Claude Sonnet 4.6 vs GPT-5.4 for Classification

What Classification demands: accurate mapping of inputs to labels or routes, predictable output formats for downstream systems, resistance to hallucination when label definitions are present, and reliable selection of next actions (tool calls or function routing). On our benchmarks (classification = 'Accurate categorization and routing'), Claude Sonnet 4.6 scores 4 while GPT-5.4 scores 3. That makes our internal classification benchmark the primary signal for this task (no external benchmark provided). Supporting capabilities that matter: structured_output (JSON/schema adherence), tool_calling (function selection and argument accuracy), faithfulness (sticking to source definitions), multilingual support (consistent labels across languages), and safety_calibration (refusing harmful or out-of-scope labels). In our tests Sonnet leads on tool_calling (5 vs 4) and matches GPT-5.4 on faithfulness and safety_calibration (both 5), while GPT-5.4 is stronger at structured_output (5 vs 4). Those component scores explain why Sonnet produces more accurate routing decisions in mixed, real-world classification tasks, whereas GPT-5.4 is more reliable when strict schema conformance is the top priority.

1. Multi-intent customer support routing: Sonnet (classification 4, tool_calling 5) routes complex, ambiguous requests more accurately and reliably triggers the correct function/queue. 2) Enterprise document tagging with strict JSON ingestion: GPT-5.4 (structured_output 5) produces cleaner, schema-compliant JSON, reducing downstream parser errors despite scoring 3 on raw classification accuracy. 3) Multilingual moderation labels: both models score 5 for multilingual and 5 for safety_calibration, but Sonnet's higher classification score (4 vs 3) means better overall label accuracy across languages in our tests. 4) High-throughput automated pipelines where you need both accurate routing and action selection: Sonnet's combination of classification=4 and tool_calling=5 favors fewer misroutes and more reliable downstream automation. Note cost and I/O: Claude Sonnet 4.6 input cost is $3 per mTok vs GPT-5.4 at $2.5 per mTok; both have $15 per mTok output — factor input-cost if you run very large preprocessing prompts.

For Classification, choose Claude Sonnet 4.6 if you need the most accurate routing and action selection in our tests (scores 4 vs 3). Choose GPT-5.4 if strict schema/JSON compliance is the single top requirement (structured_output 5) or you need slightly lower input cost ($2.5 vs $3 per mTok).