# nemotron-3.5-super-vl Usage, Cost & Rank | OpenCode Data

> nemotron-3.5-super-vl ranked #14 by tokens across OpenCode last week, with 0.0% of tokens over the past two months.

- Page: https://opencode.ai/data/nvidia/nemotron-3-5-super-vl
- JSON: https://opencode.ai/data/nvidia/nemotron-3-5-super-vl.json
- Model ID: nemotron-3.5-super-vl
- Lab: NVIDIA
- Updated: 2026-10-17T04:31:47.000Z

## OpenCode usage: past 2 months

| Metric | Value |
| --- | --- |
| Rank by tokens last week | #14 |
| Tokens | 442B |
| Share of all tokens | 0.0% |
| Change vs previous 2 months | +100% |
| Unique users | 9K |
| Completed sessions | 194,663 |
| Average tokens per session | 2.3M |
| Average cost per session | $0.0000 |
| Total spend | $0 |
| Input tokens served from cache | 86.5% |
| Weekly retention | - |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-10-15 | 87B | 1.9K | 41,867 |
| 2026-10-16 | 293B | 5.2K | 121,155 |
| 2026-10-17 | 62B | 2K | 31,641 |

## Top countries: past 2 months

| Rank | Country | Tokens | Share |
| --- | --- | --- | --- |
| 1 | United States | 52B | 11.4% |
| 2 | India | 44B | 9.6% |
| 3 | Brazil | 40B | 8.8% |
| 4 | China | 28B | 6.2% |
| 5 | Germany | 16B | 3.4% |
| 6 | Indonesia | 11B | 2.4% |
| 7 | Russia | 10B | 2.3% |
| 8 | Egypt | 9.8B | 2.1% |
| 9 | Mexico | 9.7B | 2.1% |
| 10 | Pakistan | 9.4B | 2.1% |
| 11 | Canada | 9B | 2% |
| 12 | Colombia | 8.7B | 1.9% |
| 13 | Türkiye | 8.7B | 1.9% |
| 14 | United Kingdom | 8.7B | 1.9% |
| 15 | France | 8.5B | 1.9% |

## Nearby models by tokens last week

| Rank | Model | Lab | Tokens |
| --- | --- | --- | --- |
| 10 | [beam](https://opencode.ai/data/reflection/beam.md) | Reflection | 1.4T |
| 11 | [nemotron-3.5-lightning](https://opencode.ai/data/nvidia/nemotron-3-5-lightning.md) | NVIDIA | 783B |
| 12 | [gpt-6-luna](https://opencode.ai/data/openai/gpt-6-luna.md) | OpenAI | 662B |
| 13 | [deepseek-v4-pro](https://opencode.ai/data/deepseek/deepseek-v4-pro.md) | DeepSeek | 462B |
| 14 | [nemotron-3.5-super-vl](https://opencode.ai/data/nvidia/nemotron-3-5-super-vl.md) | NVIDIA | 442B |
| 15 | [mimo-v2.6-pro](https://opencode.ai/data/xiaomi/mimo-v2-6-pro.md) | Xiaomi | 413B |
| 16 | [deepseek-v4-flash-vision-exp](https://opencode.ai/data/deepseek/deepseek-v4-flash-vision-exp.md) | DeepSeek | 394B |
| 17 | [mimo-v2.5](https://opencode.ai/data/xiaomi/mimo-v2-5.md) | Xiaomi | 323B |
| 18 | [muse-spark-1.2-contributor](https://opencode.ai/data/meta/muse-spark-1-2-contributor.md) | Meta | 308B |
| 19 | [claude-haiku-5-5](https://opencode.ai/data/anthropic/claude-haiku-5-5.md) | Anthropic | 255B |

## 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.