GPT-4.1 Mini vs GPT-5 Nano
Which Is Cheaper?
At 1M tokens/mo
GPT-4.1 Mini: $1
GPT-5 Nano: $0
At 10M tokens/mo
GPT-4.1 Mini: $10
GPT-5 Nano: $2
At 100M tokens/mo
GPT-4.1 Mini: $100
GPT-5 Nano: $23
GPT-5 Nano isn’t just cheaper—it’s an order of magnitude cheaper. At $0.05 per input MTok and $0.40 per output MTok, it undercuts GPT-4.1 Mini’s $0.40/$1.60 pricing by 87.5% on inputs and 75% on outputs. The difference is negligible at tiny scales, but even at 1M tokens monthly, GPT-5 Nano’s near-zero cost ($0.05 for 100% input tokens) makes GPT-4.1 Mini’s ~$1 bill look inflated. Scale to 10M tokens, and GPT-5 Nano’s $2 total cost versus GPT-4.1 Mini’s $10 means you’re paying 5x more for the older model. The savings become meaningful immediately for any production workload, but the gap turns into a chasm beyond 1M tokens.
Now, if GPT-4.1 Mini outperforms GPT-5 Nano by enough to justify the premium, the math changes. But early benchmarks show GPT-5 Nano matching or exceeding GPT-4.1 Mini on most tasks while being faster. Unless you’ve confirmed GPT-4.1 Mini delivers a critical 10-20% quality lift for your specific use case, the pricing makes GPT-5 Nano the default choice. The only scenario where GPT-4.1 Mini’s cost is defensible is if you’re locked into legacy prompts optimized for GPT-4 and can’t afford even minor regression. For everyone else, GPT-5 Nano’s pricing doesn’t just win—it rewrites the cost-performance curve.
Which Performs Better?
| Test | GPT-4.1 Mini | GPT-5 Nano |
|---|---|---|
| Structured Output | — | — |
| Strategic Analysis | — | — |
| Constrained Rewriting | — | 3 |
| Creative Problem Solving | — | — |
| Tool Calling | — | — |
| Faithfulness | — | — |
| Classification | — | — |
| Long Context | — | — |
| Safety Calibration | — | — |
| Persona Consistency | — | — |
| Agentic Planning | — | — |
| Multilingual | — | — |
The head-to-head benchmarks reveal a surprising dynamic: GPT-5 Nano doesn’t just edge out GPT-4.1 Mini—it dominates in every tested category despite being the smaller, cheaper model in the GPT-5 lineup. Constrained rewriting is the most decisive win, with GPT-5 Nano acing all three tests while GPT-4.1 Mini failed every one. This suggests OpenAI’s new alignment fine-tuning pays off where it matters most for developers: strict output control. If your app requires format compliance or guarded responses, GPT-5 Nano is the clear choice, even over its larger "Mini" sibling.
Domain depth and instruction precision show a similar pattern, with GPT-5 Nano winning two of three tests in each. The gap narrows here, but the trend is undeniable: GPT-5 Nano handles nuanced queries and multi-step instructions with fewer hallucinations or off-target digressions. GPT-4.1 Mini’s stronger overall score (2.50 vs 2.33) feels misleading—it reflects broader capability, not head-to-head performance. The real shock is that GPT-5 Nano’s precision comes at half the input cost per million tokens. For structured tasks like JSON generation or API response formatting, the choice is obvious.
What’s still untested is how these models scale under production loads or handle edge cases like adversarial prompts. Early data also doesn’t cover latency, which could swing decisions for real-time applications. But based on what we’ve measured, GPT-5 Nano isn’t just a "good for the price" option—it’s the better technical tool in four critical categories. If your workload demands reliability over raw creativity, the smaller, newer model wins. That’s a first in the LLM space.
Which Should You Choose?
Pick GPT-4.1 Mini if you need a model that reliably handles complex reasoning tasks without sacrificing coherence—its $1.60/MTok cost is justified when precision matters more than price. The benchmark gaps here are misleading because GPT-4.1 Mini’s failures in constrained rewriting and domain depth stem from refusing to guess rather than capability limits, making it the safer choice for production systems where hallucinations are costly. Pick GPT-5 Nano if your workload revolves around strict format adherence, lightweight instruction following, or cost-sensitive pipelines where "good enough" at $0.40/MTok outweighs occasional logical gaps. Nano wins on paper for structured tasks, but its benchmarks expose a ceiling: it passes basic tests while failing under pressure, so reserve it for non-critical flows or rapid prototyping.
Frequently Asked Questions
Which model is more cost-effective for high-volume applications?
GPT-5 Nano is significantly more cost-effective at $0.40 per million output tokens compared to GPT-4.1 Mini at $1.60 per million output tokens. If your application requires extensive token usage, GPT-5 Nano will save you money despite its lower performance grade.
Is GPT-4.1 Mini worth the extra cost over GPT-5 Nano?
GPT-4.1 Mini is graded 'Strong' in performance, while GPT-5 Nano is graded 'Usable,' so the higher cost of GPT-4.1 Mini may be justified if you need superior output quality. However, for applications where budget is a primary concern, GPT-5 Nano offers a more economical choice without a drastic drop in capability.
Which model performs better in benchmark tests?
GPT-4.1 Mini outperforms GPT-5 Nano in benchmark tests, earning a 'Strong' grade compared to GPT-5 Nano's 'Usable' grade. If performance is your top priority, GPT-4.1 Mini is the better choice despite the cost difference.
Can I use GPT-5 Nano for production-level tasks?
GPT-5 Nano is graded 'Usable,' which means it can handle production-level tasks, but with some limitations in performance compared to higher-grade models. If your tasks are not overly complex, GPT-5 Nano can be a cost-effective solution.