OpenAI: GPT-4.1 Nano vs OpenAI: GPT-6 Luna Pro
A side-by-side comparison of OpenAI: GPT-4.1 Nano ($0.35 / 1M tokens) and OpenAI: GPT-6 Luna Pro ($0.35 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.
Specifications
| OpenAI: GPT-4.1 Nano | OpenAI: GPT-6 Luna Pro | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Input | $0.35 / 1M tokens | $0.35 / 1M tokens |
| Output | $1.40 / 1M tokens | $1.75 / 1M tokens |
| Cache read | $0.11 / 1M tokens | $0.04 / 1M tokens |
| Web search | $35.00 / 1K searches | $35.00 / 1K searches |
| Cache write | - | $0.44 / 1M tokens |
| Context window | 1,047,576 | 1,050,000 |
| Max output | 942,818 | 128,000 |
| Input | image, text, file | file, image, text |
| Output | text | text |
| Tool calling | ||
| Structured output | ||
| Reasoning control | ||
| Added | Apr 14, 2025 | Sep 22, 2026 |
Key differences
- Both have about the same input price.
- OpenAI: GPT-4.1 Nano is 20% cheaper on output tokens.
- Only OpenAI: GPT-6 Luna Pro supports reasoning effort control.
- A request with 1,000 input and 500 output tokens costs $0.00105 with OpenAI: GPT-4.1 Nano and $0.00123 with OpenAI: GPT-6 Luna Pro.
In depth
Both models come from OpenAI and both are listed on api14 as text generation models. GPT-4.1 Nano is the older of the two, released in April 2025, while GPT-6 Luna Pro arrived later, in September 2026. They share the same provider and the same basic job: you send a prompt, you get text back. The interesting differences are in what each one accepts as input, how much room it has for context, and which request parameters it understands.
On context window the two are close enough that it should not drive your decision. GPT-4.1 Nano lists 1,047,576 tokens and GPT-6 Luna Pro lists 1,050,000, so both can hold very large prompts, long documents or extended conversation histories. Input modalities are identical as well: both accept text, images and files, and both return text only. Where they diverge is reasoning. GPT-6 Luna Pro is the only one of the two that exposes reasoning controls, including reasoning, reasoning_effort, include_reasoning and verbosity, which means it can be asked to think through a problem before answering and to report that thinking. GPT-4.1 Nano has no reasoning parameters at all. The two also differ slightly in the parameter surface: GPT-4.1 Nano supports temperature and top_p for sampling control, while GPT-6 Luna Pro adds max_tokens alongside max_completion_tokens. Both support tools, tool_choice, structured_outputs, response_format and seed, so function calling and JSON-shaped output are available on either.
For day to day work, the choice usually comes down to whether you need reasoning. If you are doing straightforward generation, extraction, classification, summarisation or light tool calling, GPT-4.1 Nano is the sensible default. It is the cheaper of the two on output, and input pricing is about the same on both sides, so for high volume workloads where the model mostly echoes or reformats what you give it, Nano keeps the bill down without giving up the long context or the image and file inputs. Reach for GPT-6 Luna Pro when the task benefits from deliberate reasoning: multi step analysis, tricky debugging, planning, or anything where you want to trade more output tokens for a better answer. The reasoning_effort and verbosity knobs let you dial that tradeoff rather than accept it wholesale.
Calling either model through api14 works the same way because the gateway speaks the OpenAI chat completions format. You point your existing client at the api14 base URL, send your key, and set the model field. The only change between the two is that string: use openai/gpt-4.1-nano for Nano and openai/gpt-6-luna-pro for Luna Pro. Everything else in the request body stays as it is, so switching models is a one line edit rather than a rewrite. If you want to compare them on your own traffic, keep the same messages and parameters and just swap the id.
There are still cases where the older model wins. Because GPT-4.1 Nano is cheaper on output and about the same price on input, any pipeline that runs at scale and does not need step by step reasoning is better served by Nano, even though Luna Pro is the more capable model on paper. Latency sensitive paths and simple tool loops fall into the same bucket. Treat Luna Pro as the upgrade you reach for when the task is hard, not as the default you pay for on every call.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "openai/gpt-4.1-nano" to "openai/gpt-6-luna-pro" and prompts, streaming and tools keep working.
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api14.com/api/v1",
apiKey: process.env.API14_API_KEY,
});
const result = await client.chat.completions.create({
"model": "openai/gpt-6-luna-pro",
"messages": [
{
"role": "user",
"content": "Explain vector databases in two sentences."
}
]
});
console.log(result.choices[0].message.content);
More comparisons
- LiquidAI: LFM3-1.7B vs Ricursive: Ripple 65B Preview
- LiquidAI: LFM3-1.7B vs Crouton Alpha
- LiquidAI: LFM3-1.7B vs Tencent: Hy4
- LiquidAI: LFM3-1.7B vs Z.ai: GLM 5.3 FlashX Legal
- Ricursive: Ripple 65B Preview vs Crouton Alpha
- Ricursive: Ripple 65B Preview vs Tencent: Hy4
- Ricursive: Ripple 65B Preview vs Z.ai: GLM 5.3 FlashX Legal
- Crouton Alpha vs Tencent: Hy4