Claude Haiku 4.5 vs Claude Opus 4.6 for Research

Research (deep analysis, literature review, synthesis) demands three core capabilities: strategic_analysis (nuanced tradeoff reasoning), faithfulness (accurate stick-to-source synthesis), and long_context (retrieval across 30K+ tokens). In our testing both Claude Opus 4.6 and Claude Haiku 4.5 score 5/5 on those core tests. Beyond the core, research workflows also need creative_problem_solving (novel, feasible ideas), safety_calibration (handle sensitive or regulated content appropriately), robust tool_calling for multi-step workflows, and large output/context capacity for stitching many papers. Opus 4.6 provides stronger creative_problem_solving (5 vs 4) and much stronger safety_calibration (5 vs 2) in our benchmarks, plus a 1,000,000 token context window and 128,000 max output tokens versus Haiku’s 200,000 and 64,000 — practical advantages for very long syntheses. Tool_calling and faithfulness are tied at 5 for both models, and structured_output is equal (4), so for many standard literature reviews both are competent. The decisive differences for deep, high‑assurance, very long or exploratory research are Opus’s creative and safety scores and its extreme context/output capacity; the decisive advantage for cost-sensitive, high-throughput research is Haiku’s much lower per‑mTok cost (input 1 vs 5; output 5 vs 25).

Opus 4.6 strengths (where it shines):

• Multi-paper synthesis across hundreds of thousands of tokens: use Opus (context 1,000,000 / max output 128,000) to keep source fidelity across full texts.
• Generating new research directions and experiment designs: Opus’s creative_problem_solving 5 vs Haiku’s 4 yields more non-obvious, actionable ideas in our tests.
• Regulatory or safety-sensitive literature reviews (medical, legal): Opus’s safety_calibration 5 vs Haiku 2 reduces unsafe or inappropriate outputs in our testing. Claude Haiku 4.5 strengths (where it shines):
• Cost-sensitive large-scale ingestion or batch literature parsing: Haiku is far cheaper per mTok (input 1 / output 5 vs Opus 5 / 25), so run many iterations, fine-tuning prompts, or large parallel jobs at lower spend.
• Fast, short-to-medium reviews where extreme creative novelty and the largest context window aren’t required: Haiku’s strategic_analysis, faithfulness, long_context are 5/5, matching Opus on core research tests.
• Classification and routing of documents at scale: Haiku’s classification score is 4 vs Opus’s 3 in our testing, so it can be more efficient for high-volume triage tasks.

For Research, choose Claude Haiku 4.5 if you need low-cost, high-throughput literature processing, document classification, or many iterative runs (input cost 1 / output cost 5 per mTok) and your reviews fit within 200,000-token context. Choose Claude Opus 4.6 if you need maximal creative idea generation, safety-calibrated handling of sensitive material, or stitching and synthesizing extremely long sources (1,000,000-token context, 128,000 max output) and can accept ≈5× higher per-mTok cost (input 5 / output 25).