# frantic-ferret Usage, Cost & Rank | OpenCode Data

> frantic-ferret ranked #39 by tokens across OpenCode last week, with 0% of tokens over the past two months.

- Page: https://opencode.ai/data/unknown/frantic-ferret
- JSON: https://opencode.ai/data/unknown/frantic-ferret.json
- Model ID: frantic-ferret
- Lab: Unknown
- Updated: 2026-11-08T21:26:11.000Z

## OpenCode usage: past 2 months

| Metric | Value |
| --- | --- |
| Rank by tokens last week | #39 |
| Tokens | 9.9B |
| Share of all tokens | 0% |
| Change vs previous 2 months | +100% |
| Unique users | 347 |
| Completed sessions | 2,636 |
| Average tokens per session | 3.8M |
| Average cost per session | $0.0000 |
| Total spend | $0 |
| Input tokens served from cache | 97.8% |
| Weekly retention | - |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-11-08 | 9.9B | 347 | 2,636 |
| 2026-11-09 | 0 | 0 | 0 |

## Top countries: past 2 months

| Rank | Country | Tokens | Share |
| --- | --- | --- | --- |
| 1 | United States | 1.4B | 14.5% |
| 2 | India | 1.2B | 11.8% |
| 3 | Brazil | 900M | 9.4% |
| 4 | Türkiye | 600M | 6% |
| 5 | United Kingdom | 600M | 5.9% |
| 6 | Egypt | 500M | 4.9% |
| 7 | Morocco | 500M | 4.7% |
| 8 | Algeria | 400M | 4.1% |
| 9 | Canada | 300M | 3.5% |
| 10 | Indonesia | 300M | 3.2% |
| 11 | Ukraine | 300M | 2.7% |
| 12 | Bangladesh | 200M | 2.2% |
| 13 | Israel | 200M | 1.8% |
| 14 | Argentina | 200M | 1.8% |
| 15 | Saudi Arabia | 200M | 1.8% |

## Nearby models by tokens last week

| Rank | Model | Lab | Tokens |
| --- | --- | --- | --- |
| 35 | [qwen3.8-max](https://opencode.ai/data/alibaba/qwen3-8-max.md) | Qwen | 25B |
| 36 | [hy3](https://opencode.ai/data/tencent/hy3.md) | Tencent | 15B |
| 37 | [grok-4.7](https://opencode.ai/data/xai/grok-4-7.md) | xAI | 13B |
| 38 | [hy4-preview](https://opencode.ai/data/tencent/hy4-preview.md) | Tencent | 12B |
| 39 | [frantic-ferret](https://opencode.ai/data/unknown/frantic-ferret.md) | - | 9.9B |
| 40 | [deepseek-v4p1-flash-1017](https://opencode.ai/data/deepseek/deepseek-v4p1-flash-1017.md) | DeepSeek | 7.3B |
| 41 | [step-5-preview](https://opencode.ai/data/stepfun/step-5-preview.md) | StepFun | 6.3B |
| 42 | [jev-1.13](https://opencode.ai/data/unknown/jev-1-13.md) | - | 5.6B |
| 43 | [gpt-5-nano](https://opencode.ai/data/openai/gpt-5-nano.md) | OpenAI | 283M |
| 44 | [dungcsnd113/deepseek-v4.1-flash-1017](https://opencode.ai/data/deepseek/dungcsnd113-deepseek-v4-1-flash-1017.md) | DeepSeek | 15M |

## Methodology

- Updates: Aggregated every hour. Days and weeks use UTC.
- Tokens: Input, output, reasoning, and cached tokens for each request.
- Users and sessions: Approximate counts of distinct users and OpenCode sessions.
- Cost: Session cost is the average cost per OpenCode session. Token prices are list prices from the OpenCode model catalog.
- Retention: The share of a model's users in one week who use it again the next week.
- Citation: Cite OpenCode Data (opencode.ai/data) with the update time shown at the top of the page.