GPT-5.6 Terra vs GPT-5 Mini
Pricing, context window and real answers (September 2026)
GPT-5.6 Terra (OpenAI) and GPT-5 Mini (OpenAI), 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 Terra | GPT-5 Mini | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Input (per 1M tokens) | ¥357.4 | ¥44.68 |
| Output (per 1M tokens) | ¥2,144.4 | ¥357.4 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥1,430 | ¥223 |
| Context window | 1,050,000 tokens | 400,000 tokens |
| Release date | 7/9/2026 | 8/7/2025 |
| Input modalities | file, image, text | text, image, file |
| Throughput (p50) | — | — |
| Arena · overall | #45 · ELO 1,466 | #163 · ELO 1,390 |
| Arena · Japanese | #20 · ELO 1,467 | #109 · ELO 1,365 |
| Arena · coding | #35 · ELO 1,518 | #172 · ELO 1,431 |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, GPT-5 Mini: ¥357.4 per 1M output tokens, 83% less than GPT-5.6 Terra.
- On the Overall arena board, GPT-5.6 Terra ranks higher (#45; GPT-5 Mini is #163).
- On the Japanese arena board, GPT-5.6 Terra ranks higher (#20; GPT-5 Mini is #109).
- On the Coding arena board, GPT-5.6 Terra ranks higher (#35; GPT-5 Mini is #172).
- For long documents, GPT-5.6 Terra: a 1,050,000-token context window against 400,000 for GPT-5 Mini.
- 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 Terra
“strawberry” is spelled:
s t **r** a w b e **r** **r** y
There are **3** occurrences of the letter **r**.
3.6s71 output tokens
GPT-5 Mini
We examine the word "strawberry" letter by letter:
1. s — not an r (count = 0)
2. t — not an r (count = 0)
3. r — an r (count = 1)
4. a — not an r (count = 1)
5. w — not an r (count = 1)
6. b — not an r (count = 1)
7. e — not an r (count = 1)
8. r — an r (count = 2)
9. r — an r (count = 3)
10. y — not an r (count = 3)
Total number of 'r's = 3.
5.2s382 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 Terra
The error means `users` is `undefined` when `UserList` renders, so React tries to evaluate:
```js
users.map(...)
```
But `.map()` only exists on arrays.
Common causes:
- The parent component does not pass a `users` prop.
- The user data is loaded asynchronously and has not arrived on the first render.
- The API response shape differs from what you expect, e.g. data is in `response.data.users` rather than `response.data`.
- The parent initializes its user state as `undefined` or `null`.
## Fix options
### 1. Initialize users as an empty array
In the parent component:
```jsx
const [users, setUsers] = useState([]);
```
Then pass it normally:
```jsx
<UserList users={users} />
```
An e…
5.8s559 output tokens
GPT-5 Mini
That error means users is undefined when you call users.map(...). map is an Array method, so calling it on undefined throws "Cannot read properties of undefined (reading 'map')".
Common causes
- The parent component didn't pass a users prop.
- users is still loading (e.g. fetched asynchronously) and is null/undefined until the fetch completes.
- The parent initialized users to null instead of an empty array.
How to fix
Choose one or more of these approaches depending on your app:
1) Initialize the parent state as an array
If users is state in the parent, initialize it to [] so the child always receives an array:
const [users, setUsers] = useState([]);
// fetch and setUsers(data)
2) Give…
8.0s966 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 Terra
Here are the main areas to improve:
### Issues in the original code
- **Imports inside the function**: Move `requests` and `time` imports to module scope.
- **Bare `except:`**: This catches everything, including `KeyboardInterrupt` and programming errors. Catch `requests.RequestException` (and possibly JSON decoding errors) instead.
- **Status code check**: Prefer `response.raise_for_status()` or `response.ok` over manually checking only for `200`. Successful responses can include other 2xx status codes.
- **`== True`**: Use `if item.get("active"):` instead.
- **Potential `KeyError`**: `item["name"]`, `item["id"]`, and `item["active"]` can fail if the API response is incomplete. Use `.get(…
10.6s939 output tokens
GPT-5 Mini
Thanks — this is a nice simple function, but it can be improved in several ways: move imports to module scope, avoid broad except:, use connection pooling (Session) for performance, use specific exception types, add logging and docstring, avoid comparing to True, use list comprehensions, validate JSON shape, and implement backoff (or use urllib3 Retry).
Below are two improved alternatives and a short rationale for the changes.
1) A simple, clear improvement (small changes, explicit errors, backoff + jitter)
- Moves imports to top-level
- Uses requests.Session for connection pooling
- Catches specific exceptions (requests.RequestException, ValueError)
- Uses list comprehension and item.get(…
13.6s1702 output tokens
Frequently asked questions
- Which is cheaper, GPT-5.6 Terra or GPT-5 Mini?
- Per 1M output tokens, GPT-5.6 Terra is ¥2,144.4 and GPT-5 Mini is ¥357.4 on FastMetal (yen, before tax), so GPT-5 Mini is cheaper.
- How do the context windows of GPT-5.6 Terra and GPT-5 Mini compare?
- GPT-5.6 Terra takes 1,050,000 tokens; GPT-5 Mini takes 400,000.
- Which ranks higher, GPT-5.6 Terra or GPT-5 Mini?
- GPT-5.6 Terra ranks higher on the public arena (GPT-5.6 Terra #20, GPT-5 Mini #109). Ranks move as the leaderboard updates.
- Can I use GPT-5.6 Terra and GPT-5 Mini with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-5.6-terra" or "gpt-5-mini" 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 Terra and GPT-5 Mini from the browser chat and the API. No monthly fee.