GPT-5 Mini vs MiMo-V2.5
Pricing, context window and real answers (September 2026)
GPT-5 Mini (OpenAI) and MiMo-V2.5 (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 Mini | MiMo-V2.5 | |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Input (per 1M tokens) | ¥44.68 | ¥25.02 |
| Output (per 1M tokens) | ¥357.4 | ¥50.04 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥223 | ¥50 |
| Context window | 400,000 tokens | 1,050,000 tokens |
| Release date | 8/7/2025 | 4/22/2026 |
| Input modalities | text, image, file | text, audio, image, video |
| Throughput (p50) | — | — |
| Arena · overall | #163 · ELO 1,390 | #96 · ELO 1,434 |
| Arena · Japanese | #109 · ELO 1,365 | #141 · ELO 1,321 |
| Arena · coding | #172 · ELO 1,431 | #81 · ELO 1,491 |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, MiMo-V2.5: ¥50.04 per 1M output tokens, 86% less than GPT-5 Mini.
- On the Overall arena board, MiMo-V2.5 ranks higher (#96; GPT-5 Mini is #163).
- On the Japanese arena board, GPT-5 Mini ranks higher (#109; MiMo-V2.5 is #141).
- On the Coding arena board, MiMo-V2.5 ranks higher (#81; GPT-5 Mini is #172).
- For long documents, MiMo-V2.5: a 1,050,000-token context window against 400,000 for GPT-5 Mini.
- 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 Mini
We examine the word "strawberry" letter by letter:
1. s — not an r (count = 0)
2. t — not an r (count = 0)
3. r — an r (count = 1)
4. a — not an r (count = 1)
5. w — not an r (count = 1)
6. b — not an r (count = 1)
7. e — not an r (count = 1)
8. r — an r (count = 2)
9. r — an r (count = 3)
10. y — not an r (count = 3)
Total number of 'r's = 3.
5.2s382 output tokens
MiMo-V2.5
# Counting the 'r's in 'strawberry'
Let me spell the word out letter by letter:
| Position | Letter | Is it 'r'? |
|----------|--------|------------|
| 1 | **s** | ❌ |
| 2 | **t** | ❌ |
| 3 | **r** | ✅ (1) |
| 4 | **a** | ❌ |
| 5 | **w** | ❌ |
| 6 | **b** | ❌ |
| 7 | **e** | ❌ |
| 8 | **r** | ✅ (2) |
| 9 | **r** | ✅ (3) |
| 10 | **y** | ❌ |
## Answer: **3** 'r's
The three r's are found at positions **3**, **8**, and **9** in the word.
14.9s313 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 Mini
That error means users is undefined when you call users.map(...). map is an Array method, so calling it on undefined throws "Cannot read properties of undefined (reading 'map')".
Common causes
- The parent component didn't pass a users prop.
- users is still loading (e.g. fetched asynchronously) and is null/undefined until the fetch completes.
- The parent initialized users to null instead of an empty array.
How to fix
Choose one or more of these approaches depending on your app:
1) Initialize the parent state as an array
If users is state in the parent, initialize it to [] so the child always receives an array:
const [users, setUsers] = useState([]);
// fetch and setUsers(data)
2) Give…
8.0s966 output tokens
MiMo-V2.5
This error occurs because your `users` prop is `undefined` when the component tries to call `.map()` on it. This usually happens when:
1. The parent component doesn't pass a `users` prop
2. The `users` data is still loading (asynchronous operation)
3. There's a typo in the prop name when passing from parent
## How to Fix It
### 1. Add Default Props or Prop Validation
Add a default value for the `users` prop:
```jsx
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => <li key={user.id}>{user.name}</li>)}
</ul>
);
}
// Or with PropTypes
import PropTypes from 'prop-types';
function UserList({ users }) {
return (
<ul>
{users.map(user => <li key…
11.2s808 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 Mini
Thanks — this is a nice simple function, but it can be improved in several ways: move imports to module scope, avoid broad except:, use connection pooling (Session) for performance, use specific exception types, add logging and docstring, avoid comparing to True, use list comprehensions, validate JSON shape, and implement backoff (or use urllib3 Retry).
Below are two improved alternatives and a short rationale for the changes.
1) A simple, clear improvement (small changes, explicit errors, backoff + jitter)
- Moves imports to top-level
- Uses requests.Session for connection pooling
- Catches specific exceptions (requests.RequestException, ValueError)
- Uses list comprehension and item.get(…
13.6s1702 output tokens
MiMo-V2.5
# Code Review: `get_data` Function
## Issues Identified
| Category | Issue | Severity |
|----------|-------|----------|
| Style | Imports inside function body | Medium |
| Reliability | Bare `except` catches `KeyboardInterrupt`/`SystemExit` | High |
| Reliability | Silent failures — no logging | Medium |
| Readability | `== True` comparison (redundant) | Low |
| Readability | No docstring or type hints | Medium |
| Performance | Could use list comprehension | Low |
| Robustness | No exponential backoff on retries | Medium |
| Robustness | `r.json()` can fail silently under bare except | High |
| Style | Uninformative variable names (`r`, `i`) | Low |
| Config | Magic numbers hardcoded (`ti…
33.6s1854 output tokens
Frequently asked questions
- Which is cheaper, GPT-5 Mini or MiMo-V2.5?
- Per 1M output tokens, GPT-5 Mini is ¥357.4 and MiMo-V2.5 is ¥50.04 on FastMetal (yen, before tax), so MiMo-V2.5 is cheaper.
- How do the context windows of GPT-5 Mini and MiMo-V2.5 compare?
- GPT-5 Mini takes 400,000 tokens; MiMo-V2.5 takes 1,050,000.
- Which ranks higher, GPT-5 Mini or MiMo-V2.5?
- GPT-5 Mini ranks higher on the public arena (GPT-5 Mini #109, MiMo-V2.5 #141). Ranks move as the leaderboard updates.
- Can I use GPT-5 Mini and MiMo-V2.5 with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-5-mini" or "mimo-v2.5" 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 Mini and MiMo-V2.5 from the browser chat and the API. No monthly fee.