AI model
nvidia/nemotron-3-super-120b-a12b-reasoning
NVIDIA: Nemotron 3 Super (Reasoning) is a 262,144-token model served on Composite at $0.0938 per million tokens in and $0.500 per million tokens out.
Try in Playground| Model ID | nvidia/nemotron-3-super-120b-a12b-reasoning |
|---|---|
| Provider | cv11 |
| Context window | 262,144 tokens |
| Accepts | text |
| Returns | text |
| Input price | $0.0938 per million tokens |
| Output price | $0.500 per million tokens |
| Prompt caching | Not offered for this model |
| Thinking | Optional - see "NVIDIA: Nemotron 3 Super (Reasoning) (Reasoning)" in the catalog to turn it on by default |
| Moderated | No |
| Cost on a monthly plan | About 1.0 plan credits per typical call (8k in, 1k out) |
| Routing endpoints | 1 |
Make a Composite API key and type nvidia/nemotron-3-super-120b-a12b-reasoning as the model name. JanitorAI takes the full URL https://composite.lucidity.sh/v1/chat/completions; SillyTavern, ChubAI, Agnai and RisuAI take https://composite.lucidity.sh/v1. It runs on credits, a plan, or your own provider key.
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This model has not carried enough traffic on Composite yet to publish a success rate, a latency figure or a traffic rank. The numbers appear here once it has.
$0.0938 per million tokens input · $0.500 per million tokens output
Credits pay the provider’s price plus 25%; cache hits cost less.
On a monthly plan, a typical call (8k tokens in, 1k out) uses about 1.0 plan credits, at the provider's own price.
cv11
Nemotron models are tuned by NVIDIA for instruction-following and tool use, and tend to behave predictably in structured prompts.
With a 262,144-token context window, it can hold a long-running roleplay or a full lorebook in memory without losing earlier plot details, so it suits multi-session campaigns and character cards with heavy backstory.
It is priced low enough for high-volume daily roleplay without the per-message cost adding up quickly.
NVIDIA Nemotron 3 Super is a 120B-parameter open hybrid MoE model, activating just 12B parameters for maximum compute efficiency and accuracy in complex multi-agent applications. Built on a hybrid Mamba-Transformer...
Supplied by cv11, not written by Composite.
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