GPT-4o-mini vs GPT-5.4 Nano

GPT-5.4 Nano outperforms GPT-4o-mini on 9 of 12 benchmarks in our testing, with one tie and two GPT-4o-mini wins.

Where GPT-5.4 Nano wins:

• Strategic analysis (5 vs 2): This is the widest gap. GPT-5.4 Nano ties for 1st among 54 models tested; GPT-4o-mini ranks 44th. For business analysis, scenario modeling, or nuanced tradeoff reasoning, GPT-4o-mini trails badly.
• Creative problem solving (4 vs 2): GPT-5.4 Nano ranks 9th of 54; GPT-4o-mini ranks 47th — near the bottom. If non-obvious ideation or novel solutions matter, GPT-4o-mini is a poor choice.
• Structured output (5 vs 4): Both are solid, but GPT-5.4 Nano ties for 1st among 54 models on JSON schema compliance and format adherence. GPT-4o-mini ranks 26th. For applications relying on reliable structured data extraction, GPT-5.4 Nano is meaningfully more dependable.
• Long context (5 vs 4): GPT-5.4 Nano ties for 1st of 55 models on retrieval accuracy at 30K+ tokens; GPT-4o-mini ranks 38th. GPT-5.4 Nano also has a dramatically larger context window (400K tokens vs 128K), which matters for large document ingestion. GPT-5.4 Nano also supports up to 128K output tokens versus GPT-4o-mini's 16,384 — important for long-form generation.
• Agentic planning (4 vs 3): GPT-5.4 Nano ranks 16th of 54; GPT-4o-mini ranks 42nd. For multi-step autonomous workflows, goal decomposition, and failure recovery, GPT-5.4 Nano is better equipped.
• Persona consistency (5 vs 4): GPT-5.4 Nano ties for 1st of 53 models; GPT-4o-mini ranks 38th. Relevant for chatbot and character-based applications.
• Multilingual (5 vs 4): GPT-5.4 Nano ties for 1st of 55; GPT-4o-mini ranks 36th. Non-English use cases favor GPT-5.4 Nano.
• Constrained rewriting (4 vs 3): GPT-5.4 Nano ranks 6th of 53; GPT-4o-mini ranks 31st.
• Faithfulness (4 vs 3): GPT-5.4 Nano ranks 34th of 55; GPT-4o-mini ranks 52nd — near the bottom on sticking to source material without hallucinating. This is a meaningful concern for RAG pipelines or summarization.

Where GPT-4o-mini wins:

• Safety calibration (4 vs 3): GPT-4o-mini ranks 6th of 55 models — one of its strongest relative performances across all benchmarks. GPT-5.4 Nano ranks 10th. Both are above the field median (p50 = 2), but GPT-4o-mini's refusal calibration is sharper in our testing.
• Classification (4 vs 3): GPT-4o-mini ties for 1st of 53 models; GPT-5.4 Nano ranks 31st. For routing, tagging, and categorization tasks, GPT-4o-mini is the clear pick.

Tie:

• Tool calling (4 vs 4): Both rank 18th of 54 models, sharing that score with 29 models total. Neither has a meaningful edge here.

External benchmarks (Epoch AI): On AIME 2025 (math olympiad), GPT-5.4 Nano scores 87.8% — ranking 8th of 23 models tested — versus GPT-4o-mini's 6.9%, which ranks 21st of 23. On MATH Level 5 (competition math), GPT-4o-mini scores 52.6%, ranking 13th of 14 models. GPT-5.4 Nano has no MATH Level 5 score in the payload. These external scores confirm GPT-5.4 Nano's substantial edge in mathematical reasoning.

GPT-4o-mini costs $0.15/M input tokens and $0.60/M output tokens. GPT-5.4 Nano costs $0.20/M input and $1.25/M output — 33% more expensive on input and more than twice as expensive on output. In practice, output cost dominates most LLM bills. At 1M output tokens/month, GPT-4o-mini runs $0.60 versus $1.25 for GPT-5.4 Nano — a $0.65 difference that's negligible. At 10M output tokens/month, that gap grows to $6,500; at 100M tokens/month, you're looking at $65,000 more per month for GPT-5.4 Nano. For consumer apps or low-volume use, the cost difference is a rounding error. For high-throughput pipelines — bulk document processing, real-time chat at scale, or automated classification — GPT-4o-mini's 52% output cost advantage matters significantly, especially since GPT-4o-mini actually outperforms GPT-5.4 Nano on classification in our testing. Developers running pure classification or safety-filtered pipelines at scale have a concrete financial case for GPT-4o-mini.

Choose GPT-4o-mini if:

• Your primary workload is classification, routing, or content tagging — it ties for 1st of 53 models and costs less than half as much on output.
• Safety calibration is a top priority — it outperforms GPT-5.4 Nano and ranks 6th of 55 models in our testing.
• You're running at very high output volumes (10M+ tokens/month) where the $0.65/M output cost difference compounds to tens of thousands of dollars.
• Math or reasoning are not core to your use case (GPT-4o-mini scores 6.9% on AIME 2025 per Epoch AI).

Choose GPT-5.4 Nano if:

• You need reliable structured output for data extraction or APIs — it ties for 1st of 54 models vs GPT-4o-mini's 26th.
• Strategic analysis, business reasoning, or nuanced tradeoff evaluation is in scope — GPT-5.4 Nano scores 5/5 vs GPT-4o-mini's 2/5.
• You're building agentic or multi-step AI workflows — GPT-5.4 Nano ranks 16th vs GPT-4o-mini's 42nd on agentic planning.
• You need long context handling at 30K+ tokens, a 400K context window, or up to 128K output tokens.
• Faithfulness to source material matters — GPT-4o-mini ranks 52nd of 55 models on our hallucination test; GPT-5.4 Nano ranks 34th.
• Mathematical reasoning is relevant — GPT-5.4 Nano scores 87.8% on AIME 2025 versus GPT-4o-mini's 6.9% (Epoch AI).