Claude Haiku 4.5 vs Devstral Small 1.1 for Data Analysis

What Data Analysis demands: precise tradeoff reasoning, reliable structured outputs (JSON/schema), accurate classification/routing, multi-step tool orchestration, and handling large input contexts (logs, notebooks, datasets). Because there is no external benchmark for this comparison, our internal task score is the primary signal. On the three task tests we run (strategic_analysis, classification, structured_output) Claude Haiku 4.5 outperforms Devstral Small 1.1 largely by excelling at strategic_analysis (5 vs 2). Classification and structured_output are tied (4 vs 4), so the decisive differences come from strategic tradeoffs plus supporting capabilities: Haiku 4.5 scores 5 vs 4 on tool_calling, 5 vs 4 on long_context, and 5 vs 4 on faithfulness — all directly relevant to complex data-analysis pipelines. Devstral Small 1.1 has adequate structured-output and classification ability but scores lower on strategic_analysis (2), agentic_planning (2), and creative_problem_solving (2), which limits its performance on exploratory or ambiguous analysis tasks. Finally, compare cost and context: Haiku 4.5 has a 200,000-token context window and output cost $5/mTok, while Devstral Small 1.1 has a 131,072-token window and output cost $0.3/mTok (~16.67x cheaper per output mTok).

Where Claude Haiku 4.5 shines (use Haiku when accuracy and complex workflows matter):

• Multi-file, multi-step analysis (e.g., combine a 50k-token research notebook, a 30k-token dataset excerpt, and API results) — Haiku's long_context 5 and tool_calling 5 reduce missed context and sequencing errors. (Task scores: strategic_analysis 5 vs 2; tool_calling 5 vs 4; long_context 5 vs 4.)
• Decision-focused recommendations requiring tradeoffs and numeric reasoning (budget vs accuracy, cohort comparisons) — Haiku's strategic_analysis 5 drives clearer, defensible tradeoffs.
• Pipelines that must avoid hallucination when referencing source data — Haiku's faithfulness 5 helps maintain source fidelity.

Where Devstral Small 1.1 shines (use Small 1.1 when cost and throughput matter):

• High-volume classification or schema-compliant extraction jobs where each unit is short (classification 4, structured_output 4 match Haiku) but you need to scale cheaply — output cost $0.3/mTok vs Haiku $5/mTok.
• Lightweight, repeatable ETL steps or batching many small structured-output tasks where strategic nuance is less important but budget is tight.

Concrete numeric grounding: our Data Analysis task score is 4.33 for Claude Haiku 4.5 vs 3.33 for Devstral Small 1.1. Haiku ranks 11 of 52 for this task; Devstral ranks 40 of 52 in our tests.

For Data Analysis, choose Claude Haiku 4.5 if you need high-quality tradeoff reasoning, multi-step tool workflows, and large-context retrieval (task score 4.33; strategic_analysis 5; tool_calling 5; long_context 5). Choose Devstral Small 1.1 if you must run many short classification or structured-output jobs on a tight budget — it matches Haiku on classification and structured_output (both 4) but is ~16.67x cheaper per output mTok ($0.30 vs $5.00).