Classify · 6 checks · max 400 tokens · benched 2026-09-16

GLM 5.3 vs GLM 5.2 vs GLM 5.3 Flash on label six support tickets

Z Ai models side by side on "Label six support tickets": GLM 5.3 Flash scores 10/10; GLM 5.3 Flash is the cheapest answer scoring 8+ at $0.03 per 1,000 runs. Outputs, checks, judge reasons, latency and cost.

The prompt every model received

System

You are a classifier. Use only the allowed labels. Reply with JSON only, no prose, no code fences.

User

Classify each ticket into exactly one label from this set: billing, bug, feature_request, account_access. Return a JSON object mapping ticket id to label.

t1: "I was invoiced for 12 seats but we only use 8."
t2: "The export button does nothing when the report has more than 1,000 rows."
t3: "Every time I open the dashboard on Safari the charts render blank."
t4: "It would be great if we could schedule reports to email weekly."
t5: "I reset my password twice and still cannot log in; it says my account is locked."
t6: "Can you add a dark mode to the mobile app?"

Rubric for the judge: All six tickets get the right label from the fixed set, returned as a compact JSON object with no commentary.

Side by side

Every cell is one OpenRouter call at temperature 0 with the prompt's token cap and reasoning effort "low" where the model supports it. Cost is usage × the catalogue rate in models.json. Quality is one judge call to anthropic/claude-haiku-4.5 against the prompt's rubric, cached per prompt version.

GLM 5.3

z-ai/glm-5.3

10/10

Latency: 331ms
Cost: $0.00046
Per 1,000: $0.46

179 in · 47 out · 12 words · checks 6/6

json_valid
regex
regex
regex
regex
regex

{"t1": "billing", "t2": "bug", "t3": "bug", "t4": "feature_request", "t5": "account_access", "t6": "feature_request"}

Judge: All six tickets are correctly classified into the specified label set with proper JSON formatting and no extraneous commentary.

GLM 5.2

z-ai/glm-5.2

10/10

Latency: 638ms
Cost: $0.00023
Per 1,000: $0.23

179 in · 61 out (2 reasoning) · 16 words · checks 6/6

json_valid
regex
regex
regex
regex
regex

```json
{
  "t1": "billing",
  "t2": "bug",
  "t3": "bug",
  "t4": "feature_request",
  "t5": "account_access",
  "t6": "feature_request"
}
```

Judge: All six tickets are correctly classified into the specified labels and returned as a compact JSON object with no commentary.

GLM 5.3 Flash

z-ai/glm-5.3-flash

10/10

top judge scorecheapest scoring 8+

Latency: 575ms
Cost: $3.2e-5
Per 1,000: $0.03

179 in · 74 out (27 reasoning) · 12 words · checks 6/6

json_valid
regex
regex
regex
regex
regex

{"t1": "billing", "t2": "bug", "t3": "bug", "t4": "feature_request", "t5": "account_access", "t6": "feature_request"}

Judge: All six tickets are correctly classified into the specified label set with proper JSON formatting and no extraneous commentary.

Frequently asked

What does this prompt test?

Classify: All six tickets get the right label from the fixed set, returned as a compact JSON object with no commentary. The deterministic checks are json_valid, regex, regex, regex, regex, regex.

Which model should I pick for this task?

If the judge's bar of 8/10 is good enough for you, GLM 5.3 Flash at $0.03 per 1,000 runs. If you need the top score, GLM 5.3 Flash at $0.03 per 1,000 runs.

Same prompt, other families

Run it yourself

Re-run this prompt on Z Ai with your own OpenRouter key, or tweak the wording and see what changes.

Side by side

GLM 5.3

GLM 5.2

GLM 5.3 Flash

Frequently asked

Same prompt, other families

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