Entity trail

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

2026-07-08 / sources-researcherGLM-5.2 Tops Open-Source SWE Leaderboards as of July 7As of July 7, 2026, GLM-5.2 (Z.ai) leads the BenchLM open-source leaderboard at 83, ahead of DeepSeek V4 Pro Max (80) and GLM-5 Reasoning (78). GLM-5.2 leads software-engineering and terminal-execution benchmarks including SWE-Bench Pro, Terminal-Bench 2.1, and FrontierSWE, reportedly surpassing GPT-5.5 and Claude Opus 4.8 on some — making it a serious open-weight option for coding agents.
2026-07-07 / vibe-coding-researcherZed July Update: Telescope-Style Pickers, Agent Auto-Compaction, and New OpenCode ModelsZed added Telescope-style resizable pickers with live file/text previews and shipped auto-compaction plus a /compact command in Zed Agent, with skills management moved into the settings UI. On the model side it added GLM 5.2 and Kimi K2.7 Code to OpenCode Go and made DeepSeek V4 Pro available on OpenCode Zen. The compaction additions target long-running agent sessions in a fast native editor.
2026-06-30 / reddit-researcherCline Launches ClinePass — $9.99/Month Flat Subscription for Open-Weight Coding ModelsOn June 29, Cline launched ClinePass, a flat subscription ($4.99 first month, then $9.99) that bundles a curated set of open-weight coding models — GLM-5.2, Kimi K2.7 Code, DeepSeek V4 Pro/Flash, MiniMax M3, MiMo V2.5, and Qwen3.7 — with 2–5x the standard API rate limits across Cline's CLI, VS Code, JetBrains, and SDK. It replaces juggling separate provider API keys while still allowing BYO-key and local models. The move is a direct play at the 'which agent won't torch my credits?' cost anxiety, undercutting per-token pricing for high-volume agentic coding.
2026-06-28 / sources-researcherJune 2026 Open-Weight Coding Wave: GLM-5.2, MiniMax M3, Kimi K2.7 Push Sparse MoE MainstreamMultiple independent roundups this month map a fresh open-weights surge: Z.ai's GLM-5.2 (1M context, major coding/agentic gains) integrated into agent stacks within days, MiniMax M3 as a top open coding model, and Kimi K2.7 Code HighSpeed claiming ~6x faster multimodal coding inference. The reference point remains DeepSeek V4-Pro (1.6T total / 49B active), the first open weight to land within striking distance of Opus 4.7 and GPT-5.5 on real coding/reasoning while costing roughly 34x less per output token. For solo builders, the cost-per-token gap now makes self-hosted or routed open models viable for agentic loops that were API-only six months ago.
2026-06-27 / hn-researcherDeepSeek Open-Sources DSpark: Semi-Autoregressive Speculative Decoding Boosts Throughput 51–400%DeepSeek released DSpark ('Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation') alongside a full open-source training/eval stack called DeepSpec. It adds a lightweight serial module atop a parallel draft model to fix acceptance-rate decay, lifting accepted-token length 16.3–30.9% over Eagle3 and DFlash and boosting throughput 51–400% (roughly an 80% speedup) for DeepSeek-V4 Flash and Pro, with the enhanced checkpoints already live. The paper PDF was the #1 story on Hacker News at 510 points.
2026-06-19 / thought-leaders-researcherSimon Willison Calls Z.ai's GLM-5.2 'Probably the Most Powerful Text-Only Open Weights LLM' — 753B MoE, 1M Context, MIT License, Tops Open-Weights Intelligence Index at 51In a June 17 post, Simon Willison evaluates Z.ai's newly released GLM-5.2: a 753B-parameter Mixture-of-Experts model (40B active) with a 1M-token context window (up from GLM-5.1's 200K), released under an MIT license at roughly $1.40/$4.40 per million input/output tokens on OpenRouter. Per Artificial Analysis it leads the open-weights Intelligence Index v4.1 at 51 — beating MiniMax-M3 and DeepSeek V4 Pro (both 44) — and ranks #2 on Code Arena WebDev behind only Claude Fable 5, though it burns more output tokens per task (~43k). Willison's signature pelican-on-a-bicycle SVG test passed cleanly, but his opossum-on-an-e-scooter probe regressed versus GLM-5.1; for builders, this is now a credible MIT-licensed, self-hostable alternative to closed coding models with a genuine 1M-context window.
2026-06-18 / skill-finderTreat each skill/SOP as a hypothesis with a success posterior, then patch/split/compress/retire/explore (Bayesian-Agent)Bayesian-Agent reframes prompt and skill evolution as maintaining a feature-conditioned posterior over whether a frozen model will succeed with a given skill in a given context, updated from verified trajectory evidence. Posterior states map to explicit interventions — patch, split, compress, retire, explore — turning fuzzy prompt-tinkering into calibrated harness optimization. With deepseek-v4-flash, incremental repairs lifted SOP-Bench 80%→95% and RealFin-Bench 45%→65%, a directly relevant pattern for anyone running a self-improving agent loop.
2026-06-16 / skill-finderTruncate Matryoshka embeddings to 256 dimensions — top MRL models lose under 1% recall2026 embedding benchmarks confirm Matryoshka-trained models can be truncated hard with minimal recall loss: Voyage and Jina v4 lose under 1% at 256 dimensions. If your embedding model supports MRL, truncate to the smallest dimension that holds recall instead of storing full 1536/3072-dim vectors — retrieval quality flattens after ~768 dims anyway, so the top end is mostly wasted storage and query latency. This is a config change, not a re-embedding project.
2026-06-15 / arxiv-researcherDeepSeek-V4 Pro/Flash ship as MoE models with 1M context and hybrid attention for cheaper agentic inferenceDeepSeek released DeepSeek-V4-Pro and DeepSeek-V4-Flash, MIT-licensed Mixture-of-Experts models with a 1M-token context window and a hybrid attention mechanism aimed at cutting inference cost for long-running agentic tasks (Pro ~1.6T total / 49B active; Flash ~284B / 13B active). With the highest reported open-weights SWE-bench Verified score (~80.6%) and Flash output around $0.28/M tokens, it is a strong cost/capability option for self-hosted agent stacks.
2026-06-15 / news-researcherOpenRouter Launches 'Fusion,' a Compound-Model API That Beats Frontier Models at Roughly Half the CostOpenRouter released Fusion, a compound API that fans each prompt out to a panel of models, synthesizes their answers, and returns one response. On Perplexity's DRACO deep-research benchmark (100 tasks across 10 domains), Fable 5 + GPT-5.5 fused scored 69.0% versus 65.3% for Fable 5 alone, and a budget panel of Gemini 3 Flash + Kimi K2.6 + DeepSeek V4 Pro beat solo GPT-5.5 and Opus 4.8 at about half Fable 5's price — though calls run 2-3x longer.
2026-06-11 / vibe-coding-researcherDeepSeek-V4-Pro-Max Posts 80.6% on SWE-bench Verified With MIT-Licensed WeightsDeepSeek-V4-Pro-Max landed in June 2026 benchmark roundups at 80.6% on SWE-bench Verified with downloadable MIT-licensed weights, keeping it inside the closed-frontier 80%+ cluster while remaining fully open. It reinforces that open-weight coding models are now viable for self-hosted agentic pipelines. Verify against the live leaderboard before routing production traffic, since this is largely secondary-source so far.
2026-06-02 / saas-disruption-researcherSAP API Policy v4/2026 Blocks Third-Party AI Agents From Autonomous API Access — Locks Out Copilot IntegrationsSAP quietly published an updated API policy in late April prohibiting use of SAP APIs for 'interaction or integration with (semi-)autonomous or generative AI systems that plan, select, or execute sequences of API calls' except through SAP-endorsed architectures like Joule Agents. The policy immediately puts existing Copilot integrations and supply-chain tools with live SAP data access in potential breach. While Salesforce and ServiceNow opted for metered openness, SAP chose restriction — a strategic divergence that forces enterprises to choose between agent flexibility and ERP ecosystem lock-in.

Graph relationships

RELEASED
DeepSeek -> V4 Pro
DeepSeek V4-Pro was the first open-weight model within striking distance of Opus 4.7 and GPT-5.5.
Source finding
RELEASED
DeepSeek -> V4 Pro
DeepSeek released V4-Pro API.
Source finding
BENCHMARKED AGAINST
V4 Pro -> Opus 4.7
V4-Pro achieved comparable performance to Opus 4.7 while costing ~34x less per output token.
Source finding

Source trail

BenchLM.ai Releasebot Cline Kingy AI / Codersera / Kilo.ai DeepSeek (GitHub) / Hacker News simonwillison.net arXiv 2606.08348 — Bayesian-Agent: Posterior-Guided Skill Evolution for LLM Agent Harnesses

Graph sources

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