Z.ai
Available in Shortly AI
GLM 5.3 Flash
Released August 26, 2026
ReasoningVision (Image)Tool Use
GLM-5.3-Flash is Z.ai’s native multimodal coding model, featuring 320B total parameters, 18B activated parameters, and a 1M-token context window. Its efficient hybrid attention architecture supports visual coding, tool use, and end-to-end professional workflows across code, browsers, documents, and graphical interfaces.
- Context window
- 1M tokens
- Maximum output
- 131K tokens
- Input pricing
- $0.15/M
- Output pricing
- $0.50/M
Independent benchmark
LiveBench performance
71.6overall
Benchmark data from LiveBench, release 2026-06-25. Scores are pinned and are not fetched on page load.
77.6
79.0
56.8
81.2
76.4
77.3
52.8
| Category | Score |
|---|---|
| Reasoning | 77.644 |
| Coding | 78.950 |
| Agentic Coding | 56.768 |
| Mathematics | 81.245 |
| Data Analysis | 76.401 |
| Language | 77.307 |
| Instruction Following | 52.821 |
Benchmarks measure selected evaluation tasks and do not guarantee performance for every prompt or workflow.
FAQ
Similar AI models
DeepSeek V4 Flash Vision ExpDeepSeekDeepSeek-V4-Flash-Vision-Exp is the first experimental multimodal model in the DeepSeek-V4 family. It builds on DeepSeek-V4-Flash by adding visual modules and continued training for visual understanding, with substantially improved multimodal agent capabilities while remaining comparable on text-only agent tasks.Nemotron 3.5 Lightning 30BNVIDIANVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 is a large language model (LLM) trained by NVIDIA.
The model employs a hybrid Mixture-of-Experts architecture, utilizing interleaved Mamba-2 and MoE layers, along with select Attention layers. The Lightning 3.5 model is released alongside a number of speculative decoding methods for faster text generation. The model has 3B active parameters and 30B parameters in total.Ling 3.0 FlashInclusionaiLing 3.0 Flash is designed with token efficiency and production-scale agentic inference as key priorities, enabling developers to complete more useful work within constrained token, latency, and serving-cost budgets.
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