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

Grok 4.6 vs Grok 4.5 vs Grok 4.20 vs Grok 4.3 on label six support tickets

xAI models side by side on "Label six support tickets": Grok 4.3 scores 10/10; Grok 4.3 is the cheapest answer scoring 8+ at $1.05 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.

Grok 4.6

x-ai/grok-4.6

10/10
Latency
471ms
Cost
$0.00191
Per 1,000
$1.91

381 in · 223 out (180 reasoning) · 1 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 labels with proper JSON formatting and no extraneous commentary.

Grok 4.5

x-ai/grok-4.5

10/10
Latency
305ms
Cost
$0.00185
Per 1,000
$1.85

381 in · 217 out (174 reasoning) · 1 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 labels with proper JSON formatting and no extraneous commentary.

Grok 4.20

x-ai/grok-4.20

10/10
Latency
269ms
Cost
$0.00214
Per 1,000
$2.14

355 in · 732 out (679 reasoning) · 14 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 labels, returned as a compact JSON object with no extra commentary.

Grok 4.3

x-ai/grok-4.3

10/10
top judge scorecheapest scoring 8+
Latency
331ms
Cost
$0.00105
Per 1,000
$1.05

361 in · 320 out (267 reasoning) · 14 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 labels, returned as a compact JSON object with no extra 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, Grok 4.3 at $1.05 per 1,000 runs. If you need the top score, Grok 4.3 at $1.05 per 1,000 runs.

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