R1 0528 vs GPT-5.4 for Structured Output
GPT-5.4 is the clear winner for Structured Output. In our testing GPT-5.4 scores 5/5 vs R1 0528's 4/5 on the structured_output benchmark and ranks tied for 1st (rank 1 of 52) while R1 0528 ranks 26 of 52. GPT-5.4 delivers stronger JSON schema compliance and format adherence. R1 0528 is materially cheaper (input $0.50/mTok, output $2.15/mTok vs GPT-5.4 input $2.50/mTok, output $15/mTok) and has superior tool_calling (5 vs 4), but R1 0528 has a critical quirk: in our testing it can return empty responses on structured_output and consumes reasoning tokens that eat output budget, reducing reliability for short, strict-schema tasks.
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
Structured Output tests JSON schema compliance and strict format adherence. Key capabilities: precise response_format support, deterministic token-level control, reliable structured_outputs behavior, sufficient output length, and safety calibration for permitting legitimate schema outputs. There is no external benchmark provided for this task, so our internal structured_output score is primary: GPT-5.4 = 5/5 (rank 1 of 52), R1 0528 = 4/5 (rank 26 of 52). Supporting signals: GPT-5.4 has a much larger context window (1,050,000 tokens) and multimodal input (text+image+file->text), aiding long or file-based extractions; it also has higher safety_calibration (5 vs R1's 4), reducing risky refusals or unsafe format changes. R1 0528 supports extensive parameters (response_format, structured_outputs) and scored higher on tool_calling (5 vs GPT's 4), which explains why it's strong for function-argument workflows. However, R1 0528's documented quirks in our testing—empty_on_structured_output and use of reasoning tokens that consume output budget, plus a 1000-token minimum completion behavior—meaningfully weaken its practical reliability for many structured-output jobs unless you design around those constraints.
Practical Examples
Scenario A — Strict API contract (multiple required fields, exact JSON types): GPT-5.4 (5/5) is preferred — in our testing it more reliably adheres to schema and formatting. Scenario B — Large multimodal extraction (parse values from long documents or images into JSON): GPT-5.4 is better because it accepts text+image+file inputs and has a 1,050,000-token context window, reducing truncation risk. Scenario C — Function-first pipeline where the model must choose functions and populate precise arguments: R1 0528 shines on tool_calling (5 vs GPT's 4) and can be cheaper per token (output $2.15 vs $15). Caveat: in our testing R1 0528 sometimes returns empty results on structured_output and requires high max completion tokens (min_max_completion_tokens = 1000), so a short, strict schema job may fail or consume extra budget. Scenario D — High-volume, cost-sensitive micro-JSON replies: R1 0528 reduces token costs (input $0.50/mTok, output $2.15/mTok) but expect engineering workarounds for its empty-response and reasoning-token quirks. Scenario E — Safety-sensitive schemas (allow/deny rules): GPT-5.4's safety_calibration is 5 vs R1's 4 in our tests, giving GPT-5.4 an edge in consistent, policy-aligned outputs.
Bottom Line
For Structured Output, choose R1 0528 if you need lower per-token cost and stronger tool_calling (5/5) and you can accommodate its quirks (empty responses on structured_output, high min completion tokens). Choose GPT-5.4 if you require the most reliable JSON schema compliance and format adherence (5/5, rank 1 of 52), multimodal/file input, and higher safety calibration—even at a higher cost (input $2.50/mTok, output $15/mTok).
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.