DeepSeek V4.1 Flash vs GPT-4.1 Nano
Pricing, context window and real answers (September 2026)
DeepSeek V4.1 Flash (DeepSeek) and GPT-4.1 Nano (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
 DeepSeek V4.1 Flash |  GPT-4.1 Nano | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Input (per 1M tokens) | ¥36.96 | ¥17.87 |
| Output (per 1M tokens) | ¥110.88 | ¥71.48 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥92 | ¥54 |
| Context window | 1,048,576 tokens | 1,047,576 tokens |
| Release date | 9/10/2026 | 4/14/2025 |
| Input modalities | text, image | image, text, file |
| Throughput (p50) | — | — |
| Arena · overall | Unranked | #243 · ELO 1,322 |
| Arena · Japanese | Unranked | #195 · ELO 1,234 |
| Arena · coding | Unranked | #226 · ELO 1,374 |
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, 36% less than DeepSeek V4.1 Flash.
- For long documents, DeepSeek V4.1 Flash: a 1,048,576-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.
DeepSeek V4.1 Flash
The word **“strawberry”** has **3** letter **“r”**s.
Step-by-step:
1. Spell it out: **s t r a w b e r r y**
2. Find each **r**:
- Position 3: **r** in “str”
- Position 8: first **r** in “berry”
- Position 9: second **r** in “berry”
3. Total: **3**
**Answer: 3**
7.5s226 output tokens
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
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…
DeepSeek V4.1 Flash
The error means that `users` is `undefined` when `UserList` renders, so JavaScript cannot call `.map()` on it.
Your component expects a `users` prop:
```jsx
function UserList({ users }) {
return (
<ul>
{users.map(user => <li key={user.id}>{user.name}</li>)}
</ul>
);
}
```
But it is being rendered without `users`, or `users` has not loaded yet.
Common causes:
1. Parent renders it without the prop:
```jsx
<UserList />
```
instead of:
```jsx
<UserList users={users} />
```
2. The prop name is different:
```jsx
<UserList userList={users} />
```
3. The data is fetched asynchronously and is initially `undefined`:
```jsx
const [users, setUsers] = useState();
useEffect(…
16.0s945 output tokens
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
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):…
DeepSeek V4.1 Flash
Here are the main improvements:
- Move imports to module level.
- Avoid bare `except`; catch specific exceptions.
- Use a `requests.Session` for connection reuse.
- Retry only transient network/HTTP failures, not all errors.
- Use exponential backoff and don’t sleep after the final attempt.
- Use a list comprehension for readability/performance.
- Add type hints, logging, constants, and basic JSON validation.
- Avoid `== True`; use `is True` or a truthy check depending on API contract.
Example refactor:
```python
from __future__ import annotations
import logging
import time
from typing import Any
import requests
logger = logging.getLogger(__name__)
TIMEOUT = (3.05, 10) # connect time…
31.5s5142 output tokens
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
Frequently asked questions
- Which is cheaper, DeepSeek V4.1 Flash or GPT-4.1 Nano?
- Per 1M output tokens, DeepSeek V4.1 Flash is ¥110.88 and GPT-4.1 Nano is ¥71.48 on FastMetal (yen, before tax), so GPT-4.1 Nano is cheaper.
- How do the context windows of DeepSeek V4.1 Flash and GPT-4.1 Nano compare?
- DeepSeek V4.1 Flash takes 1,048,576 tokens; GPT-4.1 Nano takes 1,047,576.
- Can I use DeepSeek V4.1 Flash and GPT-4.1 Nano with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "deepseek-v4.1-flash" or "gpt-4.1-nano" 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 DeepSeek V4.1 Flash and GPT-4.1 Nano from the browser chat and the API. No monthly fee.