GPT-5.4 vs Grok 4 for Data Analysis

What Data Analysis demands: the task focuses on strategic analysis (tradeoffs and numeric reasoning), classification (accurate labeling/routing), and structured output (precise JSON/table outputs). Key capabilities: - Structured-output fidelity: required for machine-readable deliverables (JSON/CSV). - Classification accuracy: for routing, tagging, and label generation. - Strategic numeric reasoning: for recommendations, tradeoffs, and summary metrics. - Tool calling and agentic planning: for multi-step ETL, retries, and error recovery. - Safety/fraud calibration: to avoid producing misleading analyses. Evidence from our tests: both models tie on the composite Data Analysis score (4.333) and on strategic analysis (5 vs 5). GPT-5.4 scores higher on structured output (5 vs 4) and agentic planning (5 vs 3) and has external scores of 76.9% on SWE-bench Verified and 95.3% on AIME 2025 (Epoch AI). Grok 4 scores higher on classification (4 vs 3). Use the internal 1–5 proxies to understand strengths: GPT-5.4 is better at producing exact schema-compliant outputs and multi-step plans; Grok 4 is preferable for labeling-heavy tasks.

Where GPT-5.4 shines: - Delivering production JSON reports to downstream systems: structured output 5 vs 4 means fewer schema fixes and parsing errors. - Multi-step ETL with failure recovery: agentic planning 5 vs 3 reduces manual orchestration. - Safety-sensitive dashboards and regulatory summaries: safety calibration 5 vs 2 lowers risky or disallowed content. - Large-context analysis of long documents or datasets: context_window 1,050,000 vs 256,000 supports much larger in-memory context. Where Grok 4 shines: - High-volume labeling and routing pipelines: classification 4 vs 3 produces more accurate categorizations. - Standard strategic tradeoff analyses: strategic analysis ties 5 vs 5, so Grok 4 matches GPT-5.4 on nuanced numeric reasoning. - Lower-latency, mid-context tasks where a 256k window is sufficient and classification is central. Cost/context facts: GPT-5.4 input_cost_per_mtok=2.5, output_cost_per_mtok=15; Grok 4 input_cost_per_mtok=3, output_cost_per_mtok=15—Grok 4 has a slightly higher input cost per m-tok in the payload.

For Data Analysis, choose GPT-5.4 if you need production-grade, schema-compliant outputs, stronger multi-step planning, higher safety calibration, or large-context analysis (GPT-5.4: structured output 5, agentic planning 5, safety calibration 5; SWE-bench 76.9% and AIME 95.3% per Epoch AI). Choose Grok 4 if your primary need is more accurate classification/routing (classification 4 vs GPT-5.4's 3) or you prefer Grok 4's cost/profile for mid-size context workloads.