> For the complete documentation index, see [llms.txt](https://whitepaper.gamerge.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://whitepaper.gamerge.ai/gamerge-ecosystem/ecosystem/gaming-model/one-on-one-competitive-matches.md).

# One-on-One Competitive Matches

\
For players seeking competitive thrill and rewards:

<figure><img src="/files/WUolvqLCVdXVSUc4Lkla" alt=""><figcaption><p>1v1 match flow</p></figcaption></figure>

• **Burning and Referral Model:**\
\
\&#xNAN;**– Winner Takes 90% of the Pool:** Incentivizes skillful play and competitive spirit.\
\
\&#xNAN;**– 5% Burned (for GMG Tokens):** This mechanism reduces the total supply of GMG over time, potentially increasing the token’s value and benefiting all holders.\
\
\&#xNAN;**– 2.5% to Referral of Players 1 and 2:** Encourages users to refer others, promoting organic growth and community building.

**• Multiple Pool Options:** Players can choose from various stakes (e.g., 1 USDT, 2 USDT, 5 USDT, and 10 USDT), accommodating different risk appetites.

**• Crypto Selection:** The ability to choose which cryptocurrency to wager adds flexibility and caters

to users who prefer specific tokens.

**• Automated Matchmaking:** Our system pairs online players efficiently, ensuring minimal wait

times and a seamless gaming experience.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://whitepaper.gamerge.ai/gamerge-ecosystem/ecosystem/gaming-model/one-on-one-competitive-matches.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.