Claude Haiku 4.5 vs Claude Opus 4.6 for Classification

What Classification demands: accurate label assignment, consistent routing decisions, reliable structured output (for JSON or schema), low false positives on sensitive classes, and cost-effective throughput. In the absence of an external benchmark for this task, we rely on our 1–5 proxy scores. On those proxies, Claude Haiku 4.5 scores 4/5 for Classification and is ranked 1 of 52 for the task in our tests; Claude Opus 4.6 scores 3/5 and ranks 31 of 52. Both models score 4/5 on structured_output (important for schema compliance) and 5/5 on tool_calling (important when classification is paired with downstream tools or routing). Faithfulness is 5/5 for both models, so neither is more prone to inventing labels. Key trade-offs: Haiku delivers the better raw classification accuracy in our suite and is far cheaper (input_cost_per_mtok 1, output_cost_per_mtok 5) while Opus offers superior safety_calibration (5/5 vs 2/5) and a much larger context window (1,000,000 vs 200,000 tokens), which matters for safety-sensitive or massive-document classification.

Where Claude Haiku 4.5 shines (based on our scores and costs):

• High-throughput email routing: Haiku’s 4/5 classification score and lower costs (input 1 / output 5 per mTok) reduce per-message spend while keeping routing accuracy high.
• Product categorization for ecommerce feeds: structured_output 4/5 + classification 4/5 gives reliable JSON labels at scale with lower token cost.
• Multilingual customer intent triage: Haiku’s 5/5 multilingual and 4/5 classification balance accuracy and cost. Where Claude Opus 4.6 shines (based on our scores and capabilities):
• Safety-critical moderation routing: Opus’s safety_calibration 5/5 (vs Haiku 2/5) reduces the risk of permitting harmful content during classification.
• Very large-context classification: Opus’s 1,000,000-token window supports labeling across long documents or many concatenated examples where Haiku’s 200,000 window may be limiting.
• Complex pipeline classification that needs creative problem solving: Opus scores 5/5 for creative_problem_solving (vs Haiku 4/5), useful when label decisions require nuanced reasoning or fallback heuristics.

For Classification, choose Claude Haiku 4.5 if you need higher classification accuracy in our tests (4/5 vs 3/5), top task ranking (1st), and much lower per-mTok costs (input 1, output 5). Choose Claude Opus 4.6 if safety calibration or extreme context length is critical (safety_calibration 5/5 vs Haiku 2/5; context_window 1,000,000 tokens) and you accept higher cost (input 5, output 25).