GPT-5.4 vs Grok 4 for Classification
Winner: Grok 4. In our testing Grok 4 scores 4/5 on Classification vs GPT-5.4's 3/5, and Grok 4 is ranked tied for 1st for this task (tied with 29 others) while GPT-5.4 ranks 31st. No third-party external benchmark is present for Classification in the payload, so this decision is driven by our task score and task ranks. Grok 4’s higher task score indicates better out‑of‑the‑box categorization and routing accuracy; GPT-5.4 remains valuable when strict schema compliance, broader context, or strong safety calibration matter.
openai
GPT-5.4
Benchmark Scores
External Benchmarks
Pricing
Input
$2.50/MTok
Output
$15.00/MTok
modelpicker.net
xai
Grok 4
Benchmark Scores
External Benchmarks
Pricing
Input
$3.00/MTok
Output
$15.00/MTok
modelpicker.net
Task Analysis
What Classification demands: accurate mapping of inputs to labels, consistent schema-compliant outputs for routing, safe refusal or re-routing for harmful/ambiguous content, and the ability to use tool calls or context to resolve edge cases. There is no external benchmark provided for Classification in this payload, so the primary signal is our internal task score (1–5). Grok 4: classification score 4, task rank tied for 1st of 52, structured output 4, tool calling 4, faithfulness 5, persona consistency 5, safety calibration 2. GPT-5.4: classification score 3, task rank 31 of 52, structured output 5, tool calling 4, faithfulness 5, persona consistency 5, safety calibration 5. Use our scores as follows: the 1‑point classification gap (4 vs 3) is the primary reason Grok 4 wins for raw routing/labeling accuracy; GPT-5.4’s advantages in structured output (5 vs 4) and safety calibration (5 vs 2) explain why it is preferable when strict JSON/schema adherence or conservative safety behavior are required. Also consider context windows: GPT-5.4 supports a ~1,050,000 token window vs Grok 4’s 256,000 tokens — relevant for classification that must inspect extremely long documents.
Practical Examples
When Grok 4 shines (based on scores):
- High-volume ticket routing: Grok 4 (classification 4) provides better label accuracy for short-to-medium inputs and routing decisions; tool calling 4 means it can integrate with function calls to tag and route.
- Multi-language routing: Grok 4 has multilingual 5 and persona consistency 5, so cross‑language categorization stays consistent. When GPT-5.4 shines (based on scores and metadata):
- Schema‑strict pipelines: GPT-5.4’s structured output 5 makes it better when you need exact JSON responses for downstream automation (5 vs 4).
- Safety‑sensitive classification: GPT-5.4’s safety calibration is 5 vs Grok 4’s 2, so GPT-5.4 is preferable when classification decisions must reliably refuse or reclassify harmful content.
- Extremely long‑document classification: GPT-5.4’s 1,050,000 token context window supports classification over massive contexts that exceed Grok 4’s 256,000 token window. Operational cost nuance: both models share the same output cost per mT (15), while GPT-5.4 has a lower input cost (2.5 vs Grok 4’s 3), so at very high input volumes GPT-5.4 can be cheaper to feed large inputs despite its lower classification score.
Bottom Line
For Classification, choose GPT-5.4 if you need strict schema compliance, conservative safety calibration, or classification over extremely long documents. Choose Grok 4 if you want the higher out‑of‑the‑box categorization and routing accuracy (our score 4 vs 3) for everyday multilingual or ticket‑routing classification tasks.
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.