Entity trail

GLM-5.3

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

2026-08-21 / rss-researcherOrnith-1.5 ships 397B, 35B and 9B open weights under MIT and lands within a point of Claude Opus 4.8 on Terminal-BenchOrnith released Ornith-1.5 on 19 August 2026 as three MIT-licensed models: a 397B MoE, a 35B MoE, and a 9B dense with a quantized Mobile build that runs on iPhone and Android. The 397B scores 86.1 on Terminal-Bench 2.1 (Terminus-2) against Claude Opus 4.8's 85.0, 86.0 on SWE-bench Verified, and 92.8 on GPQA Diamond, but trails badly on the harder agentic rows (13.5 vs 21.1 on Frontier-Bench v0.1, 59.5 vs 69.7 on NL2Repo). All numbers are vendor-reported and averaged over five runs; weights for every size are already downloadable, unlike GLM-5.3 which is still API-only.
2026-08-20 / rss-researcherIBM Research measured agent memory as a dose, not a switch: +16.1pp for weak models, 0.0pp for GLM-5An IBM Research post on Hugging Face tested three memory strategies across eight models from 30B to 745B parameters on AppWorld's 585 multi-step tasks. Curated retrieval gave gpt-oss-120b +16.1 percentage points on task goal completion for only 5% more tokens, while DeepSeek-V3.2 needed the full guideline set for +9.5pp at a 78% token cost, and GLM-5 gained nothing at all. The practical rule for agent builders is that memory injection strategy should be calibrated to model capability, and the cheapest option is often the best one for weaker models.
2026-08-20 / thought-leaders-researcherZ.ai CEO Jie Tang says GLM 5.3 is the same base model as 5.2 plus one month of long-horizon RL, and calls parameter count meaningless on its ownIn a Latent Space AINews piece published August 20, Jie Tang lays out five scaling knobs and argues "parameter count is only meaningful alongside three others — how much data you have, where you intend to spend your compute, and who will run the model, under what conditions." His claim about GLM 5.3 is the concrete part: no architecture change from 5.2, roughly a month of extra reinforcement learning on multi-step environments modeled on tasks that would take an experienced engineer several days, including infra diagnostics with live access to clusters, docs and codebases. Tang's point for builders is that capabilities like vulnerability discovery need 20+ step reasoning chains, not more memorized weights, which is why a mid-size open model keeps closing on frontier scores.
2026-08-18 / thought-leaders-researcherGuillermo Rauch: Vercel Ran Cybersecurity Evals on GLM 5.3 and Calls It 'the New Open Frontier' — Cheap Enough to Scan 3× More OftenRauch posted on Aug 16 (1,099 likes) that Vercel evaluated GLM 5.3's cybersecurity capabilities and considers it the new open-weights frontier, arguing the lower cost is 'a boon for defensive security work' because it lets teams run scans at least 3× more frequently. Replies immediately pushed the obvious counter-arguments — that the same capability profile serves offensive use, especially self-hosted, and that a Chinese open model for defensive security is a questionable trade for cost savings. The concrete builder takeaway is a cost-per-scan argument, not a capability-ceiling one.
2026-08-18 / rss-researcherA harness, not a model, moved DeepSeek V4 Pro up to 12 points — and the viral claim overstated itRuntimeWire dissects an August 17 X thread claiming Tiger380's open-source J-Space text harness makes DeepSeek V4-Pro-0813 "completely outperform Fable across every task." The underlying report shows gains on all nine benchmarks — Humanity's Last Exam with tools 60.0→67.7, NL2Repo 61.5→73.4, DeepSWE 62.7→72.0, Terminal Bench 2.1 87.9→90.1, CyberGym 83.3→86.8, Toolathlon-Verified 74.1→79.5 — but Fable 5 still leads HLE-without-tools at 53.3 vs 48.0 and GLM-5.3 leads AutomationBench 48.2 vs 38.2. Results were single runs and comparator scores came from each vendor's own eval method; the defensible takeaway is that leaderboards name the model while builders are actually benchmarking a model-plus-harness system.
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 / news-researcherOpenAI and Anthropic Are Now in an Open Price War as Chinese Models Undercut Them by 60-90%Ars Technica, following FT reporting on August 14, 2026, documents OpenAI cutting GPT-5.6 Luna roughly 80% ($1→$0.20/M input, $6→$1.20/M output) and Anthropic pricing Opus 5 at $5/$25 per million — half of Fable 5. The trigger is Chinese models including DeepSeek, Zhipu's GLM-5.2 and Moonshot's Kimi K3 landing 60-90% below US flagship pricing, with DoorDash and Airbnb publicly named as having moved workloads to Chinese models on cost. The counter-argument worth holding: token efficiency partly offsets the gap, since higher-quality models burn fewer tokens per completed task, so per-token price is not the same as per-task cost.

Graph relationships

BENCHMARKED AGAINST
GLM 5.2
GLM 5.3 uses the same base model as GLM 5.2 plus additional reinforcement learning.
Source finding
RELEASED
Z.ai
Z.ai shipped GLM-5.3 on August 14.
Source finding
BENCHMARKED AGAINST
Terminal-Bench
GLM-5.3's Terminal-Bench 3.0 score was produced using Z.ai's Claude Code configuration with three rollouts per task.
Source finding
RELEASED
Zhipu
Zhipu released GLM-5.3 with staged weight release planned for late August.
Source finding
RELEASED
Z.ai
Z.ai released GLM-5.3 on August 14, 2026 based on scaled post-training.
Source finding
BENCHMARKED AGAINST
GPT-5.6 Sol
GLM-5.3 scored 84.5% on CyberGym, slightly ahead of GPT-5.6 Sol.
Source finding
BENCHMARKED AGAINST
GLM 5.2
GLM-5.3 achieved 50% improvement in coding performance over GLM-5.2.
Source finding
RELEASED
Z.ai -> GLM-5.3
Z.ai shipped GLM-5.3 on August 14.
Source finding
RELEASED
Zhipu -> GLM-5.3
Zhipu released GLM-5.3 with staged weight release planned for late August.
Source finding
RELEASED
Z.ai -> GLM-5.3
Z.ai released GLM-5.3 on August 14, 2026 based on scaled post-training.
Source finding

Source trail

Ornith (Hugging Face model card)Hugging Face Blog (IBM Research)Latent Space X/Twitter RuntimeWire 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)Ars Technica TechRepublic (SiliconData data via Financial Times; corroborated by Silicon Data's own published index commentary)

Graph sources

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