R1 vs GPT-4.1
Pick GPT-4.1 for production apps that require best long-context handling, tool calling, and classification; it wins 4 clear benchmarks versus R1's 1. Choose R1 when you need much lower inference cost and stronger math/problem-solving (R1 scores 93.1% on MATH Level 5 vs GPT-4.1's 83.0%, Epoch AI). The tradeoff is steep: GPT-4.1 costs roughly 3.1x more per token.
deepseek
R1
Benchmark Scores
External Benchmarks
Pricing
Input
$0.700/MTok
Output
$2.50/MTok
modelpicker.net
openai
GPT-4.1
Benchmark Scores
External Benchmarks
Pricing
Input
$2.00/MTok
Output
$8.00/MTok
modelpicker.net
Benchmark Analysis
Summary of wins/ties (our 12-test suite + external math tests): GPT-4.1 wins 4 internal tests (constrained_rewriting 5 vs R1 4; tool_calling 5 vs R1 4; classification 4 vs R1 2; long_context 5 vs R1 4). R1 wins 1 internal test (creative_problem_solving 5 vs GPT-4.1 3). The rest tie: structured_output (4), strategic_analysis (5), faithfulness (5), safety_calibration (1), persona_consistency (5), agentic_planning (4), multilingual (5). External (Epoch AI) results: on MATH Level 5 R1 scores 93.1% vs GPT-4.1 83.0% (R1 leads); on AIME 2025 R1 53.3% vs GPT-4.1 38.3% (R1 leads); on SWE-bench Verified (Epoch AI) GPT-4.1 posts 48.5% while R1 has no SWE-bench entry in the payload. What this means for tasks: - Long-context and tool workflows: GPT-4.1’s 5/5 long_context and 5/5 tool_calling (tied for top ranks) translate to more reliable function selection and retrieval over 30K+ tokens (GPT-4.1 has a 1,047,576 token window vs R1's 64k). - Classification and constrained rewriting: GPT-4.1’s higher scores (classification 4 vs 2; constrained_rewriting 5 vs 4) indicate fewer routing errors and better handling of strict character/format limits. - Creative problem-solving and advanced math: R1’s 5/5 creative_problem_solving and 93.1% on MATH Level 5 (Epoch AI) suggest stronger idea generation and higher math-competition accuracy in our tests. - Safety & faithfulness: both models tie at faithfulness 5 and safety_calibration 1 in our suite—neither has a safety edge per these scores. Rankings context: GPT-4.1 ranks tied for 1st on long_context and tool_calling across 54–55 models; R1 ranks top in creative_problem_solving and scores higher on external math benchmarks. Use the external Epoch AI numbers when math/coding accuracy is a primary selection factor.
Pricing Analysis
R1 input $0.7/mTok and output $2.5/mTok; GPT-4.1 input $2/mTok and output $8/mTok. Using a 50/50 input/output split, 1M tokens (1,000 mTok) costs: R1 = $1,600 (500mTok0.7 + 500mTok2.5) vs GPT-4.1 = $5,000 (500mTok2 + 500mTok8). At 10M tokens: R1 ≈ $16,000 vs GPT-4.1 ≈ $50,000. At 100M tokens: R1 ≈ $160,000 vs GPT-4.1 ≈ $500,000. The price ratio is ~0.3125 (R1 costs ~31% of GPT-4.1 for comparable I/O mix). High-volume services, cost-sensitive startups, and apps with predictable, short outputs should favor R1; teams prioritizing long-context reasoning, complex tool workflows, or highest classification accuracy may accept GPT-4.1's higher bill.
Real-World Cost Comparison
Bottom Line
Choose R1 if: you need dramatically lower token costs (R1 input $0.7/mTok, output $2.5/mTok), require strong math/creative problem-solving (93.1% MATH Level 5, 53.3% AIME 2025 per Epoch AI), and can work within a 64k context window. Choose GPT-4.1 if: you need best-in-class long-context (1,047,576 token window), robust tool calling, and higher classification or constrained-rewriting accuracy (GPT-4.1 wins 4 internal tests), and your budget can absorb roughly 3x the per-token cost.
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.