GPT-4o-mini vs GPT-5 Nano

Summary: in our 12-test suite GPT-5 Nano wins 7 tests, GPT-4o-mini wins 1 (classification), and 4 tests tie. Key head-to-heads from our testing: - Long context: GPT-5 Nano 5 vs GPT-4o-mini 4 — GPT-5 Nano is tied for 1st on long-context (tied with 36 others) and supports a 400k context window vs GPT-4o-mini’s 128k; expect better retrieval and accuracy on >30k-token documents when using GPT-5 Nano. - Structured output: GPT-5 Nano 5 vs GPT-4o-mini 4 — GPT-5 Nano ties for 1st in structured output (Epoch ranking: tied for 1st with 24 others); this matters for reliable JSON/schema compliance. - Multilingual: GPT-5 Nano 5 vs GPT-4o-mini 4 — GPT-5 Nano is tied for 1st in multilingual (tied with 34 others); better for non-English parity. - Strategic analysis: GPT-5 Nano 4 vs GPT-4o-mini 2 — GPT-5 Nano gives more nuanced tradeoff reasoning in our tests. - Faithfulness: GPT-5 Nano 4 vs GPT-4o-mini 3 — GPT-5 Nano sticks to source material more reliably in our tasks. - Agentic planning: GPT-5 Nano 4 vs GPT-4o-mini 3 — GPT-5 Nano produces stronger goal decomposition and failure recovery traces in our agentic tests. - Creative problem solving: GPT-5 Nano 3 vs GPT-4o-mini 2 — GPT-5 Nano produced more viable non-obvious ideas on our prompts. - Tool calling: tied 4 vs 4 — both models scored 4 on function selection/sequencing; each ranks 18 of 54 in our listings (tie). - Safety calibration and persona consistency: tied at 4 — both models rank 6 of 55 on safety calibration in our testing (tied). - Constrained rewriting: tied at 3. - Classification: GPT-4o-mini 4 vs GPT-5 Nano 3 — GPT-4o-mini is tied for 1st in classification (tied with 29 others), so it’s slightly better at tight categorization/routing tasks in our tests. External math benchmarks (Epoch AI): on MATH Level 5 (Epoch AI), GPT-5 Nano scores 95.2% vs GPT-4o-mini 52.6%; on AIME 2025 (Epoch AI), GPT-5 Nano scores 81.1% vs GPT-4o-mini 6.9%. Those external results strongly favor GPT-5 Nano for mathematical reasoning and algorithmic tasks (attribution: Epoch AI). Practical interpretation: choose GPT-5 Nano if your product needs long-context reliability, strict schema outputs, multilingual parity, or math/analysis performance. Pick GPT-4o-mini if your workload is classification-heavy and you accept a ~1.5× price premium.

Raw per-1k-token prices from the payload: GPT-4o-mini input $0.15 / output $0.60; GPT-5 Nano input $0.05 / output $0.40. Using a simple 50/50 split of input vs output tokens (common for chat-like apps), GPT-4o-mini costs ~$0.375 per 1k tokens ($375 per 1M tokens) and GPT-5 Nano ~$0.225 per 1k tokens ($225 per 1M). At scale that gap matters: 1M tokens/month = $375 vs $225 (difference $150); 10M = $3,750 vs $2,250 (difference $1,500); 100M = $37,500 vs $22,500 (difference $15,000). The payload’s priceRatio is 1.5, so GPT-4o-mini is roughly 1.5× the cost of GPT-5 Nano under comparable usage. Who should care: high-volume services, SaaS products, or any developer expecting millions of tokens/month — the cheaper per-token model (GPT-5 Nano) materially reduces monthly cloud spend. Low-volume hobby projects or features where the specific small advantages of GPT-4o-mini matter may not justify the extra cost.

Choose GPT-5 Nano if: - You need long-context handling (400k context) for docs, archives, or multi-step workflows. - You require robust structured outputs (JSON/schema), multilingual parity, or strong math/strategic analysis (95.2% on MATH Level 5 vs 52.6%). - You expect high token volumes and want lower per-token cost (input $0.05 / output $0.40). Choose GPT-4o-mini if: - Your primary workload is tight classification/routing where it scored better in our tests (classification 4 vs 3) and those gains matter. - You prefer the small-model tradeoff from GPT-4o-mini and can absorb ~1.5× higher token costs (input $0.15 / output $0.60).