# Nemotron 3.5 Lightning 30B A3B Usage, Cost & Rank | OpenCode Data

> Nemotron 3.5 Lightning 30B A3B ranked #15 by tokens across OpenCode last week, with 0.5% of tokens over the past two months. Nemotron 3.5 Lightning 30B A3B costs $0.05 per 1M input tokens and $0.20 per 1M output tokens.

Fast NVIDIA Nemotron MoE for reliable agentic tasks across enterprise workloads

- Page: https://opencode.ai/data/nvidia/nemotron-3-5-lightning
- JSON: https://opencode.ai/data/nvidia/nemotron-3-5-lightning.json
- Model ID: nvidia/nemotron-3.5-lightning
- Lab: NVIDIA
- Updated: 2026-10-02T08:39:37.000Z

## Model facts

| Fact | Value |
| --- | --- |
| Context window | 262K |
| Max output | 262K |
| Knowledge cutoff | - |
| Release date | 2026-08-11 |
| Input modalities | text |
| Output modalities | text |
| Reasoning | Yes |
| Tool calling | Yes |
| Open weights | Yes |

## Pricing: USD per 1M tokens

| Input | Output | Cached input | Cache write |
| --- | --- | --- | --- |
| $0.05 | $0.20 | $0.01 | - |

## OpenCode usage: past 2 months

| Metric | Value |
| --- | --- |
| Rank by tokens last week | #15 |
| Tokens | 4.3T |
| Share of all tokens | 0.5% |
| Change vs previous 2 months | +100% |
| Unique users | 276K |
| Completed sessions | 5,269,441 |
| Average tokens per session | 813K |
| Average cost per session | $0.0000 |
| Total spend | $0 |
| Input tokens served from cache | 83.8% |
| Weekly retention | - |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-08-11 | 13B | 1.1K | 0 |
| 2026-08-12 | 92B | 6.4K | 0 |
| 2026-08-13 | 102B | 5.4K | 1 |
| 2026-08-14 | 97B | 4.7K | 1 |
| 2026-08-15 | 103B | 4.7K | 9 |
| 2026-08-16 | 87B | 3.8K | 1 |
| 2026-08-17 | 100B | 4.8K | 6 |
| 2026-08-18 | 109B | 4.8K | 45,797 |
| 2026-08-19 | 105B | 4.9K | 50,978 |
| 2026-08-20 | 101B | 5.2K | 56,482 |
| 2026-08-21 | 136B | 9.7K | 82,853 |
| 2026-08-22 | 121B | 6.7K | 69,430 |
| 2026-08-23 | 133B | 6.6K | 69,580 |
| 2026-08-24 | 148B | 8.2K | 90,374 |
| 2026-08-25 | 144B | 7.8K | 89,887 |
| 2026-08-26 | 109B | 9.1K | 87,660 |
| 2026-08-27 | 67B | 9.8K | 80,241 |
| 2026-08-28 | 0 | 7.8K | 67,675 |
| 2026-08-29 | 133B | 6.1K | 65,108 |
| 2026-08-30 | 155B | 7.9K | 77,505 |
| 2026-08-31 | 130B | 8.4K | 81,953 |
| 2026-09-01 | 119B | 7.7K | 76,649 |
| 2026-09-02 | 104B | 7K | 68,379 |
| 2026-09-03 | 118B | 7.6K | 161,999 |
| 2026-09-04 | 134B | 7.1K | 197,373 |
| 2026-09-05 | 136B | 6.3K | 132,608 |
| 2026-09-06 | 122B | 6.3K | 112,823 |
| 2026-09-07 | 96B | 6.1K | 120,705 |
| 2026-09-08 | 64B | 5.5K | 108,350 |
| 2026-09-09 | 82B | 5.5K | 167,837 |
| 2026-09-10 | 74B | 5.4K | 329,621 |
| 2026-09-11 | 44B | 4.3K | 126,917 |
| 2026-09-12 | 88B | 4.4K | 163,005 |
| 2026-09-13 | 40B | 4.2K | 155,744 |
| 2026-09-14 | 39B | 4.4K | 136,664 |
| 2026-09-15 | 88B | 5K | 135,726 |
| 2026-09-16 | 73B | 5.5K | 130,939 |
| 2026-09-17 | 79B | 3.9K | 313,437 |
| 2026-09-18 | 76B | 4.2K | 218,717 |
| 2026-09-19 | 39B | 4.3K | 184,112 |
| 2026-09-20 | 32B | 3.5K | 131,557 |
| 2026-09-21 | 34B | 3.1K | 83,955 |
| 2026-09-22 | 36B | 3.4K | 88,042 |
| 2026-09-23 | 36B | 3.1K | 81,092 |
| 2026-09-24 | 42B | 3K | 90,314 |
| 2026-09-25 | 32B | 2.8K | 106,670 |
| 2026-09-26 | 27B | 2.5K | 98,932 |
| 2026-09-27 | 40B | 2.5K | 100,927 |
| 2026-09-28 | 70B | 3.1K | 131,671 |
| 2026-09-29 | 42B | 3.2K | 95,119 |
| 2026-09-30 | 38B | 3K | 93,249 |
| 2026-10-01 | 34B | 2.8K | 75,489 |
| 2026-10-02 | 15B | 1K | 35,278 |

## Top countries: past 2 months

| Rank | Country | Tokens | Share |
| --- | --- | --- | --- |
| 1 | India | 651B | 15.2% |
| 2 | United States | 412B | 9.6% |
| 3 | China | 317B | 7.4% |
| 4 | Brazil | 282B | 6.6% |
| 5 | Indonesia | 170B | 4% |
| 6 | Germany | 128B | 3% |
| 7 | Pakistan | 107B | 2.5% |
| 8 | France | 85B | 2% |
| 9 | Spain | 83B | 1.9% |
| 10 | Türkiye | 77B | 1.8% |
| 11 | Egypt | 73B | 1.7% |
| 12 | United Kingdom | 73B | 1.7% |
| 13 | Colombia | 69B | 1.6% |
| 14 | Argentina | 69B | 1.6% |
| 15 | Canada | 63B | 1.5% |

## Nearby models by tokens last week

| Rank | Model | Lab | Tokens |
| --- | --- | --- | --- |
| 11 | [deepseek-v4-flash-vision-exp](https://opencode.ai/data/deepseek/deepseek-v4-flash-vision-exp.md) | DeepSeek | 687B |
| 12 | [gpt-6-luna](https://opencode.ai/data/openai/gpt-6-luna.md) | OpenAI | 640B |
| 13 | [deepseek-v4-pro](https://opencode.ai/data/deepseek/deepseek-v4-pro.md) | DeepSeek | 465B |
| 14 | [mimo-v2.6-pro](https://opencode.ai/data/xiaomi/mimo-v2-6-pro.md) | Xiaomi | 464B |
| 15 | [nemotron-3.5-lightning](https://opencode.ai/data/nvidia/nemotron-3-5-lightning.md) | NVIDIA | 267B |
| 16 | [ling-3.0-flash-fin](https://opencode.ai/data/inclusionai/ling-3-0-flash-fin.md) | inclusionAI | 256B |
| 17 | [qwen3.8-flash](https://opencode.ai/data/alibaba/qwen3-8-flash.md) | Qwen | 256B |
| 18 | [gpt-5.6-luna](https://opencode.ai/data/openai/gpt-5-6-luna.md) | OpenAI | 195B |
| 19 | [minimax-m3](https://opencode.ai/data/minimax/minimax-m3.md) | MiniMax | 181B |
| 20 | [mai-experimental-test](https://opencode.ai/data/unknown/mai-experimental-test.md) | - | 180B |

## Benchmarks

| Benchmark | Score | Metric | Source |
| --- | --- | --- | --- |
| MMLU-Pro | 81.94 | score | https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 |
| AA-Omniscience | 17.5 | score | https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 |
| SciCode | 32.6 | score | https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 |
| PinchBench | 85.37 | score | https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 |
| BrowseComp | 36.97 | score | https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 |
| AA-LCR | 52 | score | https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 |
| GPQA Diamond | 75.44 | accuracy | https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 |
| Humanity's Last Exam | 11.72 | accuracy | https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 |
| SWE-Bench Verified | 51.56 | accuracy | https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 |
| SWE-Bench Multilingual | 39.33 | accuracy | https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 |
| Terminal-Bench | 24.58 | accuracy | https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 |
| tau3-bench | 9.28 | score | https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 |
| GDPval-AA | 832 | Elo | https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 |
| IFBench | 71.88 | accuracy | https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 |

## 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.