GLM-5.3

Z.ai announced GLM-5.3 on August 14, 2026 as a post-training-only release built on the same base model as GLM-5.2, with every capability gain attributed to expanded post-training rather than a new architecture. The official launch blog reports a 50% coding improvement over GLM-5.2 on Z.ai's in-house Z.ai Code Bench and All benchmark figures cited in the blog are self-reported by Z.ai and not independently verified within the supplied excerpt. The release is framed by Z.ai as "Frontier Coding with Emergent Cyber Capabilities," and the delayed weight release signals explicit dual-use risk acknowledgment by the creator.

Apidog's self-hosting prep guide covers the GLM-5.3 launch of August 14, 2026, with open weights expected approximately two weeks later (around August 28, 2026) on Z.AI's Hugging Face organization following safety evaluation. The article frames the gap between launch and open-weights drop as preparation time for infras The guide notes that prior GLM-5 family releases shipped as paired BF16 and FP8 repositories on Hugging Face, so GLM-5.3 and GLM-5.3-FP8 are expected at release with community GGUF quantizations following days to weeks later. The recommended day-one serving stacks are vLLM and SGLang, both exposing OpenAI-compatible en

GLM 5.3, released by Z.ai on 14 August 2026, uses the same 743-billion-parameter base architecture as GLM 5.2, with all reported gains coming from scaled-up post-training rather than a new model. Per the supplied review, Z.ai's own comparison charts show roughly a 50% improvement on its internal coding benchmark and first-place open-weights finishes on Terminal-Bench 3.0, DeepSWE, and GDPval-AA v2, positioning it as a notable post-training-only capability jump. The same review reports a cybersecurity-capability jump from 24.4% to 54.4% on ExploitBench and roughly 2,436 vulnerabilities flagged across 269 open-source projects, including a reported flaw in the Cursor code editor. Because of these dual-use risks, Z.ai is withholding the open weights for approximately two weeks of additional safety evaluation before public release, with the model ranked alongside GLM 5.2, Kimi K3, DeepSeek V4 Pro, Claude Fable 5, and GPT-5.6 Sol in Z.ai's published comparisons.

Independent benchmark aggregator llm-stats ranks GLM-5.3 at a composite score of 52.1 with a blended price around $1.33 per million tokens. The model places third on GDPval-AA with a score of 1769 out of 3000, and sixth on the CyberGym vulnerability discovery benchmark with 0.84, corroborating Z.ai's own reported cybersecurity gains. GLM-5.3 ranks seventh on Terminal-Bench 2.1 with 0.88 using max reasoning effort, placing it among capable but not top-tier agents on end-to-end autonomous computer tasks. The aggregator's quality tracker shows performance improving over a 20-vote baseline window, with notable upward movement in the seven-day trend.

Z.ai's official developer documentation introduces GLM-5.3 as the company's latest flagship model, targeting complex software engineering and long-horizon agent tasks. It shares the same base model as GLM-5.2, with all improvements driven by post-training, including a reported 50% performance gain on Z.ai Code Bench and state-of-the-art results on Terminal Bench 3.0 and Agents' Last Exam. GLM-5.3 supports text-only inputs with a 1M-token context window and up to 128K tokens of output. Reasoning is always enabled across three effort levels: low, high, and max, with max set as the default. Applications using thinking.type disabled must switch to enabled before updating the model ID, or requests will fail.

InferenceX provides an independent technical analysis of GLM-5.3, confirming it as a post-training-only update over the GLM-5.2 base model. The architecture is documented as Mixture of Experts with 744B total parameters and approximately 40B active per pass, shared with GLM-5.2. GLM-5.3 is text-only and always reasonin On cyber capabilities, InferenceX reports that working with security teams in China, GLM-5.3 surfaced 2,436 vulnerabilities across 269 projects, including 1,097 medium-to-high severity issues, with an average latent lifetime of 26.6 years and the oldest flaw introduced in 1981. The analysis situates GLM-5.3 alongside i

Eigent.ai published a third-party breakdown of Z.ai's GLM-5.3 release dated after 2026-08-14, confirming the same 743B-parameter Mixture-of-Experts base as GLM-5.2 and framing the improvement as "environment scaling": the team spent more compute running the existing base through a much larger set of simulated long-hori The Eigent.ai piece corroborates specific vendor-reported benchmark deltas against GLM-5.2 — Terminal-Bench 3.0 climbing from 4.6 to 28.3 (roughly a 6x jump on a long-horizon CLI benchmark), DeepSWE v1.1 moving from 46.2 to 66.9, Agents' Last Exam (CLI) from 23.8 to 28.5, and a GDPval-AA v2 score of 1,769 across 44 occ