GPT-4.1 Nano vs GPT-5.2

GPT-4.1 Nano isn’t just cheaper—it’s dramatically cheaper, with input costs 17.5x lower and output costs 35x lower than GPT-5.2. At 1 million tokens per month, the difference is negligible (GPT-5.2 costs ~$8, Nano is effectively free), but scale to 10 million tokens and Nano saves you $76 for the same workload. That’s a 96% cost reduction, which translates to real money for production apps. If you’re processing high-volume logs, generating synthetic data, or running batch inference, Nano’s pricing turns a budget line item into a rounding error.

The catch is that GPT-5.2 outperforms Nano on nearly every benchmark, but the premium is steep. For tasks where Nano’s 82% accuracy (vs. GPT-5.2’s 91% on MMLU) is acceptable—like draft generation, lightweight classification, or internal tooling—the savings are undeniable. The break-even point for accuracy vs. cost lands around specialized use cases: if you’re building a customer-facing LLM app where hallucinations or precision errors carry a tangible risk, GPT-5.2’s price might justify itself. For everything else, Nano’s cost efficiency is a no-brainer. Test both on your specific workload, but start with Nano. The odds are good you won’t need the upgrade.

GPT-5.2 doesn’t just outperform GPT-4.1 Nano—it exposes the tradeoffs of Nano’s aggressive optimization. In reasoning benchmarks, GPT-5.2 scores 2.8/3 on complex multi-step logic (e.g., MMLU, ARC-Challenge), while Nano stumbles at 2.1/3, revealing its pruned architecture struggles with abstraction. The gap widens in code generation, where GPT-5.2 maintains 92% correctness on HumanEval against Nano’s 78%, a delta that matters if you’re shipping to production. Nano’s strength—its 4x lower latency—comes at a cost: it sacrifices nuanced instruction-following, scoring 2.3/3 in alignment tests where GPT-5.2 hits 2.7/3. That’s the difference between a model that guesses at edge cases and one that handles them deliberately.

Where Nano fights back is in cost-sensitive, high-throughput tasks. Its 1.2¢/1k tokens undercuts GPT-5.2’s 3.5¢/1k by nearly 3x, and in simple QA (TriviaQA) or summarization (CNN/DM), the performance gap shrinks to 0.2 points—a negligible difference for many applications. The surprise isn’t that Nano lags in capability; it’s that it’s usable at all given its size. Early tests show it matching GPT-5.2 in 60% of creative writing prompts (e.g., short-story generation), suggesting its compression prioritized fluency over rigor. Still, untested areas like multimodal reasoning and long-context retrieval (100k+ tokens) remain question marks for both. If your workload demands precision, GPT-5.2’s lead is worth the premium. If you’re batch-processing generic text, Nano’s efficiency turns its limitations into a feature. Choose accordingly.