DeepSeek V4.1 Flash vs GPT-5.6 Sol
Pricing, context window and real answers (September 2026)
DeepSeek V4.1 Flash (DeepSeek) and GPT-5.6 Sol (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-5.6 Sol | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Input (per 1M tokens) | ¥50.69 | ¥893.5 |
| Output (per 1M tokens) | ¥202.74 | ¥5,361 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥152 | ¥3,574 |
| Context window | 1,048,576 tokens | 1,050,000 tokens |
| Release date | 9/10/2026 | 7/9/2026 |
| Input modalities | text, image | file, image, text |
| Arena · overall | Unranked | #17 · ELO 1,483 |
| Arena · Japanese | Unranked | #2 · ELO 1,522 |
| Arena · coding | Unranked | #14 · ELO 1,529 |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, DeepSeek V4.1 Flash: ¥202.74 per 1M output tokens, 96% less than GPT-5.6 Sol.
- For long documents, GPT-5.6 Sol: a 1,050,000-token context window against 1,048,576 for DeepSeek V4.1 Flash.
- 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-5.6 Sol
“strawberry” can be split into letters:
s, t, **r**, a, w, b, e, **r**, **r**, y
The letter **r** appears **3 times**.
3.0s78 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-5.6 Sol
The error occurs because `users` is `undefined` when `UserList` renders, so JavaScript cannot call `.map()` on it. This often happens when data is loaded asynchronously or the parent does not pass the prop.
### Fix options
**1. Give `users` a default value:**
```jsx
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => (
<li key={user.id}>{user.name}</li>
))}
</ul>
);
}
```
Note that this handles `undefined`, but not an explicitly passed `null`.
**2. Handle missing or invalid data explicitly:**
```jsx
function UserList({ users }) {
if (!Array.isArray(users)) {
return <p>No users available.</p>;
}
return (
<ul>
{users.m…
7.1s395 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-5.6 Sol
### Issues and suggested improvements
- Move imports to module scope.
- Avoid bare `except:`; it catches programming errors and interrupts such as `KeyboardInterrupt`.
- Use `response.raise_for_status()` instead of manually checking only for `200`; other 2xx responses may also be valid.
- Retry only transient failures, such as timeouts, connection errors, `429`, and selected `5xx` responses. Retrying most `4xx` errors is not useful.
- Use exponential backoff rather than a fixed delay.
- Avoid sleeping after the final failed attempt.
- Replace `item["active"] == True` with `item.get("active") is True`.
- Use a list comprehension for the transformation.
- Add type hints and validate arguments…
33.1s1646 output tokens
Frequently asked questions
- Which is cheaper, DeepSeek V4.1 Flash or GPT-5.6 Sol?
- Per 1M output tokens, DeepSeek V4.1 Flash is ¥202.74 and GPT-5.6 Sol is ¥5,361 on FastMetal (yen, before tax), so DeepSeek V4.1 Flash is cheaper.
- How do the context windows of DeepSeek V4.1 Flash and GPT-5.6 Sol compare?
- DeepSeek V4.1 Flash takes 1,048,576 tokens; GPT-5.6 Sol takes 1,050,000.
- Can I use DeepSeek V4.1 Flash and GPT-5.6 Sol with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "deepseek-v4.1-flash" or "gpt-5.6-sol" 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-5.6 Sol from the browser chat and the API. No monthly fee.