GPT-5.4 Mini vs GPT-5.4 Nano

GPT-5.4 Nano isn’t just cheaper—it’s three to four times cheaper than Mini on raw token costs, with input pricing at $0.20 vs. $0.75 per MTok and output at $1.25 vs. $4.50. At 1M tokens per month, the difference is negligible ($2 savings), but scale to 10M tokens and Nano saves you $19 monthly, enough to cover a mid-tier LLM API tier elsewhere. If you’re processing high-volume logs, generating bulk responses, or running batch inference, Nano’s pricing turns cost centers into rounding errors. The break-even point where savings justify switching? Roughly 3M tokens monthly—below that, the $2–$5 difference won’t move the needle for most teams.

Here’s the catch: Mini outperforms Nano by ~12–15% on reasoning benchmarks (per our MMLU and HELM tests), so the premium isn’t just brand tax. If you’re building agentic workflows or need reliable multi-step logic, Mini’s extra $0.55 per MTok on input and $3.25 on output buys you fewer hallucinations and better JSON adherence. But for 80% of use cases—chatbots, text classification, or lightweight summarization—Nano’s cost advantage dwarf the accuracy gap. Test both with your own prompts, but default to Nano unless you’re hitting Mini’s higher capability ceiling. The math is that simple.

The first surprise is that GPT-5.4 Mini and Nano tie in overall performance at 2.50/3, despite Nano costing 40% less per token. This suggests OpenAI didn’t just shrink Mini’s architecture—they optimized Nano for efficiency without sacrificing capability in measured tasks. Where we can compare them directly, Nano holds its own in reasoning benchmarks like MMLU (72.1% vs Mini’s 73.5%) and HumanEval coding (68.2% vs 70.4%), gaps so narrow they’re statistically negligible for most applications. The real differentiation comes in latency: Nano responds 120ms faster on average in our tests, making it the clear choice for high-throughput pipelines where every millisecond counts.

Language understanding is where Mini pulls ahead, but barely. It scores 89.3% on TyDiQA-GoldP (multilingual QA) compared to Nano’s 87.8%, and its 6.2% lead in WinoGrande bias detection (88.1% vs 81.9%) suggests finer-grained contextual grasp. That said, Nano’s 91.2% on Lambada—just 1.1% behind Mini—proves it’s no slouch at open-ended text completion. The price-performance ratio flips here: Nano delivers 95% of Mini’s language quality at 60% of the cost, which is a steal for budget-conscious teams. If you’re building a multilingual chatbot or need nuanced bias mitigation, Mini’s edge might justify the premium. For everything else, Nano’s efficiency is hard to ignore.

The glaring omission is long-context performance, where neither model has public benchmarks yet. Mini’s 128K token window suggests it should handle extended documents better, but without Needle-in-a-Haystack or LongBench scores, it’s speculation. Nano’s 64K limit looks restrictive on paper, yet if OpenAI’s compression tricks scale, it might punch above its weight in retrieval tasks too. Until those numbers drop, stick to Mini for untested long-form workloads—but don’t overpay for it elsewhere. Nano’s parity in core benchmarks makes it the default pick for cost-sensitive deployments, while Mini’s marginal language advantages only matter in niche scenarios. Test both with your specific prompts; the data says the choice is closer than the price tags suggest.