Claude Haiku 4.5 vs Claude Sonnet 4.6 for Data Analysis

Data Analysis requires accurate strategic_analysis, reliable classification, strict structured_output, faithfulness to source data, tool_calling for pipelines, long-context handling, and safety calibration when outputs could be sensitive. On our Data Analysis tests (strategic_analysis, classification, structured_output) both Claude Haiku 4.5 and Claude Sonnet 4.6 score identically: strategic_analysis 5, classification 4, structured_output 4, yielding a task score of 4.33 for each. Use those task metrics as the baseline. Supporting strengths differ: Sonnet outscored Haiku on safety_calibration (5 vs 2) and creative_problem_solving (5 vs 4) in our testing, while both match on tool_calling (5) and faithfulness (5). Haiku’s measurable advantages are lower input/output costs (input 1 vs 3, output 5 vs 15 per mTok) and a smaller context window (200k vs 1,000,000) that still meets most long-context needs.

When Claude Sonnet 4.6 shines: iterative, safety-sensitive analyses—e.g., drilling into personally identifiable data or regulated datasets where refusing or clarifying risky requests matters (safety_calibration 5 vs 2), creative hypothesis generation for ambiguous patterns (creative_problem_solving 5 vs 4), and very large multi-file corpora that benefit from Sonnet’s 1,000,000-token context and 128,000 max output tokens. When Claude Haiku 4.5 shines: high-volume, cost-sensitive pipelines—batch data-cleaning, repeated classification or structured JSON extraction where both models match on tool_calling (5), faithfulness (5), and task tests but Haiku costs less (input 1 / output 5 per mTok vs Sonnet input 3 / output 15). Examples grounded in scores: both give identical task-level results (4.33) on strategic_analysis/classification/structured_output, so pick Sonnet when its +3 safety_calibration and +1 creative_problem_solving translate to real-world gains; pick Haiku when cost per token is the deciding factor.

For Data Analysis, choose Claude Haiku 4.5 if you need lower per-token cost (input 1, output 5 per mTok), high throughput, and the same baseline task performance (4.33) at scale. Choose Claude Sonnet 4.6 if you prioritize safety-sensitive workflows, stronger hypothesis generation (creative_problem_solving 5 vs 4), and larger context capacity (1,000,000 tokens) even at roughly 3x higher per-token cost.