Claude Sonnet 4.6 vs Grok 4 for Data Analysis

What Data Analysis demands: clear structured output, reliable classification, and strategic tradeoff reasoning (our test names: structured_output, classification, strategic_analysis). With no external benchmark present, the primary signal is our taskScore and component metrics. Both models score identically on the overall Data Analysis task (4.333333333333333), so the deciding evidence comes from subtests: Sonnet 4.6 leads in tool_calling (5 vs 4), safety_calibration (5 vs 2), and agentic_planning (5 vs 3) — traits important for orchestrating ETL steps, automating iterative analysis, and safely handling PII. Grok 4 wins constrained_rewriting (4 vs 3), which matters when producing terse dashboard text or character-limited exports. They tie on structured_output, classification, strategic_analysis, faithfulness, long_context, persona_consistency, and multilingual, so both are competent at schema compliance, labeling, and high-context retrieval. Other practical differences in the payload: Sonnet 4.6 has a 1,000,000-token context window vs Grok 4's 256,000, and Grok 4 accepts file inputs (text+image+file->text) while Sonnet accepts text+image->text. Input/output costs per mTok are equal in the payload (input_cost_per_mtok = 3, output_cost_per_mtok = 15). Use these component scores to pick the model that matches your workflow needs.

When Claude Sonnet 4.6 shines: - Orchestrating multi-step pipelines that call tools (score: tool_calling 5 vs Grok 4): e.g., run SQL, call a plotting tool, and re-run transforms based on that plot. - Iterative, safety-sensitive analysis (safety_calibration 5 vs 2): cleaning PII, redacting fields, or making privacy-preserving recommendations. - Complex project decomposition and failure recovery (agentic_planning 5 vs 3): end-to-end feature extraction with fallback plans when a data source fails. When Grok 4 shines: - Producing ultra-compact summaries or character-limited reports (constrained_rewriting 4 vs 3): compressing analysis into preset UI widget limits. - File-first ingestion workflows (payload modality: text+image+file->text): one-shot transforms from uploaded spreadsheets or logs. Shared strengths: both tie on structured_output and classification, so both reliably emit JSON schemas and label data correctly; both score long_context 5, so they handle large transcripts or long data dumps similarly well.

For Data Analysis, choose Claude Sonnet 4.6 if you need stronger tool orchestration, safer handling of sensitive data, and robust agentic planning (Sonnet wins 4 vs 1 on our 12 subtests). Choose Grok 4 if your priority is constrained rewriting or direct file ingestion (Grok wins constrained_rewriting and accepts file inputs). Both models tie on the overall task score (4.333333333333333) and share many strengths, so pick by the subtest gaps and modality/context needs.