Claude Haiku 4.5 vs Claude Opus 4.7 for Research

Research requires three primary capabilities: high-quality strategic analysis, strict faithfulness to sources, and reliable long-context retrieval and reasoning. In our testing the canonical Research tests are strategic analysis, faithfulness, and long context; Claude Haiku 4.5 and Claude Opus 4.7 both score 5/5 on these, so they match on the task’s core measures. Secondary capabilities that materially affect research workflows include creative problem solving (novel, feasible ideas), constrained rewriting (compressing into exact limits), safety calibration (correctly refusing harmful prompts while permitting legitimate ones), multilingual handling, and classification/routing. In our internal scores Opus 4.7 outperforms Haiku 4.5 on creative problem solving (5 vs 4), constrained rewriting (4 vs 3), and safety calibration (3 vs 2), while Haiku 4.5 is stronger at multilingual (5 vs 4) and classification (4 vs 3). Both tie at 5/5 for tool calling, agentic planning, faithfulness, long context, and strategic analysis, and both support structured outputs at 4/5. Also note practical infrastructure differences in our data: Haiku’s context window is 200,000 tokens vs Opus’s 1,000,000 tokens—Opus offers a much larger raw context if you need to ingest extremely large corpora, though both already score 5/5 on long context in our tests.

Long literature synthesis (100k–300k tokens): both models score 5/5 on long context and faithfulness in our testing, so either will maintain retrieval accuracy and stay close to sources; choose Opus when you anticipate needing the 1,000,000-token context window or deeper hypothesis generation. Generating novel research directions and experiments: Opus 4.7 scored 5 vs Haiku 4.5’s 4 on creative problem solving in our tests, so Opus produces more non-obvious, specific, feasible ideas. Tight summarization into publication limits (e.g., 280‑char summaries or fixed abstract slots): Opus scored 4 vs Haiku’s 3 on constrained rewriting, making Opus better at guaranteed compression. Safety‑sensitive literature triage (flagging questionable experiments or refusing illicit requests): Opus has a safety calibration score of 3 vs Haiku’s 2 in our testing, reducing risky outputs. Multilingual literature review or classifying papers by topic: Haiku 4.5 scores 5 on multilingual and 4 on classification versus Opus’s 4 and 3, so Haiku is preferable when many non‑English sources or high-volume automated routing are priorities. Cost and throughput considerations: Haiku is far cheaper — $1 per million input tokens and $5 per million output tokens versus Opus at $5/$25 — making Haiku the practical choice for large-scale batch processing of corpora when the marginal gains from Opus are not essential.

For Research, choose Claude Haiku 4.5 if you need cost‑efficient, high‑throughput literature processing, stronger multilingual quality, or better classification at much lower price ($1/$5). Choose Claude Opus 4.7 if you prioritize stronger creative problem solving, tighter constrained rewriting, better safety calibration, or the largest context window (1,000,000 tokens) and you can accept the higher cost ($5/$25).