Model comparison

GPT-5.6 Sol vs MiniMax M3.1 Flash

Pricing, context window and real answers (October 2026)

GPT-5.6 Sol (OpenAI) and MiniMax M3.1 Flash (MiniMax), 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-5.6 Solminimax logoMiniMax M3.1 Flash
ProviderOpenAIMiniMax
Input (per 1M tokens)$4.40$0.1583
Output (per 1M tokens)$22.00$0.633
Typical cost (1,000 in + 500 out tokens × 1,000 calls)$15.40$0.4748
Context window1,050,000 tokens1,048,576 tokens
Release date7/9/202610/19/2026
Input modalitiesfile, image, texttext, image
Throughput (p50)207 tokens/s (n=26)—
Arena · overall#21 · ELO 1,485Unranked
Arena · Japanese#12 · ELO 1,495Unranked
Arena · coding#16 · ELO 1,534Unranked

Prices are FastMetal's rates, before tax.

Rankings: LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset), CC BY 4.0, snapshot 2026-10-15; category names mapped and ratings rounded by FastMetal.

Which should you pick?

  • On price, MiniMax M3.1 Flash: $0.633 per 1M output tokens, 97% less than GPT-5.6 Sol.
  • For long documents, GPT-5.6 Sol: a 1,050,000-token context window against 1,048,576 for MiniMax M3.1 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-5.6 Sol

“strawberry” can be split into letters: s, t, **r**, a, w, b, e, **r**, **r**, y The letter **r** appears **3 times**.
3.0s78 output tokens

MiniMax M3.1 Flash

1. Spell **strawberry**: **s – t – r – a – w – b – e – r – r – y** 2. Count the letter **r**: it appears at positions **3, 8, and 9**. **Total: 3 “r”s.**
3.0s193 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-5.6 Sol

The error occurs because `users` is `undefined` when `UserList` renders, so JavaScript cannot call `.map()` on it. This often happens when data is loaded asynchronously or the parent does not pass the prop. ### Fix options **1. Give `users` a default value:** ```jsx function UserList({ users = [] }) { return ( <ul> {users.map(user => ( <li key={user.id}>{user.name}</li> ))} </ul> ); } ``` Note that this handles `undefined`, but not an explicitly passed `null`. **2. Handle missing or invalid data explicitly:** ```jsx function UserList({ users }) { if (!Array.isArray(users)) { return <p>No users available.</p>; } return ( <ul> {users.m…
7.1s395 output tokens

MiniMax M3.1 Flash

The error occurs because `users` is `undefined` when `UserList` renders. Calling `.map()` on `undefined` causes the `TypeError`. This commonly happens when: - `<UserList />` is rendered without passing the `users` prop. - The prop has a different name, such as `user`. - Data is still loading and the parent initializes its state as `undefined`. - An API response is accessed before it exists. ### Immediate fix Provide a default empty array: ```jsx function UserList({ users = [] }) { return ( <ul> {users.map(user => ( <li key={user.id}>{user.name}</li> ))} </ul> ); } ``` Then pass the actual data from the parent: ```jsx const [users, setUsers] = useState([…
7.3s744 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-5.6 Sol

### Issues and suggested improvements - Move imports to module scope. - Avoid bare `except:`; it catches programming errors and interrupts such as `KeyboardInterrupt`. - Use `response.raise_for_status()` instead of manually checking only for `200`; other 2xx responses may also be valid. - Retry only transient failures, such as timeouts, connection errors, `429`, and selected `5xx` responses. Retrying most `4xx` errors is not useful. - Use exponential backoff rather than a fixed delay. - Avoid sleeping after the final failed attempt. - Replace `item["active"] == True` with `item.get("active") is True`. - Use a list comprehension for the transformation. - Add type hints and validate arguments…
33.1s1646 output tokens

MiniMax M3.1 Flash

The function is understandable, but it has a few important issues: - `except:` catches programming errors such as `KeyError`, `TypeError`, and even `KeyboardInterrupt`. - Every non-200 response is retried, including permanent failures such as 400, 401, and 404. - It sleeps after the final failed attempt. - Returning `[]` makes a failed request indistinguishable from a successful response containing no active records. - A fixed delay can cause clients to retry simultaneously; exponential backoff and `Retry-After` are preferable. - `item["active"] == True` should be `item.get("active") is True` if the API contract says it is a JSON boolean. - A `Session` can reuse connections, while `requests…
146.5s

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Frequently asked questions

Which is cheaper, GPT-5.6 Sol or MiniMax M3.1 Flash?
Per 1M output tokens, GPT-5.6 Sol is $22.00 and MiniMax M3.1 Flash is $0.633 on FastMetal (US dollars, before tax), so MiniMax M3.1 Flash is cheaper.
How do the context windows of GPT-5.6 Sol and MiniMax M3.1 Flash compare?
GPT-5.6 Sol takes 1,050,000 tokens; MiniMax M3.1 Flash takes 1,048,576.
Can I use GPT-5.6 Sol and MiniMax M3.1 Flash with the same API key?
Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-5.6-sol" or "minimax-m3.1-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-5.6 Sol and MiniMax M3.1 Flash from the browser chat and the API. No monthly fee.

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