Claude Haiku 4.5 vs R1

Overview — wins/ties: Claude Haiku 4.5 wins 5 tests (tool_calling, classification, long_context, safety_calibration, agentic_planning); R1 wins 2 tests (constrained_rewriting, creative_problem_solving); 5 tests tie (structured_output, strategic_analysis, faithfulness, persona_consistency, multilingual). Details: - Tool calling: Haiku 5 vs R1 4. Haiku ties for 1st on tool_calling ("tied for 1st with 16 other models out of 54 tested"), while R1 ranks 18 of 54; this means Haiku is measurably better at selecting functions, arguments, and sequencing for integrated tool workflows. - Classification: Haiku 4 vs R1 2; Haiku is tied for 1st (tied with 29 others) while R1 ranks 51 of 53 — R1 is weak for routing/categorization tasks. - Long context: Haiku 5 vs R1 4; Haiku ties for 1st (tied with 36 others) and also offers a 200,000-token context window (vs R1's 64,000), so Haiku will retrieve and reason over long documents more reliably. - Agentic planning: Haiku 5 vs R1 4; Haiku ties for 1st, indicating stronger goal decomposition and recovery in our tests. - Safety calibration: Haiku 2 vs R1 1; Haiku ranks 12 of 55 vs R1 32 of 55 — Haiku is better at refusing harmful prompts while permitting legitimate ones in our evaluation. - Constrained rewriting: R1 4 vs Haiku 3; R1 ranks 6 of 53 on this test (Haiku rank 31), so R1 is preferable for aggressive compression within hard character limits. - Creative problem solving: R1 5 vs Haiku 4; R1 ties for 1st (tied with 7 others) while Haiku ranks 9 of 54 — R1 produces more non-obvious, feasible ideas in our tests. - Ties: structured_output (both 4), strategic_analysis (both 5), faithfulness (both 5), persona_consistency (both 5), and multilingual (both 5) — on these dimensions either model delivers comparable quality per our 12-test suite. External math benchmarks: R1 posts 93.1% on MATH Level 5 and 53.3% on AIME 2025 (Epoch AI) — we report these Epoch AI scores as supplementary evidence that R1 is strong on high-difficulty math problems in those external tests.

Pricing per mTok: Claude Haiku 4.5 charges $1 input / $5 output; R1 charges $0.7 input / $2.5 output. Assuming a 50/50 split of input vs output tokens, 1M total tokens (1,000 mTok) costs: Haiku ≈ $3,000 (500*$1 + 500*$5) and R1 ≈ $1,600 (500*$0.7 + 500*$2.5). At 10M tokens/month multiply by 10 (Haiku $30,000 vs R1 $16,000). At 100M tokens/month multiply by 100 (Haiku $300,000 vs R1 $160,000). The payload’s priceRatio is 2, reflecting Haiku roughly doubling R1’s cost in typical mixes. Teams running high-volume inference or tight margins should care: R1 saves ~47% on token bill under a 50/50 I/O split. Teams that need Haiku’s advantages (200k context, multimodal input, stronger tool-calling and classification) may justify the higher spend.

Choose Claude Haiku 4.5 if you need: - Reliable tool calling and function orchestration (Haiku 5 vs R1 4; Haiku tied for 1st on tool_calling). - Very long-context retrieval and multimodal input (200k window vs R1 64k; Haiku long_context 5 vs R1 4). - Strong classification, agentic planning and better safety calibration in our tests. Expect to pay roughly ~2x R1 for typical I/O mixes. Choose R1 if you need: - Lower cost at scale (input/output $0.7/$2.5 vs Haiku $1/$5 per mTok; ~47% lower bill under a 50/50 split). - Better constrained rewriting and creative problem solving (R1 wins those tests and ranks 6th and tied for 1st respectively). - Competitive faithfulness, persona consistency, and multilingual performance at a lower price point. If you must compress text to tight limits or want higher ideation output per dollar, pick R1.