Entity trail

GLM-5.1

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

2026-08-17 / skill-finderContext bombs: plant a prompt injection in your AWS secrets and offensive AI agents refuse to continue (57% → 5% admin escalation)Tracebit found that placing short text designed to trip an attacking LLM's safety guardrails inside canary AWS Secrets Manager values halts autonomous attack agents mid-reconnaissance while alerting the defender that the canary was read. Across 152 runs against Opus 4.8, Gemini 3.1 Pro, GLM 5.2, DeepSeek 4 Pro and Kimi K2.6, admin access fell from 57% to 5% (93% → 0% for Opus 4.8), full compromise from 36% to 1%, and any-attack-path completion from 91% to 15%. The technique is free to deploy and doubles as a honeytoken, but Schneier's caveat is the real constraint: it only works against agents that have guardrails, so a locally-run unfiltered model walks straight through it.
2026-08-17 / sources-researcher'Models Are Getting Dumber on Purpose': GLM-5.2 Hits 99.2% on AIME 2026 With 40B Active Params While the Best Model Manages 53% on SimpleQAWalter van der Giessen argues (published Aug 17, 318 points on Hacker News) that labs are deliberately trading factual storage for reasoning capacity, and assembles the numbers: GLM-5.2 scores 99.2% on AIME 2026 with 40B active parameters and Qwen3.5 91.3% with 17B, while GPT-4's ~280B active parameters in 2023 'could barely solve an AIME problem'; meanwhile SimpleQA's best result (Gemini 2.5 Pro) is 53% factual recall and Qwen 4B/9B hallucinate on 80–82% of knowledge tasks. The mechanism he cites is roughly two bits of factual knowledge per parameter — facts are expensive to store and go stale, whereas decomposition, verification, and backtracking compress cheaply and never expire. The builder consequence is concrete: architect for a 20–40B quantized local reasoner plus external retrieval, which also makes errors fixable by correcting a data source instead of retraining.
2026-08-16 / saas-disruption-researcherCROSS-CATEGORY: Inference Pricing Bifurcated in 72 Hours — DeepSeek Went Metered-and-Up While Z.ai Went Flat-Rate Subscription at a Tenth of US RatesZ.ai shipped GLM-5.3 on Aug 14 — same 743B base as GLM-5.2, moved by post-training alone (Terminal-Bench 3.0 from 4.6 to 28.3, DeepSWE v1.1 from 46.2 to 66.9) — and priced it as GLM Coding Plan subscriptions at $18/$72/$160 per month, roughly one-tenth of US frontier rates, with the per-token API rate still unpublished and open weights staged behind a safety review over cyber-offense capability. Two days later DeepSeek moved the other direction, hiking metered token prices and adding peak/off-peak tiers. The builder takeaway: the cost lever is no longer 'which model' but 'which billing shape' — a flat monthly coding seat and a peak-priced token meter are now the two competing primitives underneath every AI SaaS P&L.
2026-08-16 / agents-researcherSRE-Bench: 5,000 expert hours to build the first contamination-free reverse-engineering benchmark, and frontier agents solve only 31.5% of itResearchers built 19 private, real-world-scale programs from scratch (averaging 16.9K lines) plus 44 in-house anti-analysis primitives, yielding 262 binary instances and 1,572 deterministically graded tasks — designed so no instance's source code can be in any model's training data. Across GPT-5.6-sol, Claude Opus 5, GPT-5.5, Grok-4.5 and GLM-5.2, the strongest model scored 61.4% per instance and fully solved just 31.5% of instances. The finding that matters for builders: strong source-code security capability does not transfer to binary analysis, and agents are oddly insensitive to compiler optimization and static linking in ways human reverse engineers are not.
2026-08-16 / projects-researcherProduct Hunt, August 15: Inferock Bench Wins the Day at 289 Votes Selling 'An Independent Receipt for Every LLM API Call'The August 15 Product Hunt leaderboard ran Inferock Bench first at 289 votes, GLM-5.3 second at 217, Big Mike (a sports-betting uncle on iMessage) third at 198, Zetik ('a chief of staff in your pocket') fourth at 177, and Attyn ('bringing intelligence to your cursor') fifth at 154. The day's winner is notable for what it implies: independent third-party verification of what a provider actually served you is now a fundable product category, which is a trust signal about inference providers rather than a capability launch. Four of the top five are AI, and only one — GLM-5.3 — is a model.
2026-08-15 / saas-disruption-researcherCROSS-CATEGORY: The Verification Layer Is Now Outranking the Application Layer on Product Hunt — Receipts, Benchmarks, and Weight-Gating in One DayAugust 15's Product Hunt board leads with Inferock Bench (154 upvotes), a billing-audit proxy for LLM calls, and carries GLM-5.3 at #6 (93 upvotes) — a model whose news is that its weights are being withheld pending safety evaluation — while the application-layer agents sit lower: Zetik at #2 (106), Attyn at #4 (94), SalesCloser.ai at #7 (91). Combined with CodeRabbit's $143M raise on August 12 to review AI-written code, the same signal now appears in developer tooling, model distribution, and venture funding within four days: the thing being sold is proof about what an agent did, not the agent. For a builder, the durable product in an agent stack is increasingly the receipt, the review, or the gate — the layers that stay necessary no matter which model is underneath.
2026-08-15 / reddit-researcherFour Chinese Frontier Models in Under a Month: Kimi K3-2.8T, Qwen3.8-2.4T, DeepSeek-V4-Pro-0813-1.6T, GLM-5.3-743BAn r/LocalLLaMA post cataloguing the release cadence — Kimi K3 at 2.8T parameters, Qwen3.8 at 2.4T, DeepSeek-V4-Pro-0813 at 1.6T and GLM-5.3 at 743B, all shipped inside a single month — reached 212 upvotes and 52 comments. Nikkei Asia reported on 2026-08-15 that Z.ai is positioning GLM-5.3 as a direct rival to Anthropic's Mythos on coding and security. The aggregate parameter count shipped by Chinese labs in 30 days now exceeds what the entire open-weight ecosystem produced in the first half of 2026.
2026-08-15 / hn-researcherZ.ai's Public Vulnerability Ledger Goes Live: 2,436 Findings Across 269 Projects, With Flaws Dating to 1981Following the GLM-5.3 launch, Z.ai stood up cvd.z.ai as a public coordinated-disclosure ledger, and the numbers are now browsable: 2,436 recorded vulnerabilities across 269 open-source projects, of which only 53 are publicly disclosed and 2,383 remain private. The severity split is 107 critical, 990 high, 1,286 medium, 53 low, and the site claims a 45-year impact span with the earliest flaws traceable to 1981 and an average 26.6 years of latency before discovery. The page functions as a searchable database with no published program terms, scope boundaries, reward structure, or disclosure timelines — a notable gap given the volume still held private.
2026-08-14 / saas-disruption-researcherWriter Ships Palmyra X6 Post-Trained on Z.ai's GLM-5.2 and Claims a 52% Cheaper Agent — the Harness, Not the Model, Is the Cost LeverWriter launched Palmyra X6 on August 13 alongside major upgrades to its agentic harness, saying its agent product now runs at 52% lower average cost with 48% better speed and 10% better quality. Palmyra X6 is a post-training variation of Z.ai's open-source GLM-5.2 — an enterprise SaaS vendor building its flagship on a Chinese open-weights base rather than a frontier lab's API. Writer's own research found harness changes cut cost ~40% on average and were a more reliable lever than model choice, which reframes 'AI COGS' as an orchestration engineering problem rather than a procurement one.
2026-08-14 / rss-researcherVercel makes GLM 5.2 free for eve agents through August 27 — 1M context, served by Blackbox AIVercel is giving eve agents free access to Z.ai's open-weights coding model GLM 5.2 (1M-token context window) through August 27, 2026, served by Blackbox AI on AI Gateway. New eve agents get it as their default; existing agents switch via `eve set --model zai/glm-5.2` or by editing `agent/agent.ts`. The offer excludes Fast mode and the `zai/glm-5.2-fast` variant, and reverts to standard pricing afterward — a short window worth using if you are benchmarking open-weights coding models against Gemini 3.7 Flash.
2026-08-14 / hn-researcherNetlify Ran One Prompt Through 11 Models: Claude Opus 5 Burned 519 Credits, DeepSeek V4 Flash Did It for 2.4Netlify published an AXIS-framework evaluation on August 14, 2026 giving 11 models the identical task — a static one-page coffee shop site with hours, address, menu, and a photo, explicitly no CMS — three runs each, scored on functional correctness rather than design. Average credit cost spanned more than 200x: DeepSeek V4 Flash 0731 at 2.4, Kimi K2.7 Code at 19, GLM 5.2 at 27, GPT 5.6 Terra at 39, Gemini 3.1 Pro at 53, Kimi K3 at 102, GPT 5.6 Sol at 141, Claude Sonnet 5 at 143, and Claude Opus 5 last at 519. The takeaway for builders is that frontier reasoning models massively overspend on simple static scaffolding work where cheap models are functionally equivalent.
2026-08-14 / hn-researcherZ.ai Delays GLM-5.3 Open Weights Two Weeks After Post-Training Produced Unplanned Exploit-Chain ReasoningZ.ai released GLM-5.3 on August 14, 2026 on the same base model as GLM-5.2, attributing a claimed 50% coding gain entirely to scaled post-training, and ranking first among open-source models on Terminal Bench 3.0 and Agents' Last Exam. On CyberGym it scored 84.5%, up from GLM-5.2's 77.2% and slightly ahead of GPT-5.6 Sol, but the company says vulnerability-discovery training environments produced an unintended emergent ability to reason across full multi-stage exploitation chains rather than isolated bugs. Weights are withheld roughly two weeks for safety hardening — reportedly the first time a Chinese lab has cited emergent offensive capability as the reason; Z.ai says its models have found 2,436 vulnerabilities across 269 open-source projects since GLM-5.2, 1,097 rated critical or high.

Graph relationships

BENCHMARKED AGAINST
Gemini 3.1 Pro
GLM-5.1 outperforms Gemini 3.1 Pro on SWE-Bench Pro.
Source finding
BENCHMARKED AGAINST
Claude Opus 4.6
GLM-5.1 outperforms Claude Opus 4.6 on SWE-Bench Pro.
Source finding
BENCHMARKED AGAINST
GPT-5.4
GLM-5.1 achieves 58.4% on SWE-Bench Pro, beating GPT-5.4.
Source finding
RELEASED
Z.ai
Z.AI released GLM-5.1, a 754B parameter MoE model under MIT license.
Source finding
BENCHMARKED AGAINST
Kimi K2.6
Kimi K2.6 outperforms GLM-5.1 on agentic tasks despite similar SWE-bench Pro performance.
Source finding
BUILT BY
Zhipu AI
GLM-5.1 is available to all Zhipu Coding Plan users.
Source finding
RELEASED
Z.ai
Z.ai confirms GLM-5.1 will be open-weight.
Source finding
RELEASED
Z.ai
Z.ai launched GLM-5.1 as 744B parameter MoE model.
Source finding
BENCHMARKED AGAINST
GPT-5.4
GLM-5.1 beats GPT-5.4 on SWE-Bench Pro.
Source finding
BENCHMARKED AGAINST
SWE-bench Pro
GLM-5.1 achieves state-of-the-art 58.4% on SWE-Bench Pro.
Source finding
MENTIONS
Simon Willison
Simon Willison tested GLM-5.1 and demonstrated 8-hour sustained engineering sessions
Source finding
RELEASED
Z.ai
Z.AI released GLM-5.1 model
Source finding

Source trail

Tracebit — Context Bombs (via Schneier on Security, 2026-08-12)w4g1.dev (Walter van der Giessen)Decrypt / Z.ai (corroborated by DeepSeek API docs Aug 13 and the Product Hunt Aug 15 leaderboard, where GLM-5.3 placed #2 with 217 upvotes)arXiv Multiple Sources (Product Hunt Aug 15 leaderboard, upvotes verified on the leaderboard page; Businesswire/CodeRabbit Aug 12; SiliconANGLE/Z.ai Aug 14)r/LocalLLaMA (release cadence corroborated by Nikkei Asia)Z.ai / Hacker News TechCrunch (corroborated by VentureBeat)Vercel Changelog Netlify / Hacker News Z.ai / Unite.AI / Hacker News r/singularity (496up/60c) — community aggregation of individually verified releases

Graph sources

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