GLM-5.3-Flash
A lightweight large language model in Zhipu AI's GLM series, supporting multi-turn dialogue and tool calling, with optimization targeting inference speed.
Core Facts
The Flash version of the GLM series is positioned as a lightweight model with low latency and low cost.
The limitation of specialized small models is that their generalization ability rapidly degrades when task boundaries are ambiguous, cross-domain reasoning is required, or unseen edge cases need to be handled.
Flash-series models are typically designed as high-throughput, low-latency, low-cost options, targeting large-scale, high-frequency scenarios that do not require deep reasoning.
Timeline (last 90 days)
On the PrimeVul held-out test set, REVA with GLM-5.3-Flash achieves 60.0% accuracy at about one second per function.
On PrimeVul, the agent-style detector VulTriage (also running on GLM-5.3-Flash) achieves 59.2% accuracy.
GLM-5.3-Flash is the first natively multimodal model in the GLM-5 series, supporting image and text input.
GLM-5.3-Flash is a multimodal MoE model with 320B total parameters and 18B activated per token, under the MIT license, supporting a 1M token context.
The model card gives GLM-5.3-Flash's benchmark scores: 84.3 on Terminal-Bench 2.1 and 63.4 on DeepSWE.
Z.ai claims that GLM-5.3-Flash is close to Claude Opus 4.8 on coding and agent benchmarks and costs one tenth of GLM-5.2's price.
Z.ai's GLM Coding Plan starts at $18/month, with the Lite tier at 2,000 credits per 5 hours, the Pro tier at 12,000, and the Max tier at 28,000, supporting GLM-5.3 and GLM-5.3-Flash.
Phase 3 planned to run the GGUF version of GLM-5.3-Flash on an H200 SXM 141GB using the llama.cpp server, but the total model weights were 108.7GB and the download rate dropped from about 9GB/min to about 1.6GB/min; it was aborted when container disk usage reached about 69GB, producing zero model outputs.
GLM-5.3 has an input multiplier of 6.9, a cached input multiplier of 1.7, and an output multiplier of 24; GLM-5.3-Flash has an input multiplier of 2.3, a cached input multiplier of 0.56, and an output multiplier of 8.
GLM-Legal is built on the base model GLM-5.3-Flash, belonging to the 'base + domain fine-tuning' route.
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All Facts (20)
The Flash version of the GLM series is positioned as a lightweight model with low latency and low cost.
75%VerifiedThe limitation of specialized small models is that their generalization ability rapidly degrades when task boundaries are ambiguous, cross-domain reasoning is required, or unseen edge cases need to be handled.
65%VerifiedFlash-series models are typically designed as high-throughput, low-latency, low-cost options, targeting large-scale, high-frequency scenarios that do not require deep reasoning.
65%VerifiedZhipu AI has launched the GLM-5.3 Flash lightweight model.
85%UnverifiedGLM-5.3-Flash achieved a score of 57 on the Artificial Analysis composite intelligence index evaluation, on par with Claude Opus 4.8.
70%UnverifiedThrough MTP (Multi-Token Prediction) technology, the inference speed of Qwen3.8-Flash-Next and GLM-5.3-Flash is improved by up to 2x.
70%UnverifiedGLM-5.3-Flash, from Z.ai, is a multimodal MoE model with a total of 320B (320 billion) parameters, with only 18B parameters activated at any given time.
70%UnverifiedOn the PrimeVul held-out test set, REVA with GLM-5.3-Flash achieves 60.0% accuracy at about one second per function.
50%UnverifiedOn PrimeVul, the agent-style detector VulTriage (also running on GLM-5.3-Flash) achieves 59.2% accuracy.
50%UnverifiedGLM-5.3-Flash is the first natively multimodal model in the GLM-5 series, supporting image and text input.
50%UnverifiedZ.ai claims that GLM-5.3-Flash is close to Claude Opus 4.8 on coding and agent benchmarks and costs one tenth of GLM-5.2's price.
50%UnverifiedGLM-5.3-Flash is a multimodal MoE model with 320B total parameters and 18B activated per token, under the MIT license, supporting a 1M token context.
50%UnverifiedThe model card gives GLM-5.3-Flash's benchmark scores: 84.3 on Terminal-Bench 2.1 and 63.4 on DeepSWE.
50%UnverifiedZ.ai's GLM Coding Plan starts at $18/month, with the Lite tier at 2,000 credits per 5 hours, the Pro tier at 12,000, and the Max tier at 28,000, supporting GLM-5.3 and GLM-5.3-Flash.
50%UnverifiedGLM-5.3 has an input multiplier of 6.9, a cached input multiplier of 1.7, and an output multiplier of 24; GLM-5.3-Flash has an input multiplier of 2.3, a cached input multiplier of 0.56, and an output multiplier of 8.
50%UnverifiedGLM-Legal is built on the base model GLM-5.3-Flash, belonging to the 'base + domain fine-tuning' route.
50%UnverifiedGLM-5.3-Flash 是第一个将稀疏注意力和线性注意力融合进单一混合系统的开放前沿模型。
50%UnverifiedGLM-5.3-Flash 使用 IndexPool 技巧，将索引器的 key 向量按4合1做加权池化，在百万token上下文下保持检索机制小巧。
50%UnverifiedGLM-5.3-Flash 在国产AI芯片上规模化运行，搭配基于 SGLang 的专用推理引擎，声称端到端服务增益达到3倍。
50%Unverified几乎所有对比数据都来自 Z.ai 自报，且各基准的测试框架设置不同，跨模型差距应视为方向性参考而非定论。
50%