A Comprehensive Look at GPT-5.4 Mini and Nano: OpenAI’s ‘Small’ Models with ‘Big’ Ambitions

Even by GPT itself:

Yes. For API use , I’d switch to Mini first and keep flagship as fallback.

Why: Mini is priced at $0.75 / $4.50 per 1M input/output tokens, supports a 400k context window plus tool use and computer use, and OpenAI positions it for high-volume coding, computer use, and agent workflows. (OpenAI)

The gap to flagship is real but usually not big enough to justify paying about 3.3x more on every call. OpenAI’s published scores are 57.7 vs 54.4 on SWE-bench Pro and 75.0 vs 72.1 on OSWorld-Verified for flagship vs mini. (OpenAI)

I’d keep flagship for:

• terminal-heavy work
• very long-context jobs
• high-stakes outputs

That is where the gap gets more meaningful. On Terminal-Bench 2.0 , flagship scores 75.1 vs 60.0 for mini, and OpenAI describes gpt-5.4 as the default for your most important work. (OpenAI)

So the simplest answer is:

Mini = economic default Flagship = risk-management default

For most coding assistants, agent loops, and routine product traffic, I’d start with Mini. For hard shell work, giant prompts, or costly mistakes, I’d route to flagship. (OpenAI Developers)