DeepSeek V4.1 Flash vs gpt-oss-120b
Pricing, context window and real answers (September 2026)
DeepSeek V4.1 Flash (DeepSeek) and gpt-oss-120b (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-oss-120b | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Input (per 1M tokens) | ¥50.69 | ¥16 |
| Output (per 1M tokens) | ¥202.74 | ¥79 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥152 | ¥56 |
| Context window | 1,048,576 tokens | 131,072 tokens |
| Release date | 9/10/2026 | 8/5/2025 |
| Input modalities | text, image | text |
| Arena · overall | Unranked | #200 · ELO 1,352 |
| Arena · Japanese | Unranked | #132 · ELO 1,328 |
| Arena · coding | Unranked | #206 · ELO 1,391 |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, gpt-oss-120b: ¥79 per 1M output tokens, 61% less than DeepSeek V4.1 Flash.
- For long documents, DeepSeek V4.1 Flash: a 1,048,576-token context window against 131,072 for gpt-oss-120b.
- 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-oss-120b
**Step‑by‑step count of the letter “r” in the word “strawberry”**
1. Write out the word with each letter in order, labeling its position:
| Position | Letter |
|----------|--------|
| 1 | s |
| 2 | t |
| 3 | **r** |
| 4 | a |
| 5 | w |
| 6 | b |
| 7 | e |
| 8 | **r** |
| 9 | **r** |
|10 | y |
2. Scan the list and mark every occurrence of the letter **r**.
- Position 3 → r (first occurrence)
- Position 8 → r (second occurrence)
- Position 9 → r (third occurrence)
3. Count the marked entries: there are **3** marked positions.
**Result:** The word “strawberry” contains **3** occurrences of the letter **r**…
1.7s332 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-oss-120b
### Why the `map` can’t be called
`Array.prototype.map` is a method that exists only on **arrays** (or array‑like objects).
When you see
```
TypeError: Cannot read properties of undefined (reading 'map')
at UserList (...)
```
it means that the variable you think is an array – `users` in this case – is actually **`undefined`** (or `null`).
React is trying to evaluate this line:
```jsx
{users.map(user => <li key={user.id}>{user.name}</li>)}
```
and it crashes because you can’t read `.map` from `undefined`.
---
## Common reasons `users` ends up undefined
| Reason | Typical scenario | How it shows up |
|--------|------------------|-----------------|
| **Prop not passed** | `<Use…
7.3s1610 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-oss-120b
Below is a **step‑by‑step review** of the original `get_data` implementation followed by a **refactored version** that addresses readability, performance, and modern Python best‑practice concerns.
---
## 1. What the original function does
```python
def get_data(url, retries=3):
import requests
import time
for i in range(retries):
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
data = r.json()
result = []
for item in data:
if item["active"] == True:
result.append({"name": item["name"], "id": item["id"]})
return result…
14.4s3132 output tokens
Frequently asked questions
- Which is cheaper, DeepSeek V4.1 Flash or gpt-oss-120b?
- Per 1M output tokens, DeepSeek V4.1 Flash is ¥202.74 and gpt-oss-120b is ¥79 on FastMetal (yen, before tax), so gpt-oss-120b is cheaper.
- How do the context windows of DeepSeek V4.1 Flash and gpt-oss-120b compare?
- DeepSeek V4.1 Flash takes 1,048,576 tokens; gpt-oss-120b takes 131,072.
- Can I use DeepSeek V4.1 Flash and gpt-oss-120b with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "deepseek-v4.1-flash" or "gpt-oss-120b" 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-oss-120b from the browser chat and the API. No monthly fee.