# iceman Usage, Cost & Rank | OpenCode Data

> iceman ranked #20 by tokens across OpenCode last week, with 0.0% of tokens over the past two months.

- Page: https://opencode.ai/data/unknown/iceman
- JSON: https://opencode.ai/data/unknown/iceman.json
- Model ID: iceman
- Lab: Unknown
- Updated: 2026-10-24T12:12:19.000Z

## OpenCode usage: past 2 months

| Metric | Value |
| --- | --- |
| Rank by tokens last week | #20 |
| Tokens | 215B |
| Share of all tokens | 0.0% |
| Change vs previous 2 months | +100% |
| Unique users | 7.8K |
| Completed sessions | 81,186 |
| Average tokens per session | 2.7M |
| Average cost per session | $0.0000 |
| Total spend | $0 |
| Input tokens served from cache | 91.8% |
| Weekly retention | - |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-10-23 | 17B | 1.3K | 11,771 |
| 2026-10-24 | 198B | 6.5K | 69,415 |

## Top countries: past 2 months

| Rank | Country | Tokens | Share |
| --- | --- | --- | --- |
| 1 | India | 29B | 13.5% |
| 2 | China | 15B | 7.1% |
| 3 | United States | 14B | 6.6% |
| 4 | Russia | 13B | 5.8% |
| 5 | Indonesia | 12B | 5.5% |
| 6 | Brazil | 8B | 3.7% |
| 7 | Germany | 7.1B | 3.3% |
| 8 | Egypt | 5.4B | 2.5% |
| 9 | Pakistan | 4B | 1.9% |
| 10 | Philippines | 4B | 1.8% |
| 11 | Spain | 3.9B | 1.8% |
| 12 | Mexico | 3.7B | 1.7% |
| 13 | France | 3.6B | 1.7% |
| 14 | Argentina | 3.4B | 1.6% |
| 15 | Nigeria | 3.3B | 1.5% |

## Nearby models by tokens last week

| Rank | Model | Lab | Tokens |
| --- | --- | --- | --- |
| 16 | [mimo-v2.5](https://opencode.ai/data/xiaomi/mimo-v2-5.md) | Xiaomi | 323B |
| 17 | [claude-haiku-5-5](https://opencode.ai/data/anthropic/claude-haiku-5-5.md) | Anthropic | 287B |
| 18 | [qwen3.8-flash](https://opencode.ai/data/alibaba/qwen3-8-flash.md) | Qwen | 277B |
| 19 | [minimax-m3](https://opencode.ai/data/minimax/minimax-m3.md) | MiniMax | 218B |
| 20 | [iceman](https://opencode.ai/data/unknown/iceman.md) | - | 215B |
| 21 | [glm-5.3](https://opencode.ai/data/zhipuai/glm-5-3.md) | Zhipu | 180B |
| 22 | [gemini-4-argon-contributor](https://opencode.ai/data/google/gemini-4-argon-contributor.md) | Google | 170B |
| 23 | [kimi-k2.7-code](https://opencode.ai/data/moonshotai/kimi-k2-7-code.md) | Moonshot | 146B |
| 24 | [deepseek-v4-flash-vision-exp](https://opencode.ai/data/deepseek/deepseek-v4-flash-vision-exp.md) | DeepSeek | 128B |
| 25 | [gpt-5.6-luna](https://opencode.ai/data/openai/gpt-5-6-luna.md) | OpenAI | 108B |

## 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.