NVIDIA • llm
Grande modelo de linguagem (llm) desenvolvido pela NVIDIA — Intelligence Index 36/100 no Artificial Analysis; US$ 0.300/1M tokens de entrada; 164 tokens/s de velocidade.
Context Window
1.0M tokens
Input Price/1M
$0.30
Output Price/1M
$0.75
Parameters
—
Speed
190 tok/s
Latency (TTFT)
1.1s
NVIDIA Nemotron 3 Super 120B A12B (Reasoning) results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Terminal-Bench Hard | 29.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| SciCode | 36.0 | 100.0 | — |
| AA Coding Index | 31.2 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA-LCR | 60.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Intelligence Index | 36.0 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| GPQA Diamond | 80.0 | 100.0 | Artificial Analysis official API |
| IFBench | 71.0 | 100.0 | — |
| HLE | 19.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Tau²-Bench | 68.0 | 100.0 | — |
NVIDIA Nemotron 3 Super 120B A12B (Reasoning) is an AI model developed by NVIDIA, classified as a llm model. It focuses on text processing and natural language generation. As a proprietary model, it is available via NVIDIA's cloud API. With a context window of 1.0M tokens, it is suitable for processing long documents such as contracts, books, and complete codebases.
NVIDIA Nemotron 3 Super 120B A12B (Reasoning) is usage-based, priced at $0.3/1M input tokens and $0.75/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. At this aggressive price point, it is one of the most cost-effective options on the market, ideal for high-volume applications like chatbots, bulk document analysis, and automation.
NVIDIA Nemotron 3 Super 120B A12B (Reasoning) was evaluated on 9 different benchmarks, covering categories like Agentic, Coding, Long Context, overall, Reasoning, Tool Use. Results show solid performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
NVIDIA Nemotron 3 Super 120B A12B (Reasoning) is suitable for a wide range of AI applications: long document analysis (contracts, legal proceedings, codebases), automation with tool calling (API integration, databases, external systems), high-volume chatbots and automated support, text generation, summarization, translation, and general assistance.
In the 2026 AI model ecosystem, NVIDIA Nemotron 3 Super 120B A12B (Reasoning) competes directly with similarly capable models. NVIDIA competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
NVIDIA Nemotron 3 Super 120B A12B (Reasoning) is an AI model developed by NVIDIA. It is a llm model.
NVIDIA Nemotron 3 Super 120B A12B (Reasoning) costs $0.3/1M input tokens and $0.75/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.
In available benchmarks, NVIDIA Nemotron 3 Super 120B A12B (Reasoning) scored: Terminal-Bench Hard: 29/100, SciCode: 36/100, AA Coding Index: 31.2/100. See the full table above for a detailed comparison.
No, NVIDIA Nemotron 3 Super 120B A12B (Reasoning) is a proprietary model from NVIDIA. It is available via cloud API. For open source alternatives, check our open source model ranking.
NVIDIA Nemotron 3 Super 120B A12B (Reasoning) excels at general-purpose language tasks. With its large context window, it handles long documents, codebases, and extended conversations. It supports tool calling for API integrations and automation.
Last updated: June 01, 2026 • View methodology →