R1 0528 vs GPT-5.4 for Classification
Winner: R1 0528. On our Classification task R1 scores 4 vs GPT-5.4's 3 on the 1–5 scale, and R1 ranks 1/52 while GPT-5.4 ranks 31/52. No external benchmark is available for this task, so the winner call is based on our internal results. R1’s advantages include a top classification score, best-in-class tool_calling (5) and strong multilingual and long-context handling (both 5). GPT-5.4 is stronger at structured_output (5 vs R1’s 4) and safety_calibration (5 vs 4), so it is preferable when strict JSON/schema compliance or stricter refusal behavior is required. Note R1 has a known quirk: it can return empty responses on structured_output and requires high max completion tokens; that nuance can change practical choice for schema-first workflows.
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
What Classification demands: precise label selection, correct routing decisions, consistent structured outputs for downstream systems, strong tool selection when routing (function calls), and faithfulness to the input. Because no external benchmark is present, our internal classification score is the primary signal: R1 0528 scores 4 vs GPT-5.4’s 3 on our 1–5 classification test, and R1 ranks 1/52 vs GPT-5.4 at 31/52. Supporting internal metrics explain why: R1 has tool_calling 5 (better at selecting and sequencing functions), faithfulness 5, multilingual 5 and long_context 5 — all valuable for classification across languages and long inputs. GPT-5.4’s structured_output is 5 (better JSON/schema reliability) and safety_calibration 5 (better refusal/allow behavior). Crucial quirk: R1 lists empty_on_structured_output true and needs high max completion tokens, which undermines its structured-output reliability despite its high classification score. Also factor cost and context window: R1 context_window 163,840 tokens; GPT-5.4 context_window 1,050,000 tokens — the latter helps classification over massive documents.
Practical Examples
High-volume routing (win for R1 0528): For enterprise routing that calls functions to forward tickets, R1’s classification 4 and tool_calling 5 mean more accurate label-to-action mapping and lower per-call cost (input $0.50/mTok, output $2.15/mTok) versus GPT-5.4 (input $2.50/mTok, output $15/mTok). Multilingual customer triage (win for R1 0528): R1’s multilingual 5 and faithfulness 5 make it better at consistent labeling across languages. Strict JSON schema labeling (win for GPT-5.4): If you need guaranteed JSON compliance and schema adherence, GPT-5.4’s structured_output 5 and structured_output rank 1 are superior — and R1 may return empty structured outputs despite a higher overall classification score. Safety-sensitive routing (win for GPT-5.4): For content that requires tight refusal logic or safe routing, GPT-5.4’s safety_calibration 5 is preferable to R1’s 4. Massive-document classification (win for GPT-5.4): When classifying across million-token contexts, GPT-5.4’s 1,050,000-token window outpaces R1’s 163,840 tokens.
Bottom Line
For Classification, choose R1 0528 if you need the highest raw classification accuracy on our 1–5 test (4 vs 3), superior tool calling (5), multilingual and long-context labeling, and much lower input/output costs. Choose GPT-5.4 if you require strict structured_output/JSON compliance, stronger safety calibration, or classification across extremely large contexts where a 1,050,000-token window matters.
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.