Claude Haiku 4.5 vs o3

Summary of our 12-test suite (in our testing): Claude Haiku 4.5 wins classification (4 vs 3), long_context (5 vs 4), and safety_calibration (2 vs 1). o3 wins structured_output (5 vs 4) and constrained_rewriting (4 vs 3). The remaining seven tests tie: strategic_analysis, creative_problem_solving, tool_calling, faithfulness, persona_consistency, agentic_planning, and multilingual. Details and task implications: - Classification: Haiku 4 vs o3 3 in our testing — this means Haiku is more reliable for routing/categorization tasks (see benchmarkDescriptions: classification = accurate categorization). Haiku's ranking shows it tied for 1st in classification (rank display: "tied for 1st with 29 other models out of 53 tested"). - Long context: Haiku 5 vs o3 4 — Haiku is stronger at retrieval and coherence across 30K+ tokens (long_context definition). Haiku is tied for 1st on long_context ("tied for 1st with 36 other models out of 55 tested"); o3 ranks lower (rank 38 of 55). For apps that feed large documents, Haiku's 5 indicates fewer retrieval errors. - Safety calibration: Haiku 2 vs o3 1 — Haiku better balances refusing harmful requests while allowing legitimate ones in our tests (Haiku rank 12/55 vs o3 rank 32/55). - Structured output: o3 5 vs Haiku 4 — o3 is superior at JSON/schema compliance and format adherence (structured_output). o3 ties for 1st in structured_output ("tied for 1st with 24 other models out of 54 tested"), so it will more reliably match strict schema requirements. - Constrained rewriting: o3 4 vs Haiku 3 — for compression within tight character limits (e.g., summarization to fixed sizes), o3 does better in our testing (o3 rank 6 of 53). - Ties: In strategic_analysis, creative_problem_solving, tool_calling, faithfulness, persona_consistency, agentic_planning, and multilingual both models score identically in our tests; both are strong in reasoning, tool selection, and multilingual outputs (see tie list). - External benchmarks (Epoch AI): o3 posts third‑party scores — SWE-bench Verified 62.3% (Epoch AI), Math Level 5 97.8% (Epoch AI), AIME 2025 83.9% (Epoch AI). Use these as supplemental evidence that o3 is especially strong on math/problem-solving benchmarks. Note: our internal 1–5 scores and Epoch AI percentages are different scales and are reported separately.

Pricing per mTok (1,000 tokens): Claude Haiku 4.5 charges $1 input / $5 output; o3 charges $2 input / $8 output. Assuming a 50/50 split of input vs output tokens, Haiku effective cost ≈ $3.00 per mTok vs o3 ≈ $5.00 per mTok (Haiku is 62.5% of o3, matching the payload priceRatio 0.625). Monthly examples: 1M tokens ≈ 1,000 mTok -> Haiku $3,000 vs o3 $5,000; 10M tokens -> Haiku $30,000 vs o3 $50,000; 100M tokens -> Haiku $300,000 vs o3 $500,000. Who should care: startups and high-volume chat/ingest apps will save tens to hundreds of thousands of dollars at scale; teams building low-volume, high-precision tools (where o3's structured-output or math strengths matter) may accept the higher spend.

Choose Claude Haiku 4.5 if: - You need affordable, high-throughput chat or retrieval with large context windows (Haiku long_context 5 vs o3 4). - You prioritize classification and safer calibration in our tests (Haiku classification 4, safety_calibration 2). - You want lower operating cost: input $1 / output $5 per mTok. Choose o3 if: - Your product needs strict JSON/format compliance or tight rewrite/compression (o3 structured_output 5, constrained_rewriting 4). - You rely on third-party math/coding performance (o3 Math Level 5 97.8% on Epoch AI). - You need larger max output token allowances or file modality (o3 max_output_tokens 100,000 and modality includes file->text).