DeepSeek V4 Pro vs GPT-5.6 Luna
Pricing, context window and real answers (August 2026)
DeepSeek V4 Pro (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 Pro |  GPT-5.6 Luna | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Input (per 1M tokens) | ¥341.32 | ¥35.74 |
| Output (per 1M tokens) | ¥684.42 | ¥214.44 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥684 | ¥143 |
| Context window | 1,048,576 tokens | 1,050,000 tokens |
| Release date | 4/24/2026 | 7/9/2026 |
| Input modalities | text | file, image, text |
| Arena · overall | #54 · ELO 1,458 | #63 · ELO 1,452 |
| Arena · Japanese | #22 · ELO 1,463 | #36 · ELO 1,444 |
| Arena · coding | #59 · ELO 1,502 | #69 · ELO 1,496 |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, GPT-5.6 Luna: ¥214.44 per 1M output tokens, 69% less than DeepSeek V4 Pro.
- On the Overall arena board, DeepSeek V4 Pro ranks higher (#54; GPT-5.6 Luna is #63).
- On the Japanese arena board, DeepSeek V4 Pro ranks higher (#22; GPT-5.6 Luna is #36).
- On the Coding arena board, DeepSeek V4 Pro ranks higher (#59; GPT-5.6 Luna is #69).
- For long documents, GPT-5.6 Luna: a 1,050,000-token context window against 1,048,576 for DeepSeek V4 Pro.
- 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 Pro
The word is "strawberry". Let's count the occurrences of the letter 'r' step by step:
1. Write the word: s t r a w b e r r y
2. Examine each character:
- s: not an 'r'
- t: not an 'r'
- r: is an 'r' → count 1
- a: not an 'r'
- w: not an 'r'
- b: not an 'r'
- e: not an 'r'
- r: is an 'r' → count 2
- r: is an 'r' → count 3
- y: not an 'r'
3. Total number of 'r's = 3.
Therefore, there are **3** 'r's in "strawberry".
7.3s1136 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 Pro
The error is occurring because `users` is `undefined` when the component renders, and you're trying to call `.map()` on it. This typically happens when the data hasn't been loaded yet or wasn't passed correctly to the component.
## Here are several ways to fix this:
### 1. **Add a default value with empty array (Simplest fix)**
```javascript
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => <li key={user.id}>{user.name}</li>)}
</ul>
);
}
```
### 2. **Add a conditional check**
```javascript
function UserList({ users }) {
if (!users) {
return <div>No users found</div>;
}
return (
<ul>
{users.map(user => <li key={user.id}>{user.name}<…
6.9s442 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 Pro
Here’s a review of your `get_data` function, highlighting issues and suggesting improvements.
## Overall Observations
- The function works but has several readability, robustness, and maintainability issues.
- Imports are inside the function, bare `except` hides errors, and the filtering logic is not Pythonic.
- No type hints or docstring, making it harder to use and understand.
## Specific Improvements
### 1. Move Imports to the Top of the Module
**Why:** Imports inside a function are executed every time the function is called, which is slightly slower and violates PEP 8 style guidelines.
**Before:**
```python
def get_data(url, retries=3):
import requests
import time
# ...
`…
40.5s2277 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 Pro or GPT-5.6 Luna?
- Per 1M output tokens, DeepSeek V4 Pro is ¥684.42 and GPT-5.6 Luna is ¥214.44 on FastMetal (yen, before tax), so GPT-5.6 Luna is cheaper.
- How do the context windows of DeepSeek V4 Pro and GPT-5.6 Luna compare?
- DeepSeek V4 Pro takes 1,048,576 tokens; GPT-5.6 Luna takes 1,050,000.
- Which ranks higher, DeepSeek V4 Pro or GPT-5.6 Luna?
- DeepSeek V4 Pro ranks higher on the public arena (DeepSeek V4 Pro #22, GPT-5.6 Luna #36). Ranks move as the leaderboard updates.
- Can I use DeepSeek V4 Pro and GPT-5.6 Luna with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "deepseek-v4-pro" 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 Pro and GPT-5.6 Luna from the browser chat and the API. No monthly fee.