GLM-5.3-Flash

Z.AI officially introduced GLM-5.3-Flash on August 26, 2026, describing it as the first natively multimodal model in the GLM-5 series. It is a Mixture-of-Experts model with 320B total parameters and 18B active parameters, built on a newly trained base rather than a post-train of GLM-5.2, and trained on a 30T-token mult The model introduces a hybrid sparse and linear attention architecture combined with Manifold-Constrained Hyper-Connections, which Z.AI credits with sharply reducing long-context serving cost while preserving long-context capability. Compared with the GLM-4.5 series, it nearly halves both activated parameters (18B vs 3

Greek Ai's August 27, 2026 Medium write-up in CodeToDeploy confirms that GLM-5.3-Flash was released by Z.ai on August 26, 2026 as the newest member of the GLM-5 family. The model has 320 billion total parameters with only about 18 billion active parameters, supports multimodal inputs, offers a context window of up to 1 The piece positions GLM-5.3-Flash within a broader industry trend where the competition is increasingly about how much intelligence can be delivered for the compute actually used, rather than simply building the largest model. It highlights the model's combination of multimodal AI, 1M-token context, agentic coding capa

Ampere.sh's comparison piece contrasts GLM 5.3 and GLM 5.3 Flash with a detailed spec table. GLM 5.3 Flash has 320B total / 18B active parameters versus GLM 5.3's 753B / 40B active; both share a 1M-token context window. In independent Artificial Analysis testing, GLM 5.3 scores 60 on the Intelligence Index compared wit Flash is natively multimodal with native image input, while GLM 5.3 lacks native image input. Flash offers 3× usable quota on the Z.ai Coding Plan versus 1× reference for GLM 5.3. Flash has public weights available on day one, while GLM 5.3's open weights are listed as "coming soon." The comparison concludes Flash is t

An independent technical deep-dive from Local AI Zone confirms GLM-5.3-Flash shipped on August 26, 2026 as a 320B-parameter MoE activating 18B per token, running natively in FP8 with a 1,048,576-token context window, and being the first natively multimodal model in the GLM-5 series. The post documents that the model ha The deep-dive details the architecture as a hybrid linear and sparse attention MoE using an IndexPool that compresses four indexer heads into a single shared 64K-token state, with 45 layers at a similar total size to GLM-4.5's 355B. It documents the changelog against GLM-4.6, GLM-5.2, and GLM-5.3, the full benchmark sh

ZhipuAI's GLM-5.3-Flash is positioned as the GLM-5 series' first natively multimodal model, combining text and image processing in one framework from initial training rather than via retrofit. It carries 320B total parameters with 18B active, a 1M-token context window, and full open weights on Hugging Face under MIT license — enabling independent verification and fine-tuning that closed APIs cannot match. The release extends ZhipuAI's open-source trajectory by pairing frontier-level capability with dramatic cost reductions, reportedly advertising claims of 1/40th Claude Opus pricing per the report's framing. Earlier GLM iterations like GLM-5.2 delivered strong coding and reasoning but lacked native multimodal training; GLM-5.3 marks the deliberate shift to ground-up multimodal integration, with GLM-5.3-Flash expanding context windows and hardware efficiency for long-context research and enterprise workloads.

Z.ai released GLM-5.3-Flash on August 26, 2026 — a 320B-parameter Mixture-of-Experts model activating just 18B parameters per token, and the first natively multimodal entry in the GLM-5 series. Full weights shipped under an MIT license on Hugging Face the same day, inverting GLM-5.3's earlier weights-held-back pattern. The model was pre-trained on a 30-trillion-token multimodal corpus with a 1M-token context window. Architecturally, GLM-5.3-Flash interleaves linear-attention and sparse-attention layers, uses Manifold-Constrained Hyper-Connections, and adds IndexPool to compress indexer key vectors at long context. Z.ai reports attention compute cut by 3.0× and KV cache size by 4.4× versus GLM-5.3. Benchmark gains over GLM-5.2 include DeepSWE v1.1 rising 46.2→63.4, AutomationBench 26.2→48.8, and GDPval-AA v2 reaching 1773, ahead of Claude Opus 4.8 and GPT-5.6 Terra in Z.ai's table.

OrcaRouter reports that Zhipu AI disclosed every GLM-5.3-Flash production request now runs on a fleet of more than 100,000 domestically produced Chinese AI accelerators, transitioning from first boot to full workload in under two weeks with 3.2x throughput gains. Much of this optimization work was done not by infrastructure engineers but by an agent driven by GLM-5.3 itself, which read bottlenecks, proposed fixes, and wrote inference-system code under human-defined objectives and review. The model is described as 320B total and 18B active. The independent scoring on OrcaRouter gives GLM-5.3-Flash a 42 Intelligence and 72 Coding rating as of its August 26, 2026 release. The post positions the model's serving-stack maturity alongside other recent frontier and budget entries, highlighting how self-tuned inference plus a 100,000-chip domestic fleet translated into measurable production efficiency within weeks of launch.