Z.ai's flagship reasoning model for repository-scale coding and long-horizon agents, with strong token efficiency.
Output Speed *
Intelligence Index *
Context Window *
Input price
Output price
Performance
Where GLM-5.3 Moves the Needle: Long-Horizon Agentic Coding
Benchmarks
Reading GLM-5.3's Coding and Agent Numbers Critically
Output Speed
Intelligence Index
MMLU *
GPQA *
HLE *
LiveCodeBench *
Technical Specifications
What the model supports
Comparison
GLM-5.3 vs GLM-5.2: What Post-Training Actually Changed
GLM-5.3 and GLM-5.2 share the exact same 743B mixture-of-experts base—Z.ai states every GLM-5.3 gain comes from post-training, with no new pretraining run or architecture change. That makes this an unusually clean upgrade decision: same underlying model, same 1M-token context and 128K output, but retuned through scaled reinforcement learning across more task environments. The practical question is whether the reported jumps on long-horizon coding and agent benchmarks, plus improved token efficiency, justify moving. GLM-5.2 shipped MIT-licensed weights near launch, whereas GLM-5.3 staged its open-weight release behind a security review.
Choose GLM-5.3 when you run long-horizon coding and agent workflows and want higher task-resolution rates with fewer output tokens per task—it posts markedly stronger agentic-coding and Terminal-Bench results than GLM-5.2 on the same base. Choose GLM-5.2 when you need immediately downloadable open weights, want a model with a documented non-reasoning path, or your workloads are short and simple enough that GLM-5.3's always-on reasoning adds unnecessary token and latency overhead.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Long-horizon coding agents
GLM-5.3 targets multi-turn agentic coding: repository-level refactoring, test generation, and bug triage across long trajectories. Its tool-use architecture lets it call functions, inspect outputs, and iterate—behaviors required for agent loops rather than single-turn completions. Vendor-reported Terminal-Bench 3.0 and DeepSWE gains support this positioning, and the token-efficiency advantage lowers per-run cost. Keep human review in the loop for production code changes.
Long-context automation pipelines
The 1M-token context window lets GLM-5.3 process large codebases, extended agent transcripts, or long document sets in a single request, reducing the need for aggressive chunking or retrieval loops. This suits automation pipelines whose inputs exceed the limits of smaller-context models. Note the 128K output cap constrains how much can be generated per response even when input is very large.
Terminal-based task execution
GLM-5.3 is tuned for CLI and terminal workflows, reaching state-of-the-art open-source scores on Terminal-Bench 3.0 and Agents' Last Exam (CLI) in Z.ai's evaluations. Combined with parallel tool calling, it fits agents that run shell commands, execute scripts, and iterate on results. Execution and permission scope come from the surrounding harness, so sandbox appropriately.
Reasoning-heavy engineering tasks
Always-on reasoning with adjustable effort makes GLM-5.3 suited to complex, multi-step software-engineering problems where persistent step-by-step reasoning improves outcomes. Reserve max effort for the hardest tasks and drop to low for simpler calls to control cost. This is a poor fit for trivial classification or high-volume short completions, where forced reasoning wastes tokens.