Claude Haiku 4.5 vs R1 0528

Summary of test-by-test results from our 12-test suite (scores 1–5):

• strategic_analysis: Claude Haiku 4.5 = 5, R1 0528 = 4. Haiku wins this test and ranks tied for 1st with 25 other models, meaning it handles nuanced tradeoff reasoning (real-number reasoning) better in our testing. Use this for financial, policy, or multi-constraint decisions.
• constrained_rewriting: Haiku = 3, R1 = 4. R1 wins and ranks 6th of 53 (shared), so it is measurably better at strict compression/hard character-limit rewriting.
• safety_calibration: Haiku = 2, R1 = 4. R1 wins and ranks 6th of 55; Haiku ranks 12th. In our testing R1 refuses harmful requests more reliably while permitting legitimate ones more accurately.
• creative_problem_solving: both score 4 (tie). Both rank 9 of 54 in our dataset: good for non-obvious, feasible idea generation.
• tool_calling: both score 5 (tie). Both tied for 1st on tool calling—strong at function selection, argument accuracy, and sequencing in our tests.
• faithfulness: both score 5 (tie). Tied for 1st — both stick to source material without hallucinating in our evaluations.
• classification: both score 4 (tie). Each tied for 1st in classification tests — reliable routing/categorization.
• long_context: both score 5 (tie). Each tied for 1st on 30K+ retrieval accuracy—suitable for long documents.
• persona_consistency: both 5 (tie). Tied for 1st on maintaining character and resisting injection.
• agentic_planning: both 5 (tie). Tied for 1st — both decompose goals and recover from failures effectively in our tests.
• multilingual: both 5 (tie). Tied for 1st — equivalent quality across languages in our suite.
• structured_output: both 4 (tie). Both rank 26 of 54 — acceptable JSON/schema compliance, though R1 has a noted quirk: it returns empty responses on structured_output unless given large max completion tokens (see quirks). External math benchmarks (supplementary, Epoch AI): R1 0528 scores 96.6% on MATH Level 5 and 66.4% on AIME 2025 (Epoch AI). Claude Haiku 4.5 has no external math scores in the payload. Overall wins: R1 wins more individual tests (constrained_rewriting and safety_calibration), Claude Haiku 4.5 wins strategic_analysis; nine tests tie. In practical terms, R1’s wins plus lower cost favor high-volume and safety-sensitive deployments; Haiku’s strategic edge favors complex decisioning tasks.

Unit prices: Claude Haiku 4.5 — $1.00 per mTok input, $5.00 per mTok output. R1 0528 — $0.50 per mTok input, $2.15 per mTok output. Translate to common volumes (1 mTok = 1,000 tokens):

• If all tokens are output (1M tokens = 1,000 mTok): Claude output = $5,000; R1 output = $2,150.
• Input-only (1M tokens): Claude input = $1,000; R1 input = $500.
• Balanced 50/50 IO split (1M tokens total): Claude = $3,000 (500 mTok input $500 + 500 mTok output $2,500); R1 = $1,325 (500 mTok input $250 + 500 mTok output $1,075). Scale linearly: at 10M tokens multiply costs by 10; at 100M multiply by 100. The cost gap (priceRatio ≈ 2.33) means teams at 10M–100M tokens/month save thousands to hundreds of thousands of dollars with R1 — critical for high-volume APIs, chat fleets, or inference pipelines. Small-scale experiments or tasks that demand Haiku’s specific strategic strengths may justify the higher per-token cost.

Choose Claude Haiku 4.5 if you need best-in-class strategic analysis and very large context support and you can accept higher per-token costs — pick it for finance, policy modeling, or scenarios where nuanced tradeoff reasoning matters. Choose R1 0528 if budget and safety/constrained-rewriting are priorities: it wins constrained_rewriting and safety_calibration in our tests, is cheaper ($0.50/$2.15 per mTok vs $1/$5.00), and posts very strong external math results (MATH Level 5 96.6%, AIME 2025 66.4% per Epoch AI). If you operate at 10M+ tokens/month or need stricter safety/compression behavior, R1 is the pragmatic default.