GLM 5.3 Flash

Z.ai announced GLM-5.3-Flash on August 26, 2026, framing it as the first natively multimodal model in the GLM-5 series with 320B total parameters but only 18B active per token. According to the launch blog, the model starts from a newly trained base, introduces a hybrid sparse-plus-linear attention architecture for the On coding and agentic benchmarks, Z.ai reports GLM-5.3-Flash scoring 63.4 on DeepSWE v1.1 (versus 46.2 for GLM-5.2) and 48.8 on AutomationBench (versus 26.2 for GLM-5.2), and reaching 29.0 versus Claude Opus 4.8's 29.5 on the in-house Z.ai Code Bench v1.0 at max effort. The model is positioned at roughly one-tenth the

A detailed third-party breakdown confirms GLM-5.3-Flash launched on August 26, 2026, as a 320B mixture-of-experts activating 18B parameters per token, with MIT-licensed weights on Hugging Face from day one. It is the first natively multimodal model in the GLM-5 series and the first built on a new base since GLM-5.2, wi Pricing is set at $0.15 per million input tokens and $0.50 output, compared with $1.40 and $4.40 for the full GLM-5.3 flagship. Notably, GLM-5.3's own 744B open weights remain missing — Z.ai's Hugging Face organization has no GLM-5.3 repository — making GLM-5.3-Flash the only open-weight model developers can access tod

An NVIDIA DGX Spark community thread documents local inference testing of GLM-5.3-Flash across multiple quantization recipes. Users evaluated NVFP4-lab (mixed NVFP4/MXFP8-MTP), RedHatAI W4A4 compressed-tensors, and NVFP4-A16 variants, measuring decode tokens-per-second, time-to-first-token, and quality scores on tool-e The thread reveals a practical vLLM requirement: the framework needs a fix in modelopt.py because MTP layers are stored under language_model.model.layers.45, an issue already addressed in DeepSeek-V4-Flash support. Community members tested RedHat quantizations and discussed ongoing optimizations, with an "update coming

A Medium technical analysis from Greek Ai describes GLM-5.3-Flash as Z.ai's newest open-weight release built around the principle of frontier-level capabilities without frontier-level inference costs. It confirms the 320B total / 18B active parameter architecture, native multimodal support, 1M-token context window, and The piece emphasizes that the GLM-5 family competition is shifting from raw parameter count to intelligence-per-compute, with GLM-5.3-Flash delivering dramatically lower inference costs. The model combines multimodal inputs, agentic capabilities, and a 1M-token context into a single architecture, making it suitable for

An Ampere comparison piece positions GLM-5.3-Flash against the full GLM-5.3 flagship. In independent Artificial Analysis testing, GLM-5.3 scores 60 on the Intelligence Index versus 57 for GLM-5.3-Flash, but Flash costs roughly one-ninth as much at normal API pricing and activates only 18B parameters versus 40B for GLM- Specs confirm GLM-5.3-Flash at 320B total / 18B active parameters with a 1M context window, while GLM-5.3 runs at 753B/40B active. GLM-5.3-Flash pricing is $0.15/M input and $0.50/M output versus $1.40/M and $4.40/M for GLM-5.3. GLM-5.3 is faster at generation (85 tok/s vs 49 tok/s) and wins on complex coding and long-

A merged community PR on the official zai-org/GLM-5.3-Flash Hugging Face repository adds structured evaluation results extracted from the model card's benchmark chart to the .eval_results/ directory. The added numbers are Terminal-Bench 2.1 at 84.3, DeepSWE at 63.4, and Humanity's Last Exam at 55.3, providing an offici The same PR documents three additional model-card claims that could not be registered because they lack a registered eval.yaml on the Hugging Face Hub: Agent's Last Exam at 26.3, AutomationBench v1.0.6 at 48.8, and GDPval-AA v2 at 1773. The contributor also flags that the cited arxiv:2602.15763 paper is the original GL