GPT-5.6 Luna vs Pareto
Pricing, context window and real answers (November 2026)
GPT-5.6 Luna (OpenAI) and Pareto (Unbiased), compared as you can actually call them on FastMetal. Both are served from the same OpenAI-compatible endpoint and API key; switching is a change to the model string.
Specs and pricing
 GPT-5.6 Luna | Pareto | |
|---|---|---|
| Provider | OpenAI | Unbiased |
| Input (per 1M tokens) | $0.211 | $0.88 |
| Output (per 1M tokens) | $1.27 | $3.52 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | $0.844 | $2.64 |
| Context window | 1,050,000 tokens | 1,048,576 tokens |
| Release date | 7/9/2026 | 10/15/2026 |
| Input modalities | file, image, text | text, image |
| Throughput (p50) | 166 tokens/s (n=28) | 25000 tokens/s (n=18) |
| Arena · overall | #76 · ELO 1,453 | Unranked |
| Arena · Japanese | #58 · ELO 1,433 | Unranked |
| Arena · coding | #78 · ELO 1,502 | Unranked |
Prices are FastMetal's rates, before tax.
Rankings: LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset), CC BY 4.0, snapshot 2026-10-28; category names mapped and ratings rounded by FastMetal.
Which should you pick?
- On price, GPT-5.6 Luna: $1.27 per 1M output tokens, 64% less than Pareto.
- For long documents, GPT-5.6 Luna: a 1,050,000-token context window against 1,048,576 for Pareto.
- If in doubt, try both on the same key. Switching is a change to the model string, and each is billed at its own rate.
Real answers to the same prompts
Exactly what the FastMetal gateway returned, side by side. Not benchmark scores: actual output.
Count the number of 'r's in 'strawberry'
Count the number of 'r's in 'strawberry'. Explain your reasoning step by step.
GPT-5.6 Luna
The word **“strawberry”** is spelled:
**s – t – r – a – w – b – e – r – r – y**
The letter **“r”** appears at positions **3, 8, and 9**.
**Answer: 3**
3.0s120 output tokens
Pareto
To count the number of 'r's in the word "strawberry," let's break it down letter by letter:
1. **s** - not an 'r'
2. **t** - not an 'r'
3. **r** - **Yes**, this is the first 'r'.
4. **a** - not an 'r'
5. **w** - not an 'r'
6. **b** - not an 'r'
7. **e** - not an 'r'
8. **r** - **Yes**, this is the second 'r'.
9. **r** - **Yes**, this is the third 'r'.
10. **y** - not an 'r'
Counting the occurrences:
- The 3rd letter is 'r'.
- The 8th letter is 'r'.
- The 9th letter is 'r'.
There are **3** 'r's in "strawberry".
5.5s225 output tokens
Debug This Error
I'm getting the following error in my Node.js application: TypeError: Cannot read properties of undefined (reading 'map') at UserList (/app/components/UserList.js:12:25) at renderWithHooks (/app/node_modules/rea…
GPT-5.6 Luna
`users` is `undefined` when `UserList` renders, so calling `.map()` on it fails. `.map()` only exists on arrays.
Common causes include:
- The parent does not pass a `users` prop.
- The prop name differs, such as `userList` instead of `users`.
- Users are loaded asynchronously and are initially undefined.
- The API response has a different shape, such as `{ data: [...] }`.
### Provide a default value
```jsx
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => (
<li key={user.id}>{user.name}</li>
))}
</ul>
);
}
```
The parent should pass an array:
```jsx
<UserList users={users} />
```
If the list comes from state, initialize it as an empty…
4.8s507 output tokens
Pareto
The error means `users` is `undefined` when `UserList` renders. Destructuring a prop does **not** protect against it:
```jsx
function UserList({ users }) { // users can still be undefined
return <ul>{users.map(...)}</ul>; // 💥
}
```
This usually happens when:
- The parent renders `<UserList />` without passing `users`
- The parent’s state starts as `undefined` before an async fetch finishes
- The API response is not an array, or `users` is passed as `undefined`
## Fix 1: Default the prop to an empty array
```jsx
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => (
<li key={user.id}>{user.name}</li>
))}
</ul>
);
}
```
This works when…
11.6s1478 output tokens
Code Review
Please review the following Python function and suggest improvements for readability, performance, and best practices: def get_data(url, retries=3): import requests import time for i in range(retries):…
GPT-5.6 Luna
### Main issues
- Imports should be at module level, not inside the function.
- A bare `except:` catches everything, including programming errors and `KeyboardInterrupt`.
- The function sleeps even after the final failed attempt.
- It retries all HTTP statuses, including permanent errors such as `404`.
- `status_code == 200` excludes other successful `2xx` responses.
- `if item["active"] == True` should generally be `if item.get("active") is True`.
- A `requests.Session` can reuse connections and improve performance.
- Returning `[]` for every failure makes it impossible to distinguish “no active items” from “request failed.”
- Retries should generally use backoff rather than a fixed delay.…
23.3s1978 output tokens
Pareto
The function is straightforward, but its error handling hides failures and retries requests that may never succeed.
### Main improvements
- **Move imports to module scope** for readability and conventional dependency management.
- **Avoid bare `except:`.** It catches everything, including programming errors and `KeyboardInterrupt`. Catch specific request failures instead.
- **Retry selectively.** Timeouts, connection failures, rate limits, and certain server errors may be temporary; most other HTTP errors should surface immediately.
- **Use exponential backoff** and avoid sleeping after the last attempt.
- **Use a list comprehension** to express the filtering and transformation directly.
-…
30.3s1090 output tokens
Frequently asked questions
- Which is cheaper, GPT-5.6 Luna or Pareto?
- Per 1M output tokens, GPT-5.6 Luna is $1.27 and Pareto is $3.52 on FastMetal (US dollars, before tax), so GPT-5.6 Luna is cheaper.
- How do the context windows of GPT-5.6 Luna and Pareto compare?
- GPT-5.6 Luna takes 1,050,000 tokens; Pareto takes 1,048,576.
- Can I use GPT-5.6 Luna and Pareto with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-5.6-luna" or "pareto" as the model. Each is billed at its own rate from the same prepaid balance.
Try both on one API key
Create an account and add credit to call GPT-5.6 Luna and Pareto from the browser chat and the API. No monthly fee.