Claude Haiku 4.5 vs Claude Sonnet 4.6 for Research

What Research demands: deep tradeoff reasoning (strategic_analysis), strict fidelity to sources (faithfulness), and reliable retrieval/processing of long documents (long_context). In our 12-test suite these three are the Research task tests. On those core tests both Claude Haiku 4.5 and Claude Sonnet 4.6 score 5 in our testing, so they match on the primary measures we use for Research. Beyond those core metrics, supporting capabilities matter: safety_calibration (refusing harmful or misleading claims while permitting legitimate lines of inquiry), creative_problem_solving (novel, feasible method ideas), and raw context capacity (holds entire long papers, appendices, or datasets). Sonnet 4.6 leads on safety_calibration (5 vs 2) and creative_problem_solving (5 vs 4) in our tests and offers a 1,000,000-token context window and larger max_output_tokens (128,000) compared with Haiku’s 200,000 context and 64,000 max output — practical advantages for multi-document synthesis. Additionally, Sonnet has third-party scores useful for research-adjacent tasks: 75.2% on SWE-bench Verified and 85.8% on AIME 2025 (Epoch AI), which we cite as supplementary evidence. Haiku is positioned as a much lower-cost option (input/output costs: Haiku $1/$5 per mTok vs Sonnet $3/$15 per mTok) while preserving parity on the task core tests.

Where Claude Sonnet 4.6 shines for Research (use Sonnet):

• Deep literature synthesis across many long PDFs: 1,000,000-token context lets Sonnet ingest entire journals/appendices in one pass and produce cohesive syntheses (long_context 5 in our testing).
• Sensitive hypothesis evaluation or policy framing: Sonnet’s safety_calibration 5 (vs Haiku 2) reduces risky permissiveness when exploring controversial topics.
• Method ideation and complex experimental design: creative_problem_solving 5 (vs Haiku’s 4) yields more non-obvious, actionable approaches.
• Coding/math-heavy research tasks: Sonnet’s external results — 75.2% on SWE-bench Verified and 85.8% on AIME 2025 (Epoch AI) — support stronger performance on technical verification and competition-level math problems.

Where Claude Haiku 4.5 shines for Research (use Haiku):

• High-volume, iterative literature triage and summarization where core accuracy matters but budget is constrained: Haiku matches Sonnet on our Research core tests (both 5) while costing roughly one-third per-token (input/output costs $1/$5 vs Sonnet $3/$15).
• Fast exploratory scans and prompt pipelines that rely on tool calling and structured outputs: Haiku scores 5 on tool_calling and 4 on structured_output in our testing, matching Sonnet on those dimensions.
• Teams that need near-Sonnet-quality Research outputs but prefer lower latency and lower spend for repeated runs.

For Research, choose Claude Haiku 4.5 if you need the same top-tier task accuracy (strategic_analysis, faithfulness, long_context = 5 in our testing) at substantially lower cost (input/output $1/$5 per mTok). Choose Claude Sonnet 4.6 if you require stronger safety handling (5 vs 2), better creative problem solving (5 vs 4), larger single-pass context (1,000,000 vs 200,000 tokens), or want supporting external benchmark evidence (SWE-bench Verified 75.2% and AIME 2025 85.8% from Epoch AI).