Z.ai
Chat with GLM 5.3 online
Z.ai's frontier engineer — a million tokens of context and reasoning that holds across a long agent run.
1.048576M tokens
上下文窗口
131,072 tokens
最大输出
快速
速度
你会用它做什么
Whole-service refactors
A million tokens of context means the whole codebase in one thread.
Long agent runs
Reasoning before every tool call, kept coherent across many steps.
Architecture and review
Design decisions and trade-offs argued end to end.
Incident analysis
Logs, traces and history read together to find the cause.
Prompts to steal
Tuned to this model — click any line to copy.
关于 GLM 5.3
GLM 5.3 is Z.ai's August 2026 flagship and the current top of the GLM line, a step up from GLM 5.2. Z.ai built it for the two jobs its predecessors were already used for hardest: complex software engineering, and agent runs that have to stay coherent over many steps. On NinjaChat it is included in every plan, in the same picker as Claude, GPT-5.6 and Gemini.
The headline change is context. GLM 5.3 works with about a million tokens on NinjaChat's rail, roughly five times what 5.2 handled here, which changes what you can put in one thread: a whole service rather than a few files, a long incident history rather than the last hour, a full spec plus the code that is supposed to implement it.
The GLM signature carries through. The model reasons before each response and before each tool call, and it adapts how much reasoning a turn deserves rather than spending the same effort on everything. In a long agent loop that is the difference between a model that drifts by step twenty and one that still remembers what it was asked to do.
It is text in and text out — no image input on this one. If you need to hand it a screenshot or a diagram, GLM 5.3 Flash is the multimodal sibling and it sits right next to it in the picker. For everyday coding at speed, GLM 4.7 is still the cheaper pick; 5.3 is what you escalate to when the problem is genuinely hard.
如何使用 GLM 5.3
从零到第一个结果，不到一分钟。
01
Create a NinjaChat account and choose a plan
02
Open chat and pick GLM 5.3 from the model list
03
Paste the whole problem: files, logs, constraints, the goal
04
Keep the task in one thread so it carries its reasoning forward
05
Drop to GLM 4.7 or GLM 5.3 Flash when you want speed
获得更好结果的技巧
- Give it more context rather than less; that is what the million tokens are for
- Ask it to think longer on the turns that matter
- Paste error output verbatim rather than describing it
- Use GLM 5.3 Flash when the task includes images
GLM 5.3 与同类模型对比
客观对比——{model} 的优势所在，以及它的不足之处。
对比 GLM 5.2
查看模型A much larger context and stronger long-horizon behavior
5.2 remains a solid flagship for shorter tasks
对比 GLM 5.3 Flash
查看模型More depth on genuinely hard engineering problems
Flash is far cheaper and reads images and video
对比 DeepSeek V4 Pro
查看模型Adaptive reasoning tuned for agent loops
V4 Pro is a strong open alternative at a similar cost
