GPT-4.1 Nano vs GPT-4o-mini

Across our 12-test suite, GPT-4.1 Nano wins 4 benchmarks, GPT-4o-mini wins 2, and the remaining 6 are ties. Where GPT-4.1 Nano wins: - structured output: Nano scores 5 vs 4 (tied for 1st of 54 models; tied with 24 others). This matters for strict JSON/schema outputs and fewer post-processing errors. - constrained rewriting: Nano 4 vs 3 (rank 6 of 53), useful when compressing or fitting text to hard limits. - faithfulness: Nano 5 vs 3 (tied for 1st of 55, tied with 32 others), so Nano is less likely to hallucinate relative to 4o-mini on our tests. - agentic planning: Nano 4 vs 3 (rank 16 of 54), giving stronger task decomposition and recovery behavior in multi-step workflows. Where GPT-4o-mini wins: - classification: 4 vs Nano's 3 (GPT-4o-mini tied for 1st of 53 with 29 models), so it is better at routing/categorization tasks in our tests. - safety calibration: 4 vs Nano's 2 (rank 6 of 55 for 4o-mini), which means 4o-mini more often refuses harmful prompts while allowing legitimate ones. Ties (no clear winner): strategic analysis (both 2), creative problem solving (both 2), tool calling (both 4; both rank 18 of 54 tied with many models), long context (both 4; rank 38 of 55), persona consistency (both 4), multilingual (both 4). On third-party math benchmarks (Epoch AI): GPT-4.1 Nano scores 70% on MATH Level 5 vs GPT-4o-mini 52.6%; for AIME 2025 Nano 28.9% vs 4o-mini 6.9% — indicating Nano substantially outperforms 4o-mini on our math tests. These numbers mean: choose Nano when you need reliable structured outputs, factual fidelity, or stronger math/problem solving; choose 4o-mini for classification pipelines and stricter safety behavior. Both models tie on tool calling and long-context retrieval in our testing, so neither has an edge for function selection or 30k+ retrieval tasks.

Per-token pricing (per 1,000 tokens): GPT-4.1 Nano input $0.10 / output $0.40; GPT-4o-mini input $0.15 / output $0.60. If you only count output tokens, 1M tokens/month costs $400 (Nano) vs $600 (4o-mini) — a $200/month gap; 10M costs $4,000 vs $6,000 (gap $2,000); 100M costs $40,000 vs $60,000 (gap $20,000). If your app averages 50/50 input/output tokens, per 1M total tokens Nano is $250 (500k in @ $0.10 = $50; 500k out @ $0.40 = $200) vs 4o-mini $375 (500k in @ $0.15 = $75; 500k out @ $0.60 = $300) — $125 gap per 1M. Teams operating at millions of tokens should care: Nano reduces monthly bill by roughly one-third versus 4o-mini in output-heavy workloads; cost-conscious startups and high-volume APIs will see the largest dollar savings.

Choose GPT-4.1 Nano if you need lower cost per token, best-in-class structured output and faithfulness, stronger math/problem-solving (MATH Level 5: 70 vs 52.6) or better agentic planning. Choose GPT-4o-mini if classification accuracy and safety calibration matter more than price (classification 4 vs 3; safety calibration 4 vs 2) or you require the additional supported parameters listed for 4o-mini. If you expect very high token volumes (10M+ output tokens/month), Nano materially reduces your bill; if you run safety-critical classification flows, prefer GPT-4o-mini despite higher per-token cost.