GPT-6 Luna vs MiMo-V2.6-Flash
Pricing, context window and real answers (November 2026)
GPT-6 Luna (OpenAI) and MiMo-V2.6-Flash (Xiaomi), 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
 GPT-6 Luna | MiMo-V2.6-Flash | |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Input (per 1M tokens) | $0.1055 | $0.1477 |
| Output (per 1M tokens) | $0.5275 | $0.2954 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | $0.3693 | $0.2954 |
| Context window | 1,050,000 tokens | 1,048,576 tokens |
| Release date | 9/22/2026 | 9/21/2026 |
| Input modalities | file, image, text | text, image, video, audio |
| Throughput (p50) | 82 tokens/s (n=25) | 83 tokens/s (n=28) |
| Arena · overall | #97 · ELO 1,441 | #85 · ELO 1,448 |
| Arena · Japanese | #73 · ELO 1,421 | #133 · ELO 1,359 |
| Arena · coding | #66 · ELO 1,506 | #60 · ELO 1,510 |
Prices are FastMetal's rates, before tax.
Rankings: LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset), CC BY 4.0, snapshot 2026-10-28; category names mapped and ratings rounded by FastMetal.
Which should you pick?
- On price, MiMo-V2.6-Flash: $0.2954 per 1M output tokens, 44% less than GPT-6 Luna.
- On the Overall arena board, MiMo-V2.6-Flash ranks higher (#85; GPT-6 Luna is #97).
- On the Japanese arena board, GPT-6 Luna ranks higher (#73; MiMo-V2.6-Flash is #133).
- On the Coding arena board, MiMo-V2.6-Flash ranks higher (#60; GPT-6 Luna is #66).
- For long documents, GPT-6 Luna: a 1,050,000-token context window against 1,048,576 for MiMo-V2.6-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.
GPT-6 Luna
In **“strawberry”**, the letter **r** appears at positions 3, 8, and 9. So there are **3** “r”s.
2.7s94 output tokens
MiMo-V2.6-Flash
## Counting the 'r's in 'strawberry'
**Step 1: Write out each letter of the word individually:**
`s - t - r - a - w - b - e - r - r - y`
**Step 2: Go through each letter one by one and check if it's an 'r':**
| Position | Letter | Is it 'r'? | Running Count |
|----------|--------|------------|---------------|
| 1 | s | ❌ | 0 |
| 2 | t | ❌ | 0 |
| 3 | **r** | ✅ | **1** |
| 4 | a | ❌ | 1 |
| 5 | w | ❌ | 1 |
| 6 | b | ❌ | 1 |
| 7 | e | ❌ | 1 |
| 8 | **r** | ✅ | **2** |
| 9 | **r** | ✅ | **3** |
| 10 | y | ❌ | 3 |
**Answer: There are 3 'r's in 'strawberry'.** 🍓
The first 'r' appears at position 3 (after "st"), and the remaining two are the double 'rr' near the end of the word.
6.5s379 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…
GPT-6 Luna
`users` is `undefined` when `UserList` renders, so calling `users.map(...)` throws. This often happens because the parent didn’t pass the prop or because data hasn’t loaded yet.
If an empty list is acceptable until the data arrives, provide a default:
```jsx
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => (
<li key={user.id}>{user.name}</li>
))}
</ul>
);
}
```
Also check that the parent passes `users`:
```jsx
<UserList users={users} />
```
If `users` is loaded asynchronously, initialize it as an array:
```jsx
const [users, setUsers] = useState([]);
```
If `null` is also possible, normalize it before mapping, for example `(users ?? […
3.8s289 output tokens
MiMo-V2.6-Flash
## Cause of the Error
The error occurs because the `users` prop is `undefined` when the component first renders. When you try to call `.map()` on `undefined`, it throws:
```
TypeError: Cannot read properties of undefined (reading 'map')
```
This typically happens when:
1. The **parent component** hasn't passed `users` yet (e.g., data is still loading from an API)
2. The prop name **doesn't match** between parent and child
3. The prop is **intentionally optional** but used unconditionally
## Fixes
### 1. Default parameter value (simplest fix)
Set a default value for `users` so it's always an array:
```js
function UserList({ users = [] }) {
return (
<ul>
{users.map(user =>…
7.6s547 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):…
GPT-6 Luna
Key improvements:
- Move imports to module scope.
- Catch `requests.RequestException` instead of using a bare `except`; the current handler can hide programming errors as well as network failures.
- Call `raise_for_status()` so unsuccessful HTTP responses are handled explicitly.
- Use a list comprehension instead of building the result manually.
- Replace `item["active"] == True` with `item.get("active") is True` if the field is expected to be the Boolean `True`.
- Add backoff between attempts. A fixed two-second delay can make retries slower than necessary.
- Decide whether failures should raise an exception or return `[]`. Returning `[]` makes a failed request indistinguishable from a suc…
16.5s1713 output tokens
MiMo-V2.6-Flash
# Code Review: `get_data`
## Issues Identified
| Category | Issue |
|----------|-------|
| **Readability** | Imports inside function, vague names (`i`, `r`), no docstring/type hints |
| **Performance** | Manual loop instead of list comprehension, no `requests.Session` (no connection reuse) |
| **Best Practices** | Bare `except`, `== True`, silent failures, no backoff, no error propagation |
---
## Improved Version
```python
import time
import logging
import requests
from typing import Optional
logger = logging.getLogger(__name__)
def get_data(
url: str,
retries: int = 3,
timeout: float = 10,
backoff_factor: float = 2.0,
) -> Optional[list[dict]]:
"""
Fetch dat…
18.9s1208 output tokens
Frequently asked questions
- Which is cheaper, GPT-6 Luna or MiMo-V2.6-Flash?
- Per 1M output tokens, GPT-6 Luna is $0.5275 and MiMo-V2.6-Flash is $0.2954 on FastMetal (US dollars, before tax), so MiMo-V2.6-Flash is cheaper.
- How do the context windows of GPT-6 Luna and MiMo-V2.6-Flash compare?
- GPT-6 Luna takes 1,050,000 tokens; MiMo-V2.6-Flash takes 1,048,576.
- Which ranks higher, GPT-6 Luna or MiMo-V2.6-Flash?
- GPT-6 Luna ranks higher on the public arena (GPT-6 Luna #73, MiMo-V2.6-Flash #133). Ranks move as the leaderboard updates.
- Can I use GPT-6 Luna and MiMo-V2.6-Flash with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-6-luna" or "mimo-v2.6-flash" 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 GPT-6 Luna and MiMo-V2.6-Flash from the browser chat and the API. No monthly fee.