Claude Sonnet 4.6 vs Grok 4 for Classification

What Classification demands: accurate, repeatable mapping from input to categories or routes; strict adherence to output schema for downstream systems; safe refusal behavior on disallowed content; and reliable tool orchestration when classification triggers actions. With no external benchmark in the payload, we use our internal task scores as the primary evidence. Both Claude Sonnet 4.6 and Grok 4 score 4/5 on our classification test and share the top rank, so the raw task metric is a tie. Secondary capability scores explain real-world differences: Sonnet 4.6 has safety_calibration 5 vs Grok 4's 2 (important for moderation and refusal decisions), tool_calling 5 vs 4 (affects routing and function argument accuracy), and faithfulness 5 for both (keeps outputs grounded). Structured_output is 4 for both (JSON/schema reliability). Context window and modality matter too: Sonnet 4.6 offers a 1,000,000-token window and text+image->text modality; Grok 4 offers 256,000 tokens and text+image+file->text. Use these internal metrics to pick the model whose ancillary strengths match your classification requirements.

High-stakes content moderation pipeline: choose Claude Sonnet 4.6 — safety_calibration 5 vs Grok 4's 2 means Sonnet is more likely to correctly refuse or flag harmful inputs in our tests. Automated routing into microservices: choose Claude Sonnet 4.6 — tool_calling 5 vs 4 indicates more accurate function selection and argument formatting for downstream actions. Multimodal image+text classification at large scale: both perform well on classification (4/5), but Sonnet 4.6's 1,000,000-token context helps when you must classify items with extensive context or long histories. File-based batch classification (PDFs/logs): choose Grok 4 — it supports file inputs (text+image+file->text) and has constrained_rewriting 4 vs Sonnet's 3, useful if you must compress or strictly format labels under character limits. Low-cost prototyping: both models share input/output costs (3 and 15 per mTok in the payload), so pick by capability, not price.

For Classification, choose Claude Sonnet 4.6 if you need safer refusals, stronger tool orchestration, and maximal context (safety_calibration 5 vs 2; tool_calling 5 vs 4). Choose Grok 4 if you require built-in file input support or better constrained rewriting and you can accept weaker safety calibration (constrained_rewriting 4 vs 3; modality includes file->text). Both score 4/5 on raw classification accuracy in our tests, so pick the model whose secondary strengths match your workflow.