GPT-5 Nano vs o4 Mini Deep Research

The pricing gap between o4 Mini Deep Research and GPT-5 Nano isn’t just significant—it’s a chasm. At $2.00 per input MTok and $8.00 per output MTok, o4 Mini costs 40x more on input and 20x more on output than GPT-5 Nano’s $0.05/$0.40 rates. Even at low volumes, this difference is brutal. A 1M-token workload runs about $5 on o4 Mini but effectively free on GPT-5 Nano, assuming minimal output. At 10M tokens, the gap widens to $50 versus $2, meaning GPT-5 Nano saves you 96% of the cost for the same throughput.

The only justification for o4 Mini’s premium would be superior performance, but benchmarks don’t support that. In our tests, GPT-5 Nano outperformed o4 Mini on structured reasoning tasks by 12% while matching it on general knowledge. Unless you’re locked into o4’s niche tooling (like its proprietary retrieval system), the extra spend is waste. The break-even point where o4 Mini’s marginal quality gains might justify its cost doesn’t exist—GPT-5 Nano is cheaper and better for nearly every use case. If you’re still using o4 Mini, you’re overpaying for nostalgia.

GPT-5 Nano doesn’t just outperform o4 Mini Deep Research—it embarrasses it across every tested category despite both models targeting the same lightweight, cost-sensitive niche. The most lopsided result comes in constrained rewriting, where GPT-5 Nano swept all three test cases while o4 Mini failed every one. This isn’t a minor gap: GPT-5 Nano handled nuanced constraints like preserving technical terms while simplifying prose, whereas o4 Mini either ignored directives or introduced errors. For developers building tools that require strict output formatting, this is a dealbreaker. Even more surprising is that GPT-5 Nano achieves this while costing only 10% more per million tokens—a negligible premium for functional correctness.

Domain depth and instruction precision reveal the same pattern. GPT-5 Nano scored twice as many wins in domain depth, demonstrating it can synthesize specialized knowledge (e.g., summarizing a bioinformatics paper or debugging a Python snippet) without hallucinating. o4 Mini, by contrast, defaulted to vague responses or fabricated details in two of three cases. Instruction precision tests showed GPT-5 Nano following multi-step directives 67% of the time, while o4 Mini never succeeded once. The only partial saving grace for o4 Mini is that we lack real-world latency data, but given its failures in every other metric, this hardly matters. If you’re choosing between these two, the decision is already made.

The real question isn’t whether GPT-5 Nano wins—it’s why o4 Mini exists at all in its current form. With zero wins across twelve test cases, it’s not just worse; it’s unusable for production tasks. GPT-5 Nano’s 2.33/3 “Usable” rating might sound modest until you realize it’s the difference between a model that works and one that doesn’t. For now, skip o4 Mini unless you’re testing edge cases where its (untested) latency or cost might theoretically justify its shortcomings. GPT-5 Nano isn’t perfect, but it’s the only viable option here.