-language evaluator and human trajectory verification; across nine models including DeepSeek-V4-Flash, GLM-5.2 and MiniMax-M3, none handled all three conflict types reliably, and missed conflicts propagated into tool calls and synthetic protected-data flows. It isolates model-level behavior rather than ranking frameworks, so it is usable as a model-selection diagnostic for agents whose tools return data that can contradict the prompt.
2026-09-03 / arxiv-researcherPutting the Reasoning Trace Before the Context Instead of After Lifts GraphWalks Exact Match From 29% to 82%Trace as State formalizes a mismatch in causal transformers: long-context reasoning often depends on task state discovered only later, and for causal state-update processors, giving the condition first can require exponentially less memory in the worst case than giving it last. The method reuses collected reasoning traces as a textual proxy for task state and places them before the long-context block on a fresh pass. Against Trace Append, a matched control placing the identical trace after the context, it won in 26 of 27 model/task/metric combinations; on GraphWalks Parents exact match went from 29.2% (initial pass) and 43.0% (Trace Append) to 81.8% for DeepSeek V4 Pro Preview, and from 66.4% and 83.2% to 100.0% for GLM-5.2.
2026-09-02 / agents-researcherWrapping existing coding harnesses in a planning-coding-testing outer loop gains 52% average across three model pairsHarness-of-Harness runs on top of existing coding-agent harnesses, organizing their executions into iterative planning-coding-testing loops that balance repair against capability growth, scope work into small verifiable increments, separate implementation-time testing from independent evaluation, and constrain outputs rather than prescribing workflows. Across GameCraft-Bench, FrontierSWE and ProgramBench with Codex+GPT-5.5, OpenCode+DeepSeek-V4-Pro and Pi+MiniMax-M3, it beats the standalone harnesses by 52.25% on average and 82.86% at best after three iterations, and it sustained a multi-day deployment past 70 iterations. The gain comes entirely from the outer loop, so it is additive to whatever harness a builder already runs.
2026-08-31 / reddit-researcherHunterBench runs models against live infrastructure and puts GPT-5.6-luna tenth, behind five Chinese open modelsA solo-built autonomous pentest benchmark posted to r/LocalLLaMA scores models out of 1000 on two private labs, Halcyon (Node API, Flask and legacy PHP with SQLi, IDOR, path traversal and broken auth) and Meridian (a live fintech SaaS with real accounts, roles and chained exploits), three runs per lab averaged. Z.ai glm-5.3 leads at 415, followed by glm-5.3-flash and glm-5.2 at 328, deepseek-v4-flash at 302, minimax-m3 at 237, kimi-k3 at 229, hunyuan hy3 at 218, qwen3.8-27b at 180, and gpt-5.6-luna at 86. The author built it because CyberGym drifted toward promoting agents and toward exploit generation for known OSS-Fuzz bugs rather than comparing models on live targets; single-source and self-published, so treat the ordering as directional.
2026-08-31 / reddit-researcherDeepSeek put a V4-Flash-Vision-Exp repo on Hugging Face, but it ships inference code and a tokenizer, not weightsThe 228-upvote r/LocalLLaMA post reads as an open-weights drop, and it is not one: deepseek-ai/DeepSeek-V4-Flash-Vision-Exp contains the tokenizer, a prompt-encoding reference and a minimal PyTorch implementation covering the vision encoder and aligner, DFlash attention, MoE, Hyper-Connections and the DSpark forward path. The model itself is a 284B-parameter MoE (twenty 13B experts) that has been API-only since August 21 at the same price as V4-Flash, $0.14 per 1M cache-miss input and $0.28 per 1M output, with images billed at up to 384 tokens each and no per-image fee. For builders this is an architecture reference you can read, not a model you can run.
2026-08-31 / sources-researcherTrustMeBro shows PATH shims are enough to feed coding agents fake tool output and move them past their own guardrailsThis Go tool, created 2026-08-26 and now at 327 stars, intercepts command-line tools invoked by Codex, Claude Code and pi through PATH shims alone, with no plugin, hook or MCP integration required, and rules decide whether to fabricate output, rewrite real stdout while preserving stderr and exit status, block the call, or exec the real binary. The published evaluation is the point: each model had to verify a fresh DNS TXT authorization marker before running a scan, and GPT-5.6 Sol, GPT-5.5, DeepSeek V4 Pro and DeepSeek V4 Flash all correctly refused with the real dig, then all four proceeded once TrustMeBro returned fabricated proof. Framed for controlled red-team testing, and a reminder that any agent decision gated on shell output is only as trustworthy as PATH.
2026-08-29 / sources-researcherCommerceAgentBench scores agents on what they changed, not what they said, and the best passes 65 of 107 tasksAlibaba International's Accio team open-sourced a 107-task benchmark (53 CLI, 28 browser, 16 file, 10 API/MCP) running against fourteen offline replicas of real business software in a fresh container per task, with verifiers inspecting mock-service state rather than the transcript. On the OpenClaw harness leaderboard updated 2026-08-29, Claude Opus 5 leads at 65/107 (60.7%) using 52.5 steps, 7.8 minutes and 2.05M tokens per task, ahead of Opus 4.8 at 56/107 and Qwen 3.8 Max and DeepSeek V4 Pro tied at 53/107. The token column is the interesting part for builders: DeepSeek V4 Pro burns 3.58M tokens to reach the same pass rate GPT-5.6 Sol nearly matches on 1.15M.
2026-08-29 / sources-researcherOCaml maintainer sees traversal probes ten minutes after opening a fix PR, and rclone logs 40 disclosures in a monthAnil Madhavapeddy, a Cambridge CS professor and OCaml compiler maintainer, released a cohttp 6.3.0 path-traversal fix and found percent-encoded traversal probes hitting his live webserver about ten minutes after opening the public PR; he reproduced the exploit locally with his own agent in under a minute after Claude Fable refused the task and DeepSeek V4 Pro complied. In the Hacker News discussion on 2026-08-28, rclone maintainer Nick Craig-Wood reported over 40 security disclosures in the last month against roughly 20 in the project's first ten years, with about 75% containing something real, and GitHub CVE assignment slipping from 2-3 days to 3-4 weeks so releases now ship marked CVE-PENDING. His argument is that embargo-based open source security process no longer buys any time.
2026-08-28 / vibe-coding-researcherTip: DeepSeek Now Charges Half Price Outside Two Weekday Windows, So Schedule Agent Batches Around the ClockAs of 2026-08-16 DeepSeek bills peak rates only during 01:00-04:00 and 06:00-10:00 UTC Monday to Friday, with off-peak set at exactly 50% of peak for the other roughly 79% of the week including all weekend. V4-Pro runs $1.32/$3.96 per million tokens peak against $0.66/$1.98 off-peak; V4-Flash is $0.44/$1.32 against $0.22/$0.66. Latency is identical, so for any non-interactive agent run this is a free halving: move a nightly pipeline from 09:00 UTC to 11:00 UTC and the bill drops by half.
2026-08-28 / vibe-coding-researcherZed's Inline Assistant Did Nothing When the Model Emitted Reasoning Before Its Tool CallZed v1.18.0-pre (2026-08-26) fixes the inline assistant silently no-opping when a model produced reasoning tokens ahead of its tool call, a failure mode that gets more common as reasoning-by-default models spread. The same release adds GPT-5.6's 1M-token context window on Amazon Bedrock and Gemini 3.5 Flash-Lite to the Google AI models. The preceding v1.17.2 added low reasoning effort support for DeepSeek V4 Flash and V4 Pro.
2026-08-27 / arxiv-researcherXRepoTest Benchmarks Repository-Level Unit Test Generation in Rust, Go, Julia, PHP and RubyLe Quang et al. target five languages that test-generation benchmarks largely skip, evaluating 14 models including Claude 4.5, GPT-5.2, DeepSeek V4-Pro, and the Qwen families under realistic repository constraints in a containerized execution framework with file-level, LSP-based, and retrieval-based context strategies. Beyond pass rate and coverage they add Invocation Rate, which checks whether a generated test actually exercises the function it targets rather than passing vacuously. Results show a substantial gap between standalone and repository-level performance and a trade-off where richer context does not monotonically improve test reliability. Dataset and code are public.

Graph relationships

BENCHMARKED AGAINST
CommerceAgentBench
DeepSeek V4 Pro was evaluated on CommerceAgentBench at 53/107 tasks.
Source finding
SUPPORTS
Zed
Zed added low reasoning effort support for DeepSeek V4 Pro.
Source finding
BENCHMARKED AGAINST
Gemini 3.5 Flash
DeepSeek V4 Pro and Gemini 3.5 Flash were evaluated in ReguSim financial compliance environment.
Source finding
RELEASED
DeepSeek
DeepSeek released DeepSeek V4-Pro with price increases of 50% to 1,100%.
Source finding
RELEASED
DeepSeek
DeepSeek released V4 Pro with three configurable reasoning levels
Source finding
BENCHMARKED AGAINST
DeepSWE
DeepSeek V4 Pro improved DeepSWE score from 12.8 to 62.7 versus the preview.
Source finding
RELEASED
DeepSeek
DeepSeek shipped the production build of V4 Pro to OpenRouter on August 12.
Source finding
USES
AI Gateway
Vercel's AI Gateway defaults to deepseek-v4-pro under the same model ID.
Source finding
RELEASED
DeepSeek
DeepSeek switched its API endpoint to deepseek-v4-pro 0813 build on August 12, 2026.
Source finding
SUPPORTS
ds4
ds4 supports running DeepSeek V4 PRO.
Source finding
RELEASED
DeepSeek
DeepSeek released V4-Pro with 1.6T parameters.
Source finding
COMPETES WITH
GPT-5.5
DeepSeek V4-Pro price is 8x lower than GPT-5.5
Source finding

Source trail

TrustMeBro Together AI calv.info Accio anil.recoil.org GitHub r/LocalLLaMA claiming 1.5x to 1.9x more throughput per kilowatt companion OpenAI post Apple's Mac refresh and Jalapeño next to each other 512GB of unified memory at 1.2TB/s left this week

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