Claude Haiku 4.5 vs Claude Sonnet 4.6 for Classification

What Classification demands: accurate categorization and routing, consistent structured outputs, faithful use of source material, robust safety calibration (correctly refuses harmful or ambiguous prompts), and reliable tool integrations when classification pipelines call external services. In our testing both Claude Haiku 4.5 and Claude Sonnet 4.6 score 4/5 on the Classification test itself, and both have identical strengths in structured output (4/5), tool calling (5/5), and faithfulness (5/5). The primary internal differentiator is safety_calibration (Haiku 2 vs Sonnet 5) and creative_problem_solving (Haiku 4 vs Sonnet 5), which indicate Sonnet handles edge cases and refusal decisions better. Supplementary external evidence: Sonnet 4.6 records 75.2% on SWE-bench Verified and 85.8% on AIME 2025 (Epoch AI), signals that align with higher robustness on complex classification-like reasoning; Haiku 4.5 has no external benchmark scores in the payload. Use the internal 1–5 scores to explain capabilities, and treat the Epoch AI numbers as additional support for Sonnet’s edge on robustness.

High-volume, low-risk routing (where cost matters): choose Claude Haiku 4.5. It matches Sonnet on raw classification accuracy in our tests (4/5), and costs less per mTok (input 1, output 5 vs Sonnet input 3, output 15) — ~3x cheaper per-token. Example: tagging millions of customer emails for simple intent routing where refusal behavior is rare. Safety-critical moderation or edge-case classification: choose Claude Sonnet 4.6. Sonnet’s safety_calibration 5 vs Haiku 2 means it better distinguishes harmful requests and applies refusals correctly in our testing; Sonnet also scores higher on creative_problem_solving (5 vs 4), helpful for ambiguous labels. Example: content-moderation pipelines, medical triage routing, or high-stakes automated decisions where wrong labels carry legal/regulatory risk. Complex, multimodal or iterative classification workflows: Sonnet has a larger context window and stronger external-language/math signals (SWE-bench 75.2% and AIME 85.8% per Epoch AI), which helps when classifications depend on long histories or nuanced rules. If you need parity on structured outputs and tool calling, both models are equivalent in our tests (structured_output 4, tool_calling 5, faithfulness 5).

For Classification, choose Claude Haiku 4.5 if you need lower-cost, low-latency bulk classification where the environment is predictable and safety refusals are uncommon. Choose Claude Sonnet 4.6 if you need safer, more robust classification for edge cases, moderation, or high-stakes routing — Sonnet ties on raw classification (4/5) but adds a safety_calibration advantage (5 vs 2) and external SWE-bench (75.2%) and AIME (85.8%) signals (Epoch AI) at roughly 3x the per-token cost.