DeepSeek V4 Flash vs GPT-5.6 Terra
Pricing, context window and real answers (August 2026)
DeepSeek V4 Flash (DeepSeek) and GPT-5.6 Terra (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 Flash |  GPT-5.6 Terra | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Input (per 1M tokens) | ¥16.08 | ¥357.4 |
| Output (per 1M tokens) | ¥32.17 | ¥2,144.4 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥32 | ¥1,430 |
| 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 | #79 · ELO 1,436 | #34 · ELO 1,468 |
| Arena · Japanese | #67 · ELO 1,390 | Unranked |
| Arena · coding | #80 · ELO 1,484 | #22 · ELO 1,521 |
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 Flash: ¥32.17 per 1M output tokens, 98% less than GPT-5.6 Terra.
- On the Overall arena board, GPT-5.6 Terra ranks higher (#34; DeepSeek V4 Flash is #79).
- On the Coding arena board, GPT-5.6 Terra ranks higher (#22; DeepSeek V4 Flash is #80).
- For long documents, GPT-5.6 Terra: a 1,050,000-token context window against 1,048,576 for DeepSeek V4 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 Flash
The word "strawberry" is spelled as s-t-r-a-w-b-e-r-r-y. I go through each letter: s (no), t (no), r (yes, first), a (no), w (no), b (no), e (no), r (yes, second), r (yes, third), y (no). Thus, there are three 'r's.
3.9s251 output tokens
GPT-5.6 Terra
“strawberry” is spelled:
s t **r** a w b e **r** **r** y
There are **3** occurrences of the letter **r**.
3.6s71 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 Flash
The error occurs because `users` is `undefined` when the component tries to call `.map()` on it.
## Why this happens
Your component expects `users` to be an array, but it's receiving `undefined` as a prop. This typically happens when:
1. The parent component doesn't pass the `users` prop
2. The data fetching hasn't completed yet (async data loading)
3. The API response is missing the expected data structure
## How to fix it
### Option 1: Default prop value (Recommended for initial state)
```jsx
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => <li key={user.id}>{user.name}</li>)}
</ul>
);
}
```
### Option 2: Guard clause (More explicit)
```jsx
func…
6.3s422 output tokens
GPT-5.6 Terra
The error means `users` is `undefined` when `UserList` renders, so React tries to evaluate:
```js
users.map(...)
```
But `.map()` only exists on arrays.
Common causes:
- The parent component does not pass a `users` prop.
- The user data is loaded asynchronously and has not arrived on the first render.
- The API response shape differs from what you expect, e.g. data is in `response.data.users` rather than `response.data`.
- The parent initializes its user state as `undefined` or `null`.
## Fix options
### 1. Initialize users as an empty array
In the parent component:
```jsx
const [users, setUsers] = useState([]);
```
Then pass it normally:
```jsx
<UserList users={users} />
```
An e…
5.8s559 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 Flash
Here's a comprehensive review with suggested improvements:
## Issues Identified:
1. **Import at wrong location** - Imports inside functions are inefficient and non-standard
2. **Bare `except:` clause** - Catches all exceptions including `KeyboardInterrupt`, `SystemExit`
3. **Missing error handling granularity** - Should distinguish between retryable and non-retryable errors
4. **Duplicate sleep logic** - Same sleep in `else` and `except` blocks
5. **Unnecessary variable** - `data` is only used once
6. **Inefficient list building** - Manual loop instead of list comprehension
7. **Hardcoded error message** - No logging or error context
8. **Missing type hints** - No type annotations
9. **Fun…
12.6s987 output tokens
GPT-5.6 Terra
Here are the main areas to improve:
### Issues in the original code
- **Imports inside the function**: Move `requests` and `time` imports to module scope.
- **Bare `except:`**: This catches everything, including `KeyboardInterrupt` and programming errors. Catch `requests.RequestException` (and possibly JSON decoding errors) instead.
- **Status code check**: Prefer `response.raise_for_status()` or `response.ok` over manually checking only for `200`. Successful responses can include other 2xx status codes.
- **`== True`**: Use `if item.get("active"):` instead.
- **Potential `KeyError`**: `item["name"]`, `item["id"]`, and `item["active"]` can fail if the API response is incomplete. Use `.get(…
10.6s939 output tokens
Frequently asked questions
- Which is cheaper, DeepSeek V4 Flash or GPT-5.6 Terra?
- Per 1M output tokens, DeepSeek V4 Flash is ¥32.17 and GPT-5.6 Terra is ¥2,144.4 on FastMetal (yen, before tax), so DeepSeek V4 Flash is cheaper.
- How do the context windows of DeepSeek V4 Flash and GPT-5.6 Terra compare?
- DeepSeek V4 Flash takes 1,048,576 tokens; GPT-5.6 Terra takes 1,050,000.
- Which ranks higher, DeepSeek V4 Flash or GPT-5.6 Terra?
- GPT-5.6 Terra ranks higher on the public arena (DeepSeek V4 Flash #67, GPT-5.6 Terra #34). Ranks move as the leaderboard updates.
- Can I use DeepSeek V4 Flash and GPT-5.6 Terra with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "deepseek-v4-flash" or "gpt-5.6-terra" 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 Flash and GPT-5.6 Terra from the browser chat and the API. No monthly fee.