DeepSeek V4.1 Flash vs GPT-4o-mini
Pricing, context window and real answers (September 2026)
DeepSeek V4.1 Flash (DeepSeek) and GPT-4o-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
 DeepSeek V4.1 Flash |  GPT-4o-mini | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Input (per 1M tokens) | ¥36.96 | ¥26.81 |
| Output (per 1M tokens) | ¥110.88 | ¥107.22 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥92 | ¥80 |
| Context window | 1,048,576 tokens | 128,000 tokens |
| Release date | 9/10/2026 | 7/18/2024 |
| Input modalities | text, image | text, image, file |
| Throughput (p50) | — | — |
| Arena · overall | Unranked | #251 · ELO 1,318 |
| Arena · Japanese | Unranked | #187 · ELO 1,250 |
| Arena · coding | Unranked | #259 · ELO 1,348 |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, GPT-4o-mini: ¥107.22 per 1M output tokens, 3% less than DeepSeek V4.1 Flash.
- For long documents, DeepSeek V4.1 Flash: a 1,048,576-token context window against 128,000 for GPT-4o-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.
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-4o-mini
To count the number of 'r's in the word "strawberry," we can follow these steps:
1. **Identify the word**: We have the word "strawberry."
2. **Break down the letters**: Let's write out the letters in the word:
- s
- t
- r
- a
- w
- b
- e
- r
- r
- y
3. **Locate the 'r's**: Now, we need to look for the letter 'r' among these letters.
- In the list of letters, we can see:
- The first 'r' is in the 3rd position.
- The second 'r' is in the 8th position.
- The third 'r' is in the 9th position.
4. **Count the occurrences**: We check each position we identified:
- There are three occurrences of 'r' in the word "strawberry."
5. **Conclusion**:…
2.1s239 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-4o-mini
The error you're encountering, `TypeError: Cannot read properties of undefined (reading 'map')`, indicates that the `users` prop is `undefined` when you try to call the `map` method on it.
In your `UserList` component, you're attempting to use `users.map(...)`, but if the `users` prop is not provided or is `undefined`, this will throw an error.
### Fixing the Error
1. **Default Props**: You can set a default value for the `users` prop to prevent the error when it is `undefined`.
```jsx
function UserList({ users = [] }) { // Default to an empty array
return (
<ul>
{users.map(user => <li key={user.id}>{user.name}</li>)}
</ul>
);
}
```…
5.0s511 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-4o-mini
Your Python function, `get_data`, essentially performs a GET request, handles retries on failure, and processes the response JSON to return a filtered list of dictionaries. Below, I've highlighted several improvements regarding readability, performance, and best practices:
1. **Avoid blanket exceptions**: It’s best practice to specify which exceptions you expect, allowing for better debugging and error handling.
2. **Use `requests` built-in exception handling**: Handle specific requests exceptions, such as `requests.exceptions.RequestException`.
3. **Improve condition checks**: Check for `item['active']` directly instead of comparing to `True`, which is more Pythonic.
4. **Use `time.sl…
8.8s628 output tokens
Frequently asked questions
- Which is cheaper, DeepSeek V4.1 Flash or GPT-4o-mini?
- Per 1M output tokens, DeepSeek V4.1 Flash is ¥110.88 and GPT-4o-mini is ¥107.22 on FastMetal (yen, before tax), so GPT-4o-mini is cheaper.
- How do the context windows of DeepSeek V4.1 Flash and GPT-4o-mini compare?
- DeepSeek V4.1 Flash takes 1,048,576 tokens; GPT-4o-mini takes 128,000.
- Can I use DeepSeek V4.1 Flash and GPT-4o-mini with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "deepseek-v4.1-flash" or "gpt-4o-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 DeepSeek V4.1 Flash and GPT-4o-mini from the browser chat and the API. No monthly fee.