Model comparison

GPT-4.1 Nano vs GPT-5.6 Terra

Pricing, context window and real answers (September 2026)

GPT-4.1 Nano (OpenAI) and GPT-5.6 Terra (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

openai logoGPT-4.1 Nanoopenai logoGPT-5.6 Terra
ProviderOpenAIOpenAI
Input (per 1M tokens)¥17.87¥357.4
Output (per 1M tokens)¥71.48¥2,144.4
Typical cost (1,000 in + 500 out tokens × 1,000 calls)¥54¥1,430
Context window1,047,576 tokens1,050,000 tokens
Release date4/14/20257/9/2026
Input modalitiesimage, text, filefile, image, text
Throughput (p50)
Arena · overall#243 · ELO 1,322#45 · ELO 1,466
Arena · Japanese#195 · ELO 1,234#20 · ELO 1,467
Arena · coding#226 · ELO 1,374#35 · ELO 1,518

Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.

Which should you pick?

  • On price, GPT-4.1 Nano: ¥71.48 per 1M output tokens, 97% less than GPT-5.6 Terra.
  • On the Overall arena board, GPT-5.6 Terra ranks higher (#45; GPT-4.1 Nano is #243).
  • On the Japanese arena board, GPT-5.6 Terra ranks higher (#20; GPT-4.1 Nano is #195).
  • On the Coding arena board, GPT-5.6 Terra ranks higher (#35; GPT-4.1 Nano is #226).
  • For long documents, GPT-5.6 Terra: a 1,050,000-token context window against 1,047,576 for GPT-4.1 Nano.
  • 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-4.1 Nano

Let's analyze the word "strawberry" step by step to count the number of 'r's. 1. Write down the word: **strawberry** 2. Examine each letter one by one: - s: no - t: no - r: yes (first 'r') - a: no - w: no - b: no - e: no - r: yes (second 'r') - r: yes (third 'r') - y: no 3. Counting the 'r's found: - First 'r' (at position 3) - Second 'r' (at position 8) - Third 'r' (at position 9) So, there are **3** 'r's in "strawberry." **Final answer: 3**
2.0s183 output tokens

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

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-4.1 Nano

The error: ``` TypeError: Cannot read properties of undefined (reading 'map') ``` indicates that `users` is `undefined` at the time your `UserList` component is rendering, and you're trying to call `map` on it. ### Why is this happening? In React, if a prop (`users` in this case) is not provided or is undefined, trying to do `users.map(...)` causes this error. This can happen if: - The parent component does not pass `users`. - `users` might be `null` or `undefined` initially, especially if data fetching is involved. ### How to fix it **Option 1: Provide a default value for `users`** Ensure that `users` defaults to an empty array if it is not provided: ```jsx function UserList({ users…
4.3s514 output tokens

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

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-4.1 Nano

Here's a review and an improved version of your function: ### Key improvements: 1. **Move imports to the top**: Imports should generally be at the top of the file, not inside functions. 2. **Specify exception types**: Catch specific exceptions rather than a generic `except`. 3. **Use context managers if needed**: Not necessary here, but good to consider. 4. **Avoid repeated code**: The sleep statement is the same whether there is a request exception or a non-200 status. 5. **Simplify filtering**: Use list comprehensions for clarity. 6. **Improve variable names**: For readability. 7. **Add optional headers or parameters** if needed (not required here). 8. **Optional:** Add more descriptive e…
3.4s391 output tokens

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

Compare on more prompts →

Frequently asked questions

Which is cheaper, GPT-4.1 Nano or GPT-5.6 Terra?
Per 1M output tokens, GPT-4.1 Nano is ¥71.48 and GPT-5.6 Terra is ¥2,144.4 on FastMetal (yen, before tax), so GPT-4.1 Nano is cheaper.
How do the context windows of GPT-4.1 Nano and GPT-5.6 Terra compare?
GPT-4.1 Nano takes 1,047,576 tokens; GPT-5.6 Terra takes 1,050,000.
Which ranks higher, GPT-4.1 Nano or GPT-5.6 Terra?
GPT-5.6 Terra ranks higher on the public arena (GPT-4.1 Nano #195, GPT-5.6 Terra #20). Ranks move as the leaderboard updates.
Can I use GPT-4.1 Nano and GPT-5.6 Terra with the same API key?
Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-4.1-nano" or "gpt-5.6-terra" 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-4.1 Nano and GPT-5.6 Terra from the browser chat and the API. No monthly fee.

More comparisons with GPT-4.1 Nano

More comparisons with GPT-5.6 Terra