DeepSeek V4.1 Flash vs GPT-5.6 Luna
Pricing, context window and real answers (October 2026)
DeepSeek V4.1 Flash (DeepSeek) and GPT-5.6 Luna (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 Luna | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Input (per 1M tokens) | $0.211 | $0.211 |
| Output (per 1M tokens) | $0.633 | $1.27 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | $0.5275 | $0.844 |
| 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 |
| Throughput (p50) | 194 tokens/s (n=27) | 174 tokens/s (n=27) |
| Arena · overall | #34 · ELO 1,476 | #75 · ELO 1,453 |
| Arena · Japanese | #37 · ELO 1,453 | #56 · ELO 1,433 |
| Arena · coding | #30 · ELO 1,523 | #76 · ELO 1,502 |
Prices are FastMetal's rates, before tax.
Rankings: LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset), CC BY 4.0, snapshot 2026-10-22; category names mapped and ratings rounded by FastMetal.
Which should you pick?
- On price, DeepSeek V4.1 Flash: $0.633 per 1M output tokens, 50% less than GPT-5.6 Luna.
- On the Overall arena board, DeepSeek V4.1 Flash ranks higher (#34; GPT-5.6 Luna is #75).
- On the Japanese arena board, DeepSeek V4.1 Flash ranks higher (#37; GPT-5.6 Luna is #56).
- On the Coding arena board, DeepSeek V4.1 Flash ranks higher (#30; GPT-5.6 Luna is #76).
- For long documents, GPT-5.6 Luna: 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 Luna
The word **“strawberry”** is spelled:
**s – t – r – a – w – b – e – r – r – y**
The letter **“r”** appears at positions **3, 8, and 9**.
**Answer: 3**
3.0s120 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 Luna
`users` is `undefined` when `UserList` renders, so calling `.map()` on it fails. `.map()` only exists on arrays.
Common causes include:
- The parent does not pass a `users` prop.
- The prop name differs, such as `userList` instead of `users`.
- Users are loaded asynchronously and are initially undefined.
- The API response has a different shape, such as `{ data: [...] }`.
### Provide a default value
```jsx
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => (
<li key={user.id}>{user.name}</li>
))}
</ul>
);
}
```
The parent should pass an array:
```jsx
<UserList users={users} />
```
If the list comes from state, initialize it as an empty…
4.8s507 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 Luna
### Main issues
- Imports should be at module level, not inside the function.
- A bare `except:` catches everything, including programming errors and `KeyboardInterrupt`.
- The function sleeps even after the final failed attempt.
- It retries all HTTP statuses, including permanent errors such as `404`.
- `status_code == 200` excludes other successful `2xx` responses.
- `if item["active"] == True` should generally be `if item.get("active") is True`.
- A `requests.Session` can reuse connections and improve performance.
- Returning `[]` for every failure makes it impossible to distinguish “no active items” from “request failed.”
- Retries should generally use backoff rather than a fixed delay.…
23.3s1978 output tokens
Frequently asked questions
- Which is cheaper, DeepSeek V4.1 Flash or GPT-5.6 Luna?
- Per 1M output tokens, DeepSeek V4.1 Flash is $0.633 and GPT-5.6 Luna is $1.27 on FastMetal (US dollars, before tax), so DeepSeek V4.1 Flash is cheaper.
- How do the context windows of DeepSeek V4.1 Flash and GPT-5.6 Luna compare?
- DeepSeek V4.1 Flash takes 1,048,576 tokens; GPT-5.6 Luna takes 1,050,000.
- Which ranks higher, DeepSeek V4.1 Flash or GPT-5.6 Luna?
- DeepSeek V4.1 Flash ranks higher on the public arena (DeepSeek V4.1 Flash #37, GPT-5.6 Luna #56). Ranks move as the leaderboard updates.
- Can I use DeepSeek V4.1 Flash and GPT-5.6 Luna with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "deepseek-v4.1-flash" or "gpt-5.6-luna" 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 Luna from the browser chat and the API. No monthly fee.