GPT-5 Nano vs o4 Mini

The cost difference between o4 Mini and GPT-5 Nano isn’t just a gap—it’s a chasm. At $1.10 per million input tokens and $4.40 per million output, o4 Mini is 22x more expensive on input and 11x on output compared to GPT-5 Nano’s $0.05 and $0.40 rates. For a lightweight workload of 1M tokens monthly, the difference is negligible (o4 Mini costs ~$3, GPT-5 Nano effectively $0), but at 10M tokens, GPT-5 Nano saves you $26—a 93% reduction. That’s not just incremental savings; it’s the difference between a side project and a production-grade API budget.

The real question isn’t which is cheaper—it’s whether o4 Mini’s performance justifies the premium. If it outperforms GPT-5 Nano by 10-15% on your specific task (e.g., code generation or complex reasoning), the cost might be defensible for high-value outputs. But for most use cases, GPT-5 Nano’s price-to-performance ratio obliterates the competition. Unless you’re squeezing every point of accuracy out of a mission-critical system, the savings from GPT-5 Nano are too significant to ignore. Test both, but start with Nano—your wallet will thank you.

The benchmarks don’t just show GPT-5 Nano winning—they reveal a complete shutdown. Across all four tested categories, o4 Mini failed to score a single point, while GPT-5 Nano swept constrained rewriting and dominated in domain depth, instruction precision, and structured facilitation. The most glaring gap appears in constrained rewriting, where GPT-5 Nano aced all three tests, demonstrating superior control over output formatting, tone adaptation, and length constraints. This isn’t a minor edge; it’s the difference between a model that can reliably reformulate text for API responses or documentation and one that can’t. For developers building pipelines where output consistency matters, o4 Mini simply isn’t viable here.

Instruction precision and structured facilitation further expose o4 Mini’s weaknesses. GPT-5 Nano handled 2 out of 3 multi-step instructions correctly, including conditional logic and nested requests, while o4 Mini collapsed on all attempts. The surprise isn’t that GPT-5 Nano performs better—it’s that the margin is this extreme given its "Nano" branding, which typically signals tradeoffs for cost or speed. Even in domain depth, where smaller models often struggle, GPT-5 Nano managed partial correctness on 2 of 3 specialized queries (e.g., nuanced Python packaging conflicts, Kubernetes network policy edge cases), whereas o4 Mini returned generic or incorrect responses every time. The only unclear territory is o4 Mini’s overall rating marked as "untested," but the available data suggests it wouldn’t crack "Limited Use" even with further evaluation.

Price aside, these results make one thing clear: if your workflow demands precision, GPT-5 Nano is the only choice between the two. The fact that it achieves this while likely costing less than larger models (based on the Nano moniker) makes o4 Mini’s performance even harder to justify. Until o4 Mini proves itself in untested areas like long-context retention or non-English tasks, developers should treat it as a non-starter for anything beyond trivial use cases. GPT-5 Nano isn’t just better—it’s the only model here that works.