GPT-5.4 Nano vs MiMo-V2.6-Flash
Pricing, context window and real answers (September 2026)
GPT-5.4 Nano (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-5.4 Nano | MiMo-V2.6-Flash | |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Input (per 1M tokens) | ¥35.74 | ¥25.02 |
| Output (per 1M tokens) | ¥223.38 | ¥50.04 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥147 | ¥50 |
| Context window | 400,000 tokens | 1,048,576 tokens |
| Release date | 3/17/2026 | 9/21/2026 |
| Input modalities | file, image, text | text, image, video, audio |
| Throughput (p50) | — | — |
| Arena · overall | #146 · ELO 1,402 | Unranked |
| Arena · Japanese | #107 · ELO 1,368 | Unranked |
| Arena · coding | #128 · ELO 1,460 | Unranked |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, MiMo-V2.6-Flash: ¥50.04 per 1M output tokens, 78% less than GPT-5.4 Nano.
- For long documents, MiMo-V2.6-Flash: a 1,048,576-token context window against 400,000 for GPT-5.4 Nano.
- 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-5.4 Nano
**Step 1:** Write the word: **strawberry**
**Step 2:** Count each letter **r** in the word:
- s **t r** a w b e **r r** y
There are **3** **r**’s in total (**one** after the **t**, and **two** near the end).
✅ **Answer: 3**
1.5s86 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-5.4 Nano
This error means that **`users` is `undefined` (or otherwise not an array)** when your component renders, so calling `users.map(...)` throws:
```js
users.map(...)
^^^^ reading 'map' of undefined
```
### Why it happens
Common causes:
- You render `<UserList />` before data has loaded (e.g., `users` comes from an async fetch).
- The parent component passes the wrong prop name (e.g., `user` instead of `users`).
- The API/parent state initializes `users` as `undefined` rather than `[]`.
- You’re destructuring a prop incorrectly upstream.
### Fix options
#### 1) Ensure `users` is always an array (recommended)
Initialize/default it in the component:
```js
function UserList({ users = []…
3.3s383 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-5.4 Nano
Here are key improvements (readability, performance, robustness, best practices) and a revised version of your function.
## What to improve
1. **Avoid bare `except:`**
Catch specific exceptions from `requests` (e.g., `requests.exceptions.RequestException`) to avoid hiding bugs.
2. **Prefer `raise_for_status()` or check status codes clearly**
`r.raise_for_status()` is a common pattern.
3. **Use exponential backoff (or at least a clear retry delay)**
Instead of sleeping the same amount every time, consider increasing delay.
4. **Reuse a `requests.Session()` (optional but best practice)**
If this function is called repeatedly, passing a session or creating one outside i…
4.4s718 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-5.4 Nano or MiMo-V2.6-Flash?
- Per 1M output tokens, GPT-5.4 Nano is ¥223.38 and MiMo-V2.6-Flash is ¥50.04 on FastMetal (yen, before tax), so MiMo-V2.6-Flash is cheaper.
- How do the context windows of GPT-5.4 Nano and MiMo-V2.6-Flash compare?
- GPT-5.4 Nano takes 400,000 tokens; MiMo-V2.6-Flash takes 1,048,576.
- Can I use GPT-5.4 Nano and MiMo-V2.6-Flash with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-5.4-nano" 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-5.4 Nano and MiMo-V2.6-Flash from the browser chat and the API. No monthly fee.