R1 0528 vs GPT-5.2

Overview: In our 12-test suite R1 0528 (deepseek) and GPT-5.2 (openai) tie on many capabilities but split the decisive wins: GPT-5.2 wins strategic_analysis (5 vs 4), creative_problem_solving (5 vs 4), and safety_calibration (5 vs 4); R1 0528 wins tool_calling (5 vs 4). Ties: structured_output (4 vs 4), constrained_rewriting (4 vs 4), faithfulness (5 vs 5), classification (4 vs 4), long_context (5 vs 5), persona_consistency (5 vs 5), agentic_planning (5 vs 5), and multilingual (5 vs 5). Detailed implications: - Strategic analysis: GPT-5.2 scores 5 vs R1's 4 and ranks tied for 1st (GPT) versus R1 rank 27 of 54 — expect GPT-5.2 to be measurably stronger on nuanced tradeoff reasoning and numeric decompositions. - Creative problem solving: GPT-5.2 5 vs R1 4 (GPT tied for 1st); pick GPT-5.2 when you need non-obvious, actionable ideas. - Safety calibration: GPT-5.2 5 vs R1 4 (GPT tied for 1st; R1 rank 6) — GPT-5.2 better at refusing harmful requests while permitting legitimate ones in our tests. - Tool calling: R1 0528 5 vs GPT-5.2 4; R1 is tied for 1st in tool_calling (tied with 16 models), while GPT ranks 18 of 54 — R1 selects and sequences functions more accurately in our tests. - Long context, persona_consistency, faithfulness: both score 5, tied for top ranks — both handle 30K+ contexts and persona maintenance well in our suite. External benchmarks (Epoch AI): R1 scores 96.6% on MATH Level 5 (Epoch AI) and 66.4% on AIME 2025; GPT-5.2 scores 73.8% on SWE-bench Verified (Epoch AI) and 96.1% on AIME 2025 (Epoch AI). That means GPT-5.2 leads on the AIME external test (96.1% vs R1's 66.4%), while R1 dominates MATH Level 5 in our payload. Practical caveats: R1 has engineering quirks — it uses reasoning tokens that consume output budget on short tasks and “returns empty responses” on structured_output/constrained_rewriting unless high completion-token settings are used; account for these when benchmarking structured JSON or short output flows. GPT-5.2 supports multimodal inputs and a larger declared context window (400,000 tokens / 128,000 max output) per the payload, which matters for file/image+text workloads.

Per the payload, R1 0528 charges $0.50 per mTok input and $2.15 per mTok output (combined $2.65/mTok if input≈output). GPT-5.2 charges $1.75 input and $14.00 output (combined $15.75/mTok). At typical monthly volumes assuming equal input/output tokens: 1M tokens (1,000 mTok) costs R1 ≈ $2,650 vs GPT-5.2 ≈ $15,750; 10M tokens ≈ $26,500 vs $157,500; 100M tokens ≈ $265,000 vs $1,575,000. The priceRatio in the payload (0.1536) means R1's per-token bill is ~15.36% of GPT-5.2's — roughly a 6.5× cost gap. Enterprises or high-volume apps (chat fleets, embeddings at scale, heavy generation) should care deeply about this gap; small-scale prototypes or safety-critical apps may accept GPT-5.2's higher price for its edge on specific benchmarks.

Choose R1 0528 if: - You run high-volume or cost-sensitive production (R1 combined ≈ $2.65/mTok vs GPT-5.2 ≈ $15.75/mTok). - You rely on accurate tool calling, function selection, and sequencing (R1 scores 5 on tool_calling and is tied for 1st). - You can tolerate or work around its structured-output and reasoning-token quirks (use high max completion tokens and test structured-output flows). Choose GPT-5.2 if: - Your priority is top performance on nuanced reasoning, creative problem solving, and safety (GPT-5.2 scores 5 vs R1's 4 in strategic_analysis, creative_problem_solving, and safety_calibration and ranks tied for 1st in those areas). - You need best-in-class performance on AIME-style problems (GPT-5.2 scores 96.1% on AIME 2025, Epoch AI). - You accept higher token costs for potentially more robust handling of safety and creative tasks.