R1 vs GPT-5.4
For most production use cases—long-context retrieval, safety-sensitive applications, and structured outputs—GPT-5.4 is the winner. R1 is the better value if you need lower cost and stronger creative problem solving, but it scores much lower on safety calibration (1 vs 5) and classification.
deepseek
R1
Benchmark Scores
External Benchmarks
Pricing
Input
$0.700/MTok
Output
$2.50/MTok
modelpicker.net
openai
GPT-5.4
Benchmark Scores
External Benchmarks
Pricing
Input
$2.50/MTok
Output
$15.00/MTok
modelpicker.net
Benchmark Analysis
Across our 12-test suite (our scores shown), GPT-5.4 wins 5 tasks, R1 wins 1, and 6 are ties. Detailed walk-through (our testing):
- Structured output: GPT-5.4 5 vs R1 4 — GPT-5.4 wins; ranks “tied for 1st” on structured output (rank 1 of 54, tied with 24 others). This matters when you need strict JSON/schema compliance.
- Classification: GPT-5.4 3 vs R1 2 — GPT-5.4 wins; R1 ranks poorly (rank 51 of 53). Expect more routing/misclassification risk on R1.
- Long context: GPT-5.4 5 vs R1 4 — GPT-5.4 wins and ranks tied for 1st (long-context rank 1 of 55); R1 is strong but lower (rank 38 of 55). For retrieval or documents >30K tokens, GPT-5.4 is the safer pick.
- Safety calibration: GPT-5.4 5 vs R1 1 — GPT-5.4 wins decisively and ranks tied for 1st on safety; R1’s low score indicates it will permit more unsafe/incorrect responses in our tests.
- Agentic planning: GPT-5.4 5 vs R1 4 — GPT-5.4 wins and is tied for 1st on agentic planning (useful for task decomposition and recovery).
- Creative problem solving: R1 5 vs GPT-5.4 4 — R1 wins here and is tied for 1st on creative problem solving; choose R1 for non-obvious ideation and brainstorming.
- Ties (both equal): strategic_analysis (5), constrained_rewriting (4), tool_calling (4), faithfulness (5), persona_consistency (5), multilingual (5). For these tasks both models perform similarly in our tests. External benchmarks (Epoch AI): GPT-5.4 scores 76.9% on SWE-bench Verified (rank 2 of 12) and 95.3% on AIME 2025 (rank 3 of 23); R1 scores 93.1% on MATH Level 5 (rank 8 of 14) and 53.3% on AIME 2025. These external results supplement our internal scores: GPT-5.4 shows top-tier code/contest performance on SWE-bench and AIME, while R1 shows strength on MATH Level 5 but trails on AIME in our payload.
Pricing Analysis
Pricing (payload): R1 input $0.7 / mTok, output $2.5 / mTok; GPT-5.4 input $2.5 / mTok, output $15 / mTok. Assuming tokens split 50/50 input/output, cost per 1M total tokens: R1 ≈ $1.60 (0.5M input = $0.35 + 0.5M output = $1.25), GPT-5.4 ≈ $8.75 (0.5M input = $1.25 + 0.5M output = $7.50). At 10M tokens/month R1 ≈ $16 vs GPT-5.4 ≈ $87.50; at 100M tokens/month R1 ≈ $160 vs GPT-5.4 ≈ $875. The payload's priceRatio is 0.1667, meaning R1 costs roughly one-sixth per-token versus GPT-5.4 on raw input+output rates. Who should care: businesses running high-volume inference (10M–100M tokens/mo) or cost-sensitive consumer apps will prefer R1 for cost savings; teams that need best-in-class long-context, safety, and multimodal support should budget for GPT-5.4.
Real-World Cost Comparison
Bottom Line
Choose R1 if: you need a much lower-cost model (input $0.7 / mTok, output $2.5 / mTok), require top-tier creative problem solving (R1 5 vs GPT-5.4 4), and can accept weaker safety and classification. Choose GPT-5.4 if: you need 1M+ token context windows, strict safety calibration (5 vs R1's 1), better structured-output compliance (5 vs 4), stronger agentic planning, and top third‑party scores on SWE-bench and AIME; budget accordingly for higher per-token cost ($2.5/$15).
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.