> For the complete documentation index, see [llms.txt](https://docs.flock.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.flock.io/flock-tokenomics/network-participation/fomo/model-users.md).
# Model Users
Model Users are developers, AI companies, agents, applications, and enterprises that consume inference through the FLock API Platform.
They are the demand engine of FOMO.
In traditional API markets, users pay for inference and receive no upside. The more they use, the more they pay. FOMO changes this by allowing users to become economically aligned with the models they rely on.
#### How Rewards Are Calculated
* A daily reward is paid out to participants based on their activity on supported **Model Tokens** (MTs).
* You **don't have to stake to earn** — using the API alone makes you eligible — but stakers earn more than non-stakers at equal usage.
* If you participate in multiple MTs, your daily reward is the sum across all of them. FLOCK rewards go to a single FLOCK balance you can claim; MT-token rewards stay in each MT's own token.
#### Benefits for Model Users
Model Users can benefit from FOMO in three ways.
First, they can access **lower inference costs** through $MT staking discounts. By staking the $MT associated with a model, users can unlock reduced API pricing.
Second, they may earn **retroactive rewards** based on their actual API usage. Since reward share is usage-weighted, users who generate meaningful demand can receive $FLOCK and $MT incentives.
Third, their usage contributes to **$MT buyback and burn**, meaning heavy users help strengthen the model economy they depend on.
#### Usage as the Core Reward Signal
FOMO is designed to reward actual consumption.
A user who spends more on a specific model contributes more to that model’s economic activity. This increases the model’s reward score and the user’s share within that model’s reward pool.
The core logic is:
```
more real usage
→ higher model activity
→ more emissions to the model
→ larger reward opportunity for active users
```
This avoids the problem of rewarding idle capital. Staking alone is not enough. If there is no usage, there should be no meaningful reward.
In rough order of impact:
1. **Spend more on API in a meaningful way** Migrate your OpenClaw or Hermes Agent to the API platform and generate more natural API spending.
2. **Stake the MT you use.** Any positive stake strictly beats not staking at equal usage. The lift starts modest while `α` is high, then grows as `α` steps down toward `0.5`.
3. **Hold gmFLOCK.** Multiplies your score on *every* MT by up to `1 + λ`. The biggest "set and forget" lever, and it stacks regardless of staking.
4. **Spread across multiple MTs you actually use.** Each MT is its own pool; activity on MT *A* never dilutes your share on MT *B*.
You don't control the denominator — other people staking or spending on the same MT will shrink your share. Your job is to pick MTs where your relative position is strong.
#### Common Reasons People Earn Less Than Expected
* **No usage on an MT = no reward on that MT** — even if you stake heavily. Staking alone earns nothing; you have to also use the MT.
* **Reward pools follow API usage, not stakers.** An MT with stakers but zero API activity that day pays out nothing. The daily FLOCK emission flows toward MTs that are actually being used.
* **Per-MT incentive pools are time-limited.** They drip out over \~180 days from the MT's launch and then stop. Earlier participation captures more of that pool.
* **Your reward "didn't go up" is impossible** — the protocol only ever increases your claimable total. If your dashboard shows the same number, it just means you didn't earn anything new in the last run, not that something was lost.
#### Season 1 vs Season 2 Usage Mapping
In **Season 1**, API usage can be automatically mapped to supported model tokens. This lowers user friction during the transition period and helps bootstrap early behavior.
In **Season 2 and forward**, users will need to use the correct **model ID** for usage to count toward a specific $MT. This improves precision and prevents ambiguity between different model deployments.
This distinction matters because FOMO is model-specific. A user’s activity should strengthen the $MT linked to the model they actually use.
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