NVIDIA Nemotron 3 Super 120B A12B
NVIDIA Nemotron 3 Super 120B A12B is NVIDIA's 120B total, 12B active-parameter hybrid Mamba-Transformer MoE built for complex multi-agent applications, featuring latent MoE and multi-token prediction.
View API reference- Input and output price
- Input $0.15, Output $0.65, Per 1M tokens
- 24h uptime
- Loading AI Gateway uptime
import { streamText } from 'ai'
const result = streamText({ model: 'nvidia/nemotron-3-super-120b-a12b', prompt: 'Why is the sky blue?'})Copy link to headingPlayground
Try out NVIDIA Nemotron 3 Super 120B A12B by NVIDIA. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.
NVIDIA Nemotron 3 Super 120B A12B
NVIDIA Nemotron 3 Super 120B A12B
Copy link to headingProviders
Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.
Copy link to headingUptime24 hours
Direct request success rate on AI Gateway and per-provider. Visit the docs for more info.
Copy link to headingThroughput24 hours
P50 throughput on live AI Gateway traffic, in tokens per second (TPS). Visit the docs for more info.
Copy link to headingLatency24 hours
P50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds. View the docs for more info.
Getting started
Call NVIDIA Nemotron 3 Super 120B A12B through AI Gateway with the AI SDK generateText and streamText functions, or through the OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages APIs by changing the base URL. AI Gateway authenticates the request and routes it to an available provider.
Install the AI SDK (pnpm add ai dotenv), create an API key from the API Keys page, and set it as AI_GATEWAY_API_KEY in your environment. Full setup is covered in the text generation quickstart.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'nvidia/nemotron-3-super-120b-a12b', prompt: 'Why is the sky blue?', });
console.log(result.text);}
main().catch(console.error);Top-level parameters
The same NVIDIA Nemotron 3 Super 120B A12B request in each API format AI Gateway supports.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'nvidia/nemotron-3-super-120b-a12b', system: 'You are a concise technical assistant.', prompt: 'Summarize the tradeoffs between static generation and SSR.', maxOutputTokens: 1024, });
console.log(result.text);}
main().catch(console.error);Standard parameters like prompt, messages, temperature, and tools work as documented in the AI SDK docs. These are the parameters with model-specific behavior.
| Parameter | Type | Required | Description |
|---|---|---|---|
model | string | Yes | Model ID in the form creator/model, e.g. nvidia/nemotron-3-super-120b-a12b. AI Gateway routes the request to an available provider. |
maxOutputTokens | number | No | Hard cap on generated tokens. NVIDIA Nemotron 3 Super 120B A12B supports up to 32,000 output tokens. Reasoning tokens count toward this limit. |
reasoning | 'provider-default' | 'none' | 'minimal' | 'low' | 'medium' | 'high' | 'xhigh' | No | Provider-agnostic reasoning effort, available in AI SDK 7 or later. Maps to the provider’s native reasoning configuration; reasoning settings under providerOptions take precedence when both are set. See the Reasoning section below. |
providerOptions | Record<string, JSONValue> | No | AI Gateway routing options under gateway, plus any provider-native options under the provider’s own namespace — see the table below. |
Input limits
| Input | Formats | Sources | Max count | Max size | Limits |
|---|---|---|---|---|---|
| Text | — | — | — | — | Prompt and response share the 256K-token context window |
Provider options
Set AI Gateway routing options under providerOptions.gateway. For provider-specific options, pass them under the provider’s namespace as documented by the AI SDK.
Learn more in the AI SDK provider docs.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'nvidia/nemotron-3-super-120b-a12b', prompt: 'Why is the sky blue?', providerOptions: { gateway: { only: ['bedrock'], }, }, });
console.log(result.text);}
main().catch(console.error);These AI Gateway routing options apply to every model. Provider-specific options pass through under the provider’s own namespace (for example providerOptions.anthropic) exactly as documented by the AI SDK.
| Parameter | Type | Required | Description |
|---|---|---|---|
providerOptions.gateway.only | string[] | No | Restrict routing to these provider slugs. Requests fail over only within the listed providers. |
providerOptions.gateway.order | string[] | No | Preferred provider order. Listed providers are tried first; unlisted providers remain available as fallbacks. |
providerOptions.gateway.sort | 'cost' | 'ttft' | 'tps' | No | Rank candidate providers by price, time to first token, or tokens per second instead of the default routing order. |
providerOptions.gateway.zeroDataRetention | boolean | No | Route only to providers with a zero-data-retention policy for this model. |
Routing across providers
AI Gateway serves the same model through multiple providers and fails over automatically. order expresses a preference while keeping every provider eligible; only is a hard allowlist — if none of the listed providers are available the request fails instead of falling back.
Options under a provider's own namespace (for example providerOptions.anthropic) are forwarded to that provider with the request. Providers ignore option namespaces that don't apply to them, so it is safe to set provider options alongside gateway routing options.
Reasoning
AI Gateway bridges reasoning across every API format. The AI SDK exposes a provider-agnostic top-level reasoning level (none, minimal, low, medium, high, or xhigh); the Chat Completions and Responses formats take the same effort under reasoning.effort; and the Anthropic Messages format uses a native thinking token budget. Whichever you send, the gateway maps it to the target model’s native configuration, converting between effort levels and token budgets as needed. Reasoning-related settings under providerOptions take full precedence over the top-level reasoning value and are never merged. Reasoning tokens typically count toward your output-token usage, though how they’re reported and billed varies by provider.
Learn more in the AI Gateway reasoning guide.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'nvidia/nemotron-3-super-120b-a12b', prompt: 'Explain the Monty Hall problem step by step.', reasoning: 'high', });
console.log(result.text);}
main().catch(console.error);Tool calling
Expose tools the model can call. Define each tool’s inputs with a Zod schema.
import { generateText, tool } from 'ai';import { z } from 'zod';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'nvidia/nemotron-3-super-120b-a12b', prompt: 'What is the weather in San Francisco?', tools: { getWeather: tool({ description: 'Get the current weather for a location', inputSchema: z.object({ location: z.string() }), execute: async ({ location }) => ({ location, temperatureC: 18 }), }), }, });
console.log(result.text);}
main().catch(console.error);Copy link to headingMore models by NVIDIA
| Model |
|---|
| 262K | 0.2 s | 521 tps | $0.05/M | $0.15/M | Read$0.01/M | — |    | 08/11/2026 | ||||||
| 1M | 0.3 s | 186 tps | $0.50/M | $2.40/M | Read$0.12/M | — |  +1 | 06/04/2026 | ||||||
| 262K | 0.3 s | 128 tps | $0.05/M | $0.24/M | — | | 12/15/2025 | |||||||
| 131K | 0.1 s | 45 tps | $0.20/M | $0.60/M | — |  | 10/28/2025 | |||||||
| 131K | 0.2 s | 149 tps | $0.06/M | $0.23/M | — | | 08/18/2025 |
Copy link to headingAbout NVIDIA Nemotron 3 Super 120B A12B
NVIDIA released NVIDIA Nemotron 3 Super 120B A12B on March 11, 2026 as the second model in the Nemotron 3 family, following Nano. It has 120B total parameters and 12B active parameters per token. The hybrid Mamba-Transformer MoE backbone interleaves Mamba-2 layers for long-sequence processing, Transformer attention layers for precise recall, and MoE layers for compute efficiency. NVIDIA Nemotron 3 Super 120B A12B delivers higher throughput than the previous Nemotron Super generation.
Two architectural innovations distinguish Super from Nano. First, latent MoE: before routing, token embeddings compress into a low-rank latent space. This lets the model consult 4x as many expert specialists at the same inference cost. Finer-grained routing allows distinct experts to activate for different subtasks (Python syntax, SQL logic, multi-hop reasoning) without paying the compute cost of running them all. Second, multi-token prediction (MTP): the model predicts multiple future tokens in a single forward pass. MTP strengthens reasoning during training and provides built-in speculative decoding at inference, yielding up to 3x speedups on structured generation tasks like code and tool calls.
On PinchBench (a benchmark evaluating LLMs as the planning brain of an OpenClaw agent), NVIDIA Nemotron 3 Super 120B A12B scores 85.6%. Full announcement: https://docs.aws.amazon.com/en_us/bedrock/latest/userguide/model-card-nvidia-nemotron-super-3-120b.html.
Copy link to headingWhat To Consider When Choosing a Provider
- Configuration: NVIDIA Nemotron 3 Super 120B A12B's multi-agent orientation means it works best as the planning and reasoning backbone in a pipeline where lighter models handle individual steps. Evaluate your task decomposition before choosing a tier. Compare $0.15 and $0.65.
- Zero Data Retention: Zero Data Retention is available for this model. It is offered on a per-provider and model basis. See the documentation for details.
- Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
Copy link to headingWhen to Use NVIDIA Nemotron 3 Super 120B A12B
Best for
- Complex multi-agent applications: Software development pipelines or cybersecurity triaging that require deep planning across long contexts
- Context explosion workloads: Multi-agent systems with up to 15x the token volume of standard chats that cause goal drift with smaller models
- Dense technical problem-solving: Tasks where higher parameter count provides reasoning headroom
- Super plus nano pattern: Agentic pipelines pairing Super for complex decisions with Nano for efficient individual steps
- Fully open model requirement: Teams that need weights and recipes for enterprise customization, data control, or reproducibility
Consider alternatives when
- Simpler task steps: Nemotron 3 Nano is more throughput-efficient for lighter workloads
- Vision-language inputs: Super is text-only; Nemotron Nano 12B v2 VL supports multimodal inputs
- Cost-first constraints: A lighter model may deliver acceptable quality at lower cost per token
Copy link to headingConclusion
NVIDIA Nemotron 3 Super 120B A12B combines latent MoE for expert specialization and multi-token prediction for inference speedups. Route requests through AI Gateway as the planning and reasoning backbone for complex multi-agent applications at scale.
Copy link to headingFrequently Asked Questions
What is latent MoE and why does it matter?
Latent MoE compresses token embeddings into a smaller latent space before routing. This reduces per-expert compute cost and lets NVIDIA Nemotron 3 Super 120B A12B consult 4x as many experts for the same inference budget. Distinct experts activate for code generation, SQL logic, and natural language without the overhead of running them all densely.
What is multi-token prediction and how does it speed up inference?
MTP trains the model to predict multiple future tokens in a single forward pass. At inference, the MTP heads provide draft tokens that can be verified in parallel, acting as built-in speculative decoding. This delivers wall-clock speedups for structured generation like code and tool calls, without requiring a separate draft model.
What is the "Super + Nano" deployment pattern?
NVIDIA describes using Nano for straightforward individual steps in a pipeline and Super for complex decisions requiring deep reasoning. In software development, for example, Nano might handle routine merge requests while Super tackles tasks that require understanding a full codebase. This pattern distributes compute across task difficulty.
What is the context window of 256K tokens used for in multi-agent systems?
Multi-agent systems generate high token volume (up to 15x that of standard chats) from tool outputs, reasoning steps, and history resent at each turn. A window of 256K tokens lets agents keep full session history, large codebases, and retrieved context in a single pass. This reduces goal drift from context truncation.
Where are hosted per-token prices?
Rates are listed on this page. They reflect the providers routing through AI Gateway and shift when providers update their pricing.