FastMetal

Model comparison

GPT-6 Luna vs Kimi K3.1 Pro

Pricing, context window and real answers (October 2026)

GPT-6 Luna (OpenAI) and Kimi K3.1 Pro (Moonshot), 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

openai logoGPT-6 Lunamoonshotai logoKimi K3.1 Pro
ProviderOpenAIMoonshot
Input (per 1M tokens)$0.1055$2.11
Output (per 1M tokens)$0.5275$10.55
Typical cost (1,000 in + 500 out tokens × 1,000 calls)$0.3693$7.39
Context window1,050,000 tokens1,048,576 tokens
Release date9/22/202610/23/2026
Input modalitiesfile, image, texttext, image, video
Throughput (p50)111 tokens/s (n=23)—
Arena · overall#91 · ELO 1,442Unranked
Arena · Japanese#70 · ELO 1,422Unranked
Arena · coding#65 · ELO 1,507Unranked

Prices are FastMetal's rates, before tax.

Rankings: LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset), CC BY 4.0, snapshot 2026-10-22; category names mapped and ratings rounded by FastMetal.

Which should you pick?

  • On price, GPT-6 Luna: $0.5275 per 1M output tokens, 95% less than Kimi K3.1 Pro.
  • For long documents, GPT-6 Luna: a 1,050,000-token context window against 1,048,576 for Kimi K3.1 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.

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

Kimi K3.1 Pro

There are **3** ‘r’s in **“strawberry.”** 1. Write out each letter: **s – t – r – a – w – b – e – r – r – y** 2. Locate the ‘r’s: positions **3, 8, and 9**. 3. Count them: **1 + 1 + 1 = 3**.
11.3s

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

Kimi K3.1 Pro

## What's causing the error The error means `users` is `undefined` when the component renders, and you can't call `.map()` on `undefined`. The component itself is fine — the problem is that whatever renders `<UserList />` either isn't passing the prop, or is passing a value that isn't an array yet. The most common cause is **async data fetching**: the parent renders `UserList` before the data has loaded. ```jsx function App() { const [users, setUsers] = useState(); // ❌ undefined on first render useEffect(() => { fetch('/api/users') .then(res => res.json()) .then(setUsers); }, []); return <UserList users={users} />; // users is undefined here initially } ``` On…
36.3s

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

Kimi K3.1 Pro

The timeout and bounded retry loop are good starting points. The biggest issue is that failures—and even programming errors—are silently converted into retries and then `[]`. ### Suggested improvements - **Avoid bare `except:`**. It catches `KeyboardInterrupt`, `KeyError`, JSON/schema errors, and other bugs. Retry only expected transient network failures; let malformed responses fail visibly. - **Use a deliberate retry policy**. Retrying `401`, `403`, or `404` usually wastes time. Retry only transient statuses, use exponential backoff, and avoid sleeping after the final attempt. - **Do not use `[]` for both failure and no results**. Prefer raising the final exception so callers can disting…
560.2s

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Frequently asked questions

Which is cheaper, GPT-6 Luna or Kimi K3.1 Pro?
Per 1M output tokens, GPT-6 Luna is $0.5275 and Kimi K3.1 Pro is $10.55 on FastMetal (US dollars, before tax), so GPT-6 Luna is cheaper.
How do the context windows of GPT-6 Luna and Kimi K3.1 Pro compare?
GPT-6 Luna takes 1,050,000 tokens; Kimi K3.1 Pro takes 1,048,576.
Can I use GPT-6 Luna and Kimi K3.1 Pro with the same API key?
Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-6-luna" or "kimi-k3.1-pro" 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 Kimi K3.1 Pro from the browser chat and the API. No monthly fee.

Create a free accountTry GPT-6 Luna in chatTry Kimi K3.1 Pro in chat

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