R1 0528 vs GPT-5 Nano

Summary (12-test suite, our testing): R1 0528 wins 7 tests, GPT-5 Nano wins 1, and 4 tests tie. Wins and scores (our testing):

• Tool calling: R1 5 vs GPT-5 Nano 4 — R1 ties for 1st (tied with 16 models of 54). This translates to more accurate function selection, argument formatting, and sequencing in integrations. Note: R1 has a quirk that it may return empty responses on structured_output and agentic_planning in short tasks; test prompt lengths accordingly.
• Faithfulness: R1 5 vs GPT-5 Nano 4 — R1 tied for 1st (1 of 55), meaning R1 is more likely to stick to source material in our evaluations.
• Classification: R1 4 vs GPT-5 Nano 3 — R1 tied for 1st (with 29 others of 53), so routing and categorization were more accurate for R1 in our runs.
• Persona consistency: R1 5 vs GPT-5 Nano 4 — R1 tied for 1st, important for agents and assistants that must maintain voice and resist injection.
• Agentic planning: R1 5 vs GPT-5 Nano 4 — R1 tied for 1st (with 14 others), showing stronger goal decomposition and failure recovery in our tasks.
• Constrained rewriting: R1 4 vs GPT-5 Nano 3 — R1 ranks 6 of 53 vs GPT rank 31, so R1 handles strict character/space-limited rewrites better.
• Creative problem solving: R1 4 vs GPT-5 Nano 3 — R1 rank 9 of 54 vs GPT rank 30, indicating more feasible, non-obvious ideas in our tests.
• Structured output (JSON/schema compliance): GPT-5 Nano 5 vs R1 4 — GPT-5 Nano ties for 1st (with 24 others of 54). If you require precise JSON or schema adherence, GPT-5 Nano is superior in our runs — note R1 also has a known quirk of returning empty responses on structured_output.
• Ties (no clear winner in our tests): strategic_analysis 4/4 (rank 27/54), long_context 5/5 (both tied for 1st across 55 models), safety_calibration 4/4 (rank 6/55 tied), multilingual 5/5 (tied for 1st). Practically, both models handle long-context retrieval (~30k+ tokens), multilingual tasks, and refuse/allow calibration similarly in our suite. External math benchmarks (Epoch AI): on MATH Level 5 (Epoch AI) R1 scores 96.6% vs GPT-5 Nano 95.2% (R1 ranks 5 of 14 vs GPT rank 7 of 14). On AIME 2025 (Epoch AI) GPT-5 Nano scores 81.1% vs R1 66.4% (GPT ranks 14 of 23 vs R1 16 of 23) — GPT-5 Nano outperforms R1 on harder AIME-style problems in these external measures. Context/quirks: R1’s strengths come with operational caveats — it uses reasoning tokens, has a min_max_completion_tokens of 1000, and can return empty outputs on certain structured tasks unless prompted with higher max completion tokens. GPT-5 Nano supports multimodal inputs (text+image+file) and a larger context window (400k vs R1's 163,840), which matters for file-heavy or multimodal developer tools.

Output pricing: R1 0528 charges $2.15 per 1k output tokens; GPT-5 Nano charges $0.40 per 1k output tokens (price ratio ≈5.375). At output-only volumes: 1M tokens/month = R1 $2,150 vs GPT-5 Nano $400; 10M = R1 $21,500 vs GPT-5 Nano $4,000; 100M = R1 $215,000 vs GPT-5 Nano $40,000. If you include typical input tokens and assume input ≈ output, add input costs (R1 input $0.50/1k, GPT-5 Nano $0.05/1k): combined per-1k becomes $2.65 (R1) vs $0.45 (GPT-5 Nano). Combined totals then are 1M: $2,650 vs $450; 10M: $26,500 vs $4,500; 100M: $265,000 vs $45,000. Who should care: startups, consumer apps, and any high-volume deployer will feel the gap — at 10–100M tokens/month the choice changes total infrastructure spend by tens to hundreds of thousands of dollars. If your app is latency-sensitive or cost-constrained (chatbots, mobile clients, heavy inference), GPT-5 Nano materially lowers OPEX; if you need top tool-calling, agentic planning, or strict faithfulness at smaller scale, R1 may justify the premium.

Choose R1 0528 if you need best-in-class tool calling, agentic planning, faithfulness, persona consistency, or stronger classification and constrained-rewrite capabilities — and you can accept higher per-token costs and R1’s prompt/length quirks. Choose GPT-5 Nano if you must minimize inference cost, require strict structured-output/JSON compliance, need multimodal inputs or very large context (400k tokens), or want better AIME-level math performance per dollar. For high-volume, cost-sensitive production (chatbots, consumer APIs), GPT-5 Nano is the pragmatic default; for mission-critical integrator agents or workflows where the 7 benchmark wins matter, R1 can justify the premium.