R1 0528 vs GPT-5.4 for Data Analysis
Winner: GPT-5.4. In our testing GPT-5.4 posts a higher Data Analysis task score (4.333 vs 4.00). That advantage is driven by GPT-5.4’s 5/5 structured_output, 5/5 strategic_analysis, stronger safety calibration (5 vs 4), plus a far larger context window and multimodal inputs — all directly relevant to real-world analysis workflows. R1 0528 is competitive on tool calling (5 vs GPT-5.4’s 4) and classification (4 vs 3) and is materially cheaper, but R1’s documented quirk of returning empty responses on structured_output makes it a riskier choice for production reporting and JSON-schema deliverables.
deepseek
R1 0528
Benchmark Scores
External Benchmarks
Pricing
Input
$0.500/MTok
Output
$2.15/MTok
modelpicker.net
openai
GPT-5.4
Benchmark Scores
External Benchmarks
Pricing
Input
$2.50/MTok
Output
$15.00/MTok
modelpicker.net
Task Analysis
Data Analysis demands reliable structured outputs (JSON/schema compliance), accurate strategic trade-off reasoning, correct tool orchestration, faithfulness to source data, long-context retrieval, and safety-aware filtering. In our testing the taskScore is the primary measure (GPT-5.4 4.333 vs R1 0528 4.00). Supporting signals: GPT-5.4 scores 5/5 on structured_output and 5/5 on strategic_analysis (our benchmarks: structured_output = JSON schema compliance; strategic_analysis = nuanced tradeoff reasoning with real numbers). R1 0528 scores 5/5 on tool_calling (function selection and sequencing) and 5/5 on agentic_planning, which helps automated pipelines and ETL agents. Practical infrastructure differences matter: GPT-5.4 accepts text+image+file inputs and has a ~1,050,000-token context window (useful for multi-file analysis), while R1 0528 is text-only with a 163,840-token window. Also note R1 0528’s quirk: empty_on_structured_output — it can return empty responses when asked for structured outputs in short tasks, which undermines its structured_output score in practice unless you manage its completion settings.
Practical Examples
- Large, multimodal dataset analysis (files + images + long logs): Choose GPT-5.4. It supports text+image+file inputs and a ~1,050,000-token context window; its structured_output 5/5 and strategic_analysis 5/5 reduce iteration for end-to-end reports. 2) Automated ETL with many tool calls (API selection, argument assembly, sequencing): Choose R1 0528. R1 scores 5/5 on tool_calling vs GPT-5.4’s 4/5 and excels at function selection and sequencing, and it’s much cheaper (output cost $2.15 per mTok vs GPT-5.4 $15 per mTok). 3) Production JSON reports for stakeholders: Prefer GPT-5.4 (structured_output 5 vs R1 4) — additionally, R1’s empty_on_structured_output quirk can cause missing payloads unless you allocate high completion tokens and guardrails. 4) Cost-sensitive batch analysis of many small CSVs: Consider R1 0528 — lower input ($0.50 vs $2.50 per mTok) and output ($2.15 vs $15 per mTok) costs make it far cheaper for high-volume jobs, provided you handle its structured-output quirk and token requirements. 5) Math-heavy or research probes: R1 posts 96.6% on MATH Level 5 (Epoch AI) and GPT-5.4 posts 95.3% on AIME 2025 (Epoch AI) and 76.9% on SWE-bench Verified (Epoch AI) — these external scores are supplementary signals on quantitative reasoning but are not the primary taskScore for Data Analysis in our suite.
Bottom Line
For Data Analysis, choose R1 0528 if cost or aggressive tool-calling automation matters (you need cheaper per-token runs and best-in-class tool orchestration). Choose GPT-5.4 if you need reliable JSON/CSV/report outputs, stronger strategic analysis, robust safety calibration, multimodal inputs, or very large-context analysis — GPT-5.4 is the overall winner in our Data Analysis tests (4.33 vs 4.00).
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.