GPT-5.4 Nano vs Llama 3.1 8B Instruct
Pricing, context window and real answers (September 2026)
GPT-5.4 Nano (OpenAI) and Llama 3.1 8B Instruct (Meta), 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-5.4 Nano |  Llama 3.1 8B Instruct | |
|---|---|---|
| Provider | OpenAI | Meta |
| Input (per 1M tokens) | ¥35.74 | ¥8.94 |
| Output (per 1M tokens) | ¥223.38 | ¥14.3 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥147 | ¥16 |
| Context window | 400,000 tokens | 131,072 tokens |
| Release date | 3/17/2026 | 7/23/2024 |
| Input modalities | file, image, text | text |
| Throughput (p50) | — | — |
| Arena · overall | #146 · ELO 1,402 | #329 · ELO 1,211 |
| Arena · Japanese | #107 · ELO 1,368 | #243 · ELO 1,087 |
| Arena · coding | #128 · ELO 1,460 | #323 · ELO 1,260 |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, Llama 3.1 8B Instruct: ¥14.3 per 1M output tokens, 94% less than GPT-5.4 Nano.
- On the Overall arena board, GPT-5.4 Nano ranks higher (#146; Llama 3.1 8B Instruct is #329).
- On the Japanese arena board, GPT-5.4 Nano ranks higher (#107; Llama 3.1 8B Instruct is #243).
- On the Coding arena board, GPT-5.4 Nano ranks higher (#128; Llama 3.1 8B Instruct is #323).
- For long documents, GPT-5.4 Nano: a 400,000-token context window against 131,072 for Llama 3.1 8B Instruct.
- 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-5.4 Nano
**Step 1:** Write the word: **strawberry**
**Step 2:** Count each letter **r** in the word:
- s **t r** a w b e **r r** y
There are **3** **r**’s in total (**one** after the **t**, and **two** near the end).
✅ **Answer: 3**
1.5s86 output tokens
Llama 3.1 8B Instruct
To count the number of 'r's in 'strawberry', I will break down the word into its individual letters and examine each one. Here's the step-by-step process:
1. The word 'strawberry' consists of the following letters: s-t-r-a-w-b-e-r-r-y.
2. I will start by counting the letters that are not 'r'. The word has 1 's', 1 't', 1 'a', 1 'w', 1 'b', 1 'e', 1 'y'.
3. Next, I will count the number of 'r's. The word has 2 'r's, one of which is part of the letter combination 'rr'.
Therefore, there are 2 'r's in the word 'strawberry'.
9.7s169 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-5.4 Nano
This error means that **`users` is `undefined` (or otherwise not an array)** when your component renders, so calling `users.map(...)` throws:
```js
users.map(...)
^^^^ reading 'map' of undefined
```
### Why it happens
Common causes:
- You render `<UserList />` before data has loaded (e.g., `users` comes from an async fetch).
- The parent component passes the wrong prop name (e.g., `user` instead of `users`).
- The API/parent state initializes `users` as `undefined` rather than `[]`.
- You’re destructuring a prop incorrectly upstream.
### Fix options
#### 1) Ensure `users` is always an array (recommended)
Initialize/default it in the component:
```js
function UserList({ users = []…
3.3s383 output tokens
Llama 3.1 8B Instruct
The error `TypeError: Cannot read properties of undefined (reading 'map')` typically occurs when you're trying to access a property (in this case, `map`) on an object that is `undefined`.
In your case, the `users` prop passed to the `UserList` component is `undefined`. This is likely because the parent component that renders `UserList` is not passing the `users` prop, or the prop is not being properly initialized.
To fix this error, you can add a simple check to ensure that `users` is not `undefined` before trying to call `map` on it:
```javascript
function UserList({ users }) {
if (!users) return null; // or return a default message, e.g. "No users found"
return (
<ul>
{us…
7.9s292 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-5.4 Nano
Here are key improvements (readability, performance, robustness, best practices) and a revised version of your function.
## What to improve
1. **Avoid bare `except:`**
Catch specific exceptions from `requests` (e.g., `requests.exceptions.RequestException`) to avoid hiding bugs.
2. **Prefer `raise_for_status()` or check status codes clearly**
`r.raise_for_status()` is a common pattern.
3. **Use exponential backoff (or at least a clear retry delay)**
Instead of sleeping the same amount every time, consider increasing delay.
4. **Reuse a `requests.Session()` (optional but best practice)**
If this function is called repeatedly, passing a session or creating one outside i…
4.4s718 output tokens
Llama 3.1 8B Instruct
Here are some suggestions for improving the function's readability, performance, and best practices:
**Readability Improvements**
1. **Function name**: Consider renaming `get_data` to `fetch_active_items` to better reflect its purpose.
2. **Variable naming**: Use more descriptive variable names, such as `response` instead of `r`, and `active_items` instead of `result`.
3. **Code organization**: Break up the function into smaller, more focused sections. This will improve readability and make it easier to understand the logic.
4. **Type hints**: Add type hints for function parameters and return types to make the code more self-documenting.
**Performance Improvements**
1. **Use a more effic…
29.7s533 output tokens
Frequently asked questions
- Which is cheaper, GPT-5.4 Nano or Llama 3.1 8B Instruct?
- Per 1M output tokens, GPT-5.4 Nano is ¥223.38 and Llama 3.1 8B Instruct is ¥14.3 on FastMetal (yen, before tax), so Llama 3.1 8B Instruct is cheaper.
- How do the context windows of GPT-5.4 Nano and Llama 3.1 8B Instruct compare?
- GPT-5.4 Nano takes 400,000 tokens; Llama 3.1 8B Instruct takes 131,072.
- Which ranks higher, GPT-5.4 Nano or Llama 3.1 8B Instruct?
- GPT-5.4 Nano ranks higher on the public arena (GPT-5.4 Nano #107, Llama 3.1 8B Instruct #243). Ranks move as the leaderboard updates.
- Can I use GPT-5.4 Nano and Llama 3.1 8B Instruct with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-5.4-nano" or "llama-3.1-8b-instruct" 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-5.4 Nano and Llama 3.1 8B Instruct from the browser chat and the API. No monthly fee.