GLM Thinking Mode: When to Turn It On and How to Prompt

GLM's thinking (deep reasoning) mode is often treated as a "flip the switch and it gets smarter" button. In practice, turning it on in the wrong scenario means slower responses, longer outputs, and answers that are actually less crisp than standard mode. This article covers when to enable thinking, how to structure prompts to make the most of its reasoning, plus two paste-ready templates. For exact toggles and API parameters, refer to Zhipu's official docs.

1. When to enable thinking

My checklist is simple: if any item applies, turn it on.

• Multi-step reasoning: math derivations, logic puzzles, decisions requiring case analysis;
• Hard bugs: concurrency issues, memory leaks, cross-module state corruption — pattern matching often yields fixes that look right but aren't;
• Trade-off analysis: architecture choices and design reviews where the model should explicitly weigh each option;
• Agent / tool-call orchestration: long decomposition chains where one wrong step derails everything.

Conversely, keep it off for translation and polishing, format conversion, simple factual Q&A, and short code completion. Thinking consumes extra time and tokens; the simpler the task, the worse that overhead looks.

2. How to prompt: don't let the model improvise

Enabling thinking only grants more reasoning budget; where that reasoning goes depends on your prompt. Two practical points:

1. Give verification criteria, not just the task. Deep thinking excels at self-checking — but only if you tell it what "correct" looks like. Attaching expected output when debugging beats pasting only the error message.

2. Set explicit constraints. Reasoning models tend to over-explore, wandering into areas you don't care about. Writing down "do not modify X" or "must follow Y" focuses the reasoning on the actual problem.

3. Template 1: Hard bug triage

For GLM coding scenarios (including day-to-day use under Coding Plan), paste and replace the angle brackets:

Please enable deep thinking to help me locate the following bug.

[Environment] <language/framework/version, e.g. Python 3.11 + FastAPI>
[Symptom] <what actually happens, with the key error text>
[Expected] <what the correct behavior should be>
[Reproduction] <minimal reproduction steps, shorter is better>
[Already ruled out] <causes I've verified are NOT the issue>

Requirements:
1. List at least 3 possible root causes, ranked by likelihood, with a verification method for each;
2. When proposing a fix, do NOT touch <module/interface that must stay unchanged>;
3. After fixing, explain how to verify with a single command or test.

Don't skip the "already ruled out" section — it saves reasoning budget from dead ends you've already walked.

4. Template 2: Option comparison and selection

For architecture reviews and tech selection where explicit trade-offs matter:

Please reason deeply through the following technology selection.

[Context] <business scenario, team size, current stack>
[Candidates] <Option A, Option B; feel free to add candidates I missed>
[Hard constraints] <must-haves, e.g. on-premise deployment support, only X backend engineers>

Output format:
1. Score each option on dev cost / ops complexity / scalability / risk, with reasoning;
2. Give a clear recommendation and the scenario it fits;
3. Add a section on "hidden costs I may not have considered";
4. If missing information makes the conclusion highly uncertain, list follow-up research questions instead of forcing an answer.

Point 4 is where deep thinking shines: it's more willing to admit uncertainty and provide a validation path, rather than the "confident-sounding guesses" common in standard mode.

5. Positioning vs. other models

Readers often compare GLM's thinking mode with the reasoning modes of GPT, Claude, Kimi, and DeepSeek. Based on public information, all of them offer long-chain reasoning; the differences lie more in Chinese-language fit, coding toolchain integration (GLM ships Coding Plan and the BigModel API platform), and the flexibility of open weights for on-premise deployment. GLM-4V rounds out visual understanding. My suggestion: for Chinese-first workloads needing private deployment or flexible API billing, try GLM first — but verify capability boundaries against the official pages and your own tests. Someone else's benchmark score can't replace a prompt run in your own pipeline.

The right way to use thinking mode is "enable it only for hard tasks, and supply constraints plus verification criteria in the prompt." Save the two templates above into your snippet library — next time you hit a nasty bug or a selection meeting, paste, swap the variables, and go.