GPT-5.2 vs GPT-5 Nano

GPT-5 Nano isn’t just cheaper—it’s 35x cheaper on input and 35x cheaper on output than GPT-5.2, making it the clear winner for cost-sensitive workloads. At 1M tokens per month, the difference is negligible (GPT-5.2 costs ~$8, Nano costs practically nothing), but scale to 10M tokens and GPT-5.2 runs ~$79 while Nano stays under $2. That’s a $77 savings for identical token volume, which compounds fast in production. If you’re processing millions of tokens daily, Nano’s pricing turns a six-figure LLM budget into a rounding error.

The catch? GPT-5.2 outperforms Nano on benchmarks like MMLU and HumanEval by ~10-15%, but that premium buys diminishing returns for most applications. Unless you’re solving nuanced reasoning tasks (e.g., multi-step code generation or legal analysis), Nano’s 85-90% performance at 5% the cost is the smarter tradeoff. Even for high-stakes use cases, the savings from Nano could fund human review or fine-tuning to close the gap. The only teams who should default to GPT-5.2 are those where model accuracy directly drives revenue—and even then, A/B test Nano first. The price delta is too steep to ignore.

The head-to-head benchmarks reveal a shocking pattern: GPT-5 Nano doesn’t just compete with its bigger sibling—it dominates in precision tasks where GPT-5.2 stumbles. In constrained rewriting, Nano swept all three tests while GPT-5.2 failed completely, suggesting the larger model’s tendency to overgenerate or ignore tight constraints. This isn’t a fluke. Nano also won instruction precision (2/3) and structured facilitation (2/3), categories where GPT-5.2’s extra parameters should theoretically give it an edge. The data implies Nano’s fine-tuning is simply more disciplined for tasks requiring strict adherence to rules or formats. If you’re building workflows where output consistency matters more than creative range, Nano is the clearer choice despite its smaller size.

Where GPT-5.2 still leads is in raw capability breadth, as reflected in its higher overall score (2.67 vs Nano’s 2.33). But that advantage evaporates in domain-specific tests. Nano took two of three domain depth challenges, proving it retains specialized knowledge better than expected for a "lightweight" model. The price-performance mismatch here is glaring: Nano costs 1/10th of GPT-5.2’s token rate yet outperforms it in half the tested categories. Untested areas like long-context reasoning or multimodal tasks may still favor GPT-5.2, but for developers optimizing for cost and reliability in structured tasks, Nano’s wins are decisive. The real question isn’t whether GPT-5.2 is more powerful—it’s why its extra capacity isn’t translating into better precision.