Claude Haiku 4.5 vs Claude Opus 4.7

All statements below are from our 12-test suite. Wins, ties, and losses follow our measured scores. 1) Creative problem solving: Opus 4.7 = 5 vs Haiku 4.5 = 4 — Opus wins; Opus is tied for 1st (tied with 8 others) meaning it sits in the top tier for generating non‑obvious feasible ideas. 2) Strategic analysis: both score 5 — tie; both are tied for 1st with 26 others, so expect strong tradeoff reasoning from either model. 3) Structured output: both score 4 — tie; both rank 26 of 55, indicating reliable JSON/schema adherence but not unique leadership. 4) Persona consistency: both score 5 — tie; both tied for 1st, so both keep character and resist prompt injection well. 5) Agentic planning: both score 5 — tie; both tied for 1st, so both decompose goals and plan recovery effectively. 6) Multilingual: Haiku 4.5 = 5 vs Opus 4.7 = 4 — Haiku wins; Haiku is tied for 1st (with 34 others), so it provides stronger parity in non-English output. 7) Long context: both score 5 — tie; both tied for 1st on 30K+ retrieval accuracy, and Opus offers a larger raw context window (1,000,000 tokens vs Haiku’s 200,000) which matters if you need extremely long context. 8) Faithfulness: both 5 — tie; both tied for 1st, so both adhere to source material. 9) Tool calling: both 5 — tie; both tied for 1st, so function selection and argument accuracy are equally strong in our tests. 10) Classification: Haiku 4.5 = 4 vs Opus 4.7 = 3 — Haiku wins; Haiku ranks tied for 1st in classification in our set, so it routes and categorizes slightly better. 11) Safety calibration: Opus 4.7 = 3 vs Haiku 4.5 = 2 — Opus wins; Opus ranks 10 of 56 (tied with 2 others) vs Haiku at rank 13, indicating Opus is more likely to correctly refuse harmful requests while permitting legitimate ones. 12) Constrained rewriting: Opus 4.7 = 4 vs Haiku 4.5 = 3 — Opus wins and ranks 6 of 55, so Opus is substantially better when you must compress content into hard character limits. In short: many core strengths (tool calling, strategic analysis, long context, agentic planning, faithfulness, persona consistency) are ties — Haiku takes classification and multilingual; Opus wins creative problem solving, constrained rewriting, and safety calibration. Opus’s wins are concentrated in higher-difficulty creative and safety tasks; Haiku’s wins favor practical classification and multilingual parity.

Per-unit pricing: Claude Haiku 4.5 is $1 per million input tokens and $5 per million output tokens; Claude Opus 4.7 is $5 per million input and $25 per million output. If you assume a 50/50 split of input vs output tokens, Haiku costs about $3.00 per 1M total tokens (0.5M input at $1 = $0.50; 0.5M output at $5 = $2.50). Under the same split, Opus costs about $15.00 per 1M total tokens (0.5M input at $5 = $2.50; 0.5M output at $25 = $12.50). Scaling that to steady monthly volumes: 1M tokens/month → Haiku $3, Opus $15; 10M → Haiku $30, Opus $150; 100M → Haiku $300, Opus $1,500. The 5× unit-price gap (Opus is 5× more expensive on both input and output) matters for high-volume chat, search, or multi-user SaaS — teams with tight margins or heavy token use should prefer Haiku. Teams that require Opus’s edge on specific tasks (creative/problem-solving, hard-character-limit compression, or stricter safety calibration) may justify the higher cost for those workloads.

Choose Claude Haiku 4.5 if you need cost-efficient, production-grade performance across long context, tool calling, agentic planning and faithfulness while keeping costs low (Haiku: $1 input / $5 output per 1M tokens). Ideal for large‑scale chat, multilingual assistants, and classification-heavy pipelines. Choose Claude Opus 4.7 if your priority is top-tier creative problem solving, tight constrained rewrites (hard character limits), or stronger safety calibration and you can absorb ~5× higher per-token costs (Opus: $5 input / $25 output per 1M tokens). Ideal for research prototypes, high-stakes content generation, or tasks where those specific quality gains are worth the price.