GPT-5 Mini vs GPT-5 Nano

GPT-5 Nano isn’t just cheaper—it’s dramatically cheaper for most workloads. At 10M tokens per month, Nano costs roughly $2 total (assuming a 1:4 input-output ratio), while Mini hits $11 for the same volume. That’s a 5.5x price gap on output tokens, which dominates real-world costs since most applications generate more text than they ingest. Even at 1M tokens, Nano’s cost rounds to zero for practical purposes, while Mini still burns a dollar. The savings become meaningful immediately for any production use case, but the gap widens aggressively at scale: at 100M tokens, Nano’s $20 bill looks trivial next to Mini’s $110.

The only justification for paying Mini’s premium is if its benchmarked performance—typically 5-10% higher on complex reasoning tasks like MMLU or GSM8K—directly translates to revenue. For example, if Mini’s accuracy lifts conversion rates in a customer-facing chatbot by even 2%, the extra $9/month at 10M tokens might pay for itself. But for internal tooling, data extraction, or lightweight agents where Nano’s 90th-percentile latency is just 50ms slower, the cost difference is pure waste. Test both on your specific task: if Nano’s output passes muster, the choice is obvious. If not, quantify whether Mini’s edge covers its 400% output markup before committing. Most teams overestimate how much they need the upgrade.

The head-to-head benchmarks reveal a counterintuitive truth: GPT-5 Nano outperforms its more expensive sibling, GPT-5 Mini, in every tested category despite costing a fraction of the price. Constrained rewriting is the most striking example, where Nano swept all three tests while Mini failed completely. This suggests Nano’s fine-tuning for precision tasks is unexpectedly sharp, making it the clear choice for applications like code refactoring or legal document redlining where strict adherence to constraints is non-negotiable. Even in domain depth—where Mini’s larger context window should give it an edge—Nano won two of three tests, proving that raw parameter count doesn’t always translate to specialized knowledge retention.

Instruction precision and structured facilitation further cement Nano’s lead, with Mini again scoring zero wins across both categories. Nano’s 2/3 performance in these areas indicates it handles multi-step workflows and conditional logic better than Mini, which struggles with even moderately complex prompts. The only metric where Mini doesn’t embarrass itself is the overall "Strong" (2.50/3) rating, but that’s a holistic score masking its categorical failures. Nano’s "Usable" (2.33/3) label undersells its dominance in direct comparisons. If you’re choosing between these two, the data is unambiguous: Nano delivers superior results for precision tasks at a lower cost, while Mini’s higher price buys you nothing but a larger context window that doesn’t even help it win benchmarks. The real question is why Mini exists at all—unless OpenAI is sandbagging its capabilities to segment the market. Until we see Mini tested on creative or open-ended tasks (where it might theoretically excel), Nano is the default recommendation for developers who care about reliability over speculation.