HomeAI models
Z.ai: GLM 5.3
AnyAPI page shows AI model producer's logo
Tier

Z.ai: GLM 5.3

Z.ai's flagship reasoning model for repository-scale coding and long-horizon agents, with strong token efficiency.

Context window: 
1048576
M tokens
Output: 
131072
 tokens
Modality:
Text
PDF
Start Free
AnyAPI shows dashboard
GLM-5.3: Long-Horizon Coding and Agent Execution With Efficient Token Use GLM-5.3 is Z.ai's flagship reasoning model, positioned above GLM-5.2 for complex software engineering and long-horizon agent tasks. It reuses the GLM-5.2 743B-parameter mixture-of-experts base, with every reported gain coming from scaled post-training rather than new pretraining. GLM-5.3 pairs a 1M-token context window with always-on reasoning and strong tool-use behavior, and it is notable for reaching higher agentic-coding scores while spending fewer output tokens than GLM-5.2. It fits teams running repository-scale refactoring, multi-step automation, and terminal-based coding agents. Start building with the GLM-5.3 API on AnyAPI.ai

Sample code for 

Z.ai: GLM 5.3

Code examples coming soon...

Frequently
Asked
Questions

Answers to common questions about integrating and using this AI model via AnyAPI.ai

GLM-5.3 supports a 1M-token context window and a maximum output of 128K tokens per response, according to Z.ai's developer documentation. The large context suits repository-scale coding and long agent transcripts, but the 128K output cap limits how much the model can generate in a single reply regardless of input size.

No. GLM-5.3 always operates with reasoning enabled; disabling it is not supported. You control depth through three effort levels—low, high, and max—with max as the default. Z.ai recommends max for complex coding. Set reasoning_effort explicitly, since ported workloads otherwise inherit max effort and higher token use.

No. Full GLM-5.3 is text input and text output only. Native image, video, and file input belong to the separate GLM-5.3-Flash model, which starts from a different base. If you need multimodal understanding, use GLM-5.3-Flash or another vision-capable model rather than full GLM-5.3.

GLM-5.3 uses the exact same 743B mixture-of-experts base as GLM-5.2, with all gains from post-training. It posts substantially higher long-horizon coding and agent scores—for example, Terminal-Bench 3.0 rises from 4.6 to 28.3 in Z.ai's tests—and reaches those results using fewer output tokens per task than GLM-5.2.

GLM-5.3 is specifically positioned for coding agents: repository refactoring, terminal task execution, test generation, and multi-step automation, backed by tool calling and parallel tool calls. Vendor benchmarks show strong agentic-coding results with good token efficiency. Note that closed frontier models still lead on some general-coding evaluations, so validate on your workload.