GPT-5.4 Mini vs GPT-5.4 Nano

The ranking difference likely reflects real-world performance metrics beyond the raw score - Nano's 3.6x lower output pricing ($1.25/M vs $4.5/M) makes it more attractive for high-volume coding tasks while maintaining the same 400K context window and 128K max output. Since both models share identical capabilities including Vision, Function Calling, and Reasoning, the ranking algorithm appears to weight cost-efficiency heavily in the coding category where output tokens can quickly accumulate.

At 50M output tokens monthly, GPT-5.4 Nano costs $62.50 versus Mini's $225 - a $162.50 monthly savings (72% reduction). The input token pricing difference is less dramatic ($0.20/M vs $0.75/M), but for typical coding workflows with high output-to-input ratios, Nano delivers the same 61/100 performance score at nearly 1/4 the total cost.

OpenAI's pricing strategy suggests Mini may be positioned for lower-volume enterprise users who value predictable costs over per-token efficiency - the $0.75/M input price (versus Nano's $0.20/M) could reflect bundled SLAs or priority routing not captured in the raw capability list. With both models supporting identical 400K context windows and full feature parity (Vision, Web Search, JSON Mode), the price differential appears purely commercial rather than technical.

Migration is a no-brainer for most use cases - you get the same 61/100 performance, identical 128K max output capacity, and full feature compatibility while cutting costs by 72% on output tokens. The only hesitation would be if Mini includes undocumented benefits like lower latency or higher rate limits, but with both models ranking in the top 14 of 326 coding models, Nano's #12 position suggests it may actually perform marginally better in production.