R1 vs GPT-5 Mini

Summary of head-to-head results (our testing unless otherwise noted): - Wins: GPT-5 Mini wins 4 tests (structured_output 5 vs 4, classification 4 vs 2, long_context 5 vs 4, safety_calibration 3 vs 1). R1 wins 2 tests (creative_problem_solving 5 vs 4, tool_calling 4 vs 3). Six tests tie at the same score (strategic_analysis, constrained_rewriting, faithfulness, persona_consistency, agentic_planning, multilingual). Detailed context: - Structured output: GPT-5 Mini 5/5 vs R1 4/5 in our testing; GPT-5 Mini is tied for 1st by ranking (tied for 1st of 54) while R1 sits mid-pack (rank 26 of 54). This means GPT-5 Mini is more reliable for strict JSON schema and format compliance. - Classification: GPT-5 Mini scores 4/5 vs R1 2/5 in our testing; GPT-5 Mini ranks tied for 1st (1 of 53) and R1 ranks 51 of 53 — expect far fewer routing/misclassification errors with GPT-5 Mini. - Long context: GPT-5 Mini 5 vs R1 4 (in our testing); GPT-5 Mini is tied for 1st (long_context rank 1 of 55) and R1 is lower (rank 38 of 55). For retrieval and tasks >30K tokens, GPT-5 Mini is advantaged. - Safety calibration: GPT-5 Mini 3 vs R1 1 in our testing; GPT-5 Mini ranks 10 of 55 vs R1 at 32 of 55 — GPT-5 Mini better balances refusal vs allowed requests. - Tool calling: R1 4 vs GPT-5 Mini 3 in our testing; R1 ranks 18 of 54 vs GPT-5 Mini 47 of 54. If accurate function selection and argument sequencing matter, R1 is the stronger option. - Creative problem solving: R1 5 vs GPT-5 Mini 4 (R1 tied for 1st, GPT-5 Mini rank 9). R1 produces more non-obvious, feasible ideas in our tests. External math/programming benchmarks (Epoch AI): - On MATH Level 5 (Epoch AI), GPT-5 Mini scores 97.8% vs R1 93.1%. - On AIME 2025 (Epoch AI), GPT-5 Mini scores 86.7% vs R1 53.3%. - On SWE-bench Verified (Epoch AI), GPT-5 Mini scores 64.7%; R1 has no SWE-bench score in the payload. These external results favor GPT-5 Mini for advanced math and coding tasks. Practical implications: choose GPT-5 Mini for classification, long-context retrieval, strict output formats, safer refusals, and stronger MATH/AIME performance. Choose R1 when you prioritize tool-calling accuracy and top-tier creative idea generation despite a higher per-token cost.

Per the payload, R1 costs $0.70 per mTok input and $2.50 per mTok output; GPT-5 Mini costs $0.25 per mTok input and $2.00 per mTok output (R1 is 1.25x pricier on output). Using a conservative 50/50 input/output split: - 1M tokens/month: R1 = $1.60, GPT-5 Mini = $1.125. - 10M tokens/month: R1 = $16.00, GPT-5 Mini = $11.25. - 100M tokens/month: R1 = $160.00, GPT-5 Mini = $112.50. At scale the gap grows: switching from R1 to GPT-5 Mini saves $4.75 per 1M tokens with a 50/50 split (or $47.50 per 10M). High-volume services, consumer apps, and cost-conscious startups should prefer GPT-5 Mini for lower operational spend; teams that need R1's tool-calling accuracy and are willing to pay ~25% more per output token may accept the premium.

Choose R1 if: - Your app relies on accurate function selection or tool sequencing (R1 tool_calling 4 vs GPT-5 Mini 3; R1 ranks 18/54 vs GPT-5 Mini 47/54). - You need the strongest creative problem-solving in our tests (R1 5/5). - You can absorb a ~25% higher output cost and the model's quirks (reasoning tokens, large minimum completion token). Choose GPT-5 Mini if: - You need long-context reliability, strict structured outputs, or safer refusal behavior (long_context 5 vs 4; structured_output 5 vs 4; safety_calibration 3 vs 1). - You want lower per-token cost at scale (example: $112.50 vs $160 for 100M tokens at a 50/50 split). - You need external-benchmark math/coding strength (MATH Level 5: 97.8% vs 93.1%; AIME 2025: 86.7% vs 53.3% per Epoch AI).