GPT-4.1 Nano vs GPT-5 Nano

GPT-5 Nano undercuts GPT-4.1 Nano on input costs by half—$0.05 vs $0.10 per MTok—while keeping output pricing identical at $0.40. That’s not just a marginal improvement. For tasks like document analysis or RAG pipelines where input tokens dominate, GPT-5 Nano delivers a 20% total cost reduction at 10M tokens monthly. The savings are negligible at low volume (just $1 difference at 1M tokens), but scale predictably. If your workload exceeds 5M tokens a month with a 3:1 input-to-output ratio, the math favors GPT-5 Nano by at least $1,500 annually.

The catch is that GPT-5 Nano isn’t just cheaper; it’s also better. Early benchmarks show it matches or exceeds GPT-4.1 Nano on reasoning tasks while maintaining lower latency. That flips the usual cost-performance tradeoff. Unless you’re locked into GPT-4.1 for legacy prompt compatibility, there’s no reason to pay the 4.1 premium. Even for output-heavy use cases like code generation where the $0.40/MTok output cost equals out, the input discount makes GPT-5 Nano the default choice. The only exception? If you’re running sub-1M tokens monthly, where the $1–$3 monthly delta doesn’t justify migration effort. For everyone else, switch now.

The benchmarks don’t just show GPT-5 Nano winning—they reveal a complete sweep in every tested category, which is remarkable given its identical price point to GPT-4.1 Nano. Constrained rewriting is the most decisive victory: GPT-4.1 Nano failed all three tests, while GPT-5 Nano aced them, handling strict output constraints (like exact word counts or forbidden phrases) without hallucinating or breaking format. This isn’t incremental improvement; it’s a step-change in reliability for tasks like API response formatting or legal clause rewrites where precision is non-negotiable.

Domain depth and instruction precision further expose GPT-4.1 Nano’s weaknesses. In domain-specific queries (e.g., niche Python library usage or obscure regulatory frameworks), GPT-5 Nano delivered correct, actionable answers twice, while GPT-4.1 Nano either defaulted to vague generalities or invented details. Instruction precision tests—where models must follow multi-step directives with conditional logic—showed a similar gap. GPT-5 Nano executed 2/3 correctly, including a tricky JSON-to-YAML conversion with embedded validation rules, whereas GPT-4.1 Nano botched all three, often merging steps or ignoring constraints entirely. The surprise isn’t that GPT-5 Nano wins; it’s that the margin is this wide in categories where prior Nano models were already optimized for cost over capability.

Structured facilitation is where the upgrade feels most pragmatic. GPT-5 Nano successfully guided users through a 3-step troubleshooting flow and generated a valid OpenAPI spec from a loose prompt—tasks GPT-4.1 Nano either abandoned mid-process or returned malformed outputs for. The 0.08-point difference in overall usability scores (2.33 vs 2.25) undersells the real-world impact: GPT-5 Nano isn’t just better, it’s dependable in scenarios where GPT-4.1 Nano would force manual review or rework. Untested areas like long-context retention or multimodal reasoning could further widen the gap, but even with current data, the choice is clear. If you’re deploying Nano for anything beyond trivial tasks, GPT-5’s version isn’t just worth the (same) cost—it’s the only responsible option.