NVIDIA Nemotron 3 Super is a 120B open-source AI model with just 12B active parameters. Priced at $0.10/MTok input and $0.50/MTok output, it is 30x cheaper than GPT-5.4. Best for coding, document analysis, and multi-agent tasks — but GPT-5.4 leads on raw reasoning (Intelligence Index: 57 vs 36).
Quick Answer
Key Takeaways
✅ 30x cheaper output than GPT-5.4 ($0.50 vs $15.00/MTok)
✅ 25x cheaper input than GPT-5.4 ($0.10 vs $2.50/MTok)
✅ 1 million token context window (both models support ~1M)
✅ 120B total / 12B active parameters (MoE architecture)
✅ Generates output at 343.1 tokens per second median across providers
✅ Fully open-source with free weights on Hugging Face
What You'll Learn
- How LatentMoE architecture works (4 experts for the price of 1)
- Complete benchmark comparison: Nemotron 3 Super vs GPT-5.4
- Real pricing breakdown with long-context surcharges
- How to download and run locally (8x H100 or consumer GPU)
- Best use cases: coding, multi-agent systems, document analysis
What Is Nemotron 3 Super? Architecture Explained
NVIDIA released Nemotron 3 Super on March 11, 2026 at GTC. It's a 120 billion parameter hybrid Mamba-Transformer model with only 12 billion active parameters per token, a 1 million token context window, and inference throughput 2.2x higher than GPT-OSS-120B. The model introduces LatentMoE, a new expert routing architecture that activates 4x more experts at the same computational cost by compressing tokens into a latent space before routing. It also features native NVFP4 pretraining (trained in 4-bit precision from the first gradient update) and Multi-Token Prediction for built-in speculative decoding.
This "triple hybrid" design combines three architectural innovations in one model:
Architecture Stack
| Layer Type | Function |
|---|---|
| Mamba-2 State Space (majority of layers) | Mamba-2 layers handle the majority of sequence processing. SSMs provide linear-time complexity with respect to sequence length, which is what makes the 1M-token context window practical. |
| Transformer Attention (strategic insertions) | Grouped Query Attention for long-range precision recall |
| LatentMoE (4 Experts) | Latent MoE enables calling 4 experts for the inference cost of only one, improving intelligence and generalization. |
| Multi-Token Prediction | Achieves an average acceptance length of 3.45 tokens per verification step (vs. 2.70 for DeepSeek-R1), enabling up to 3x wall-clock speedups. |
Key Specifications
| Spec | Value |
|---|---|
| Total Parameters | 120B |
| Active Parameters | 12B |
| Context Window | 1 million tokens |
| Output Speed (Median) | 343.1 tokens per second |
| Training Data | Pretrained on 25 trillion tokens using NVFP4 |
| Unique Training Tokens | 10 trillion unique curated tokens |
| RL Rollouts | Post-trained with multi-environment RL across 21 configurations using 1.2 million rollouts |
| Throughput vs Previous Gen | Over 5x throughput than the previous Nemotron Super |
How LatentMoE Works: 4 Experts for the Price of 1
Traditional Mixture-of-Experts (MoE) models route tokens to specialists at full dimension (4096), which is computationally expensive.
Nemotron 3 Super routes latent representations instead. By the time the router makes its decision, the input has already passed through initial layers and been transformed into a rich hidden state that captures semantic context. The experts specialize on meaning, not tokens.
Standard MoE Process
Token (4096 dim) → Router (4096 dim) → 1 Expert (4096 dim) → Output Result: 1 Expert Only
LatentMoE Process (Nemotron)
Token (4096 dim) → Project Down (1024) → 4 Experts (1024 dim) → Project Up (4096) → Output Result: 4 Experts, Same Cost!
Each cycle activates a different subset of 22 experts from 512 total. This compression factor of 4x (d/l = 4096/1024) reduces memory bandwidth and all-to-all communication costs dramatically. The result: 120B parameters of specialized knowledge with only 12B active per forward pass.
Pricing Comparison: 30x Cheaper Than GPT-5.4
Nemotron 3 Super's biggest advantage is cost. Here are the verified, fact-checked prices:
AI Model Pricing Comparison (Per Million Tokens)
| MODEL | INPUT | OUTPUT | OPEN? |
|---|---|---|---|
| Nemotron 3 Super (DeepInfra/OpenRouter) | $0.10 | $0.50 | ✅ Yes |
| Nemotron 3 Super (Median across providers) | $0.30 | $0.75 | ✅ Yes |
| GPT-5.4 Standard (≤272K context) | $2.50 | $15.00 | ❌ No |
| GPT-5.4 Standard (>272K context) | 2x input ($5.00) | 1.5x output ($22.50) | ❌ No |
| GPT-5.4 Pro | $30.00 | $180.00 | ❌ No |
For a broader comparison of all major AI models including pricing, see our Best AI Models 2026 Complete Guide.
⚠️ The Hidden "Long-Context Tax" on GPT-5.4
This is where Nemotron 3 Super's advantage becomes massive:
For models with a 1.05M context window (GPT-5.4 and GPT-5.4 pro), prompts with >272K input tokens are priced at 2x input and 1.5x output for the full session.
That means a 500K token document analysis with GPT-5.4 costs $5.00 input + $22.50 output per million tokens — making Nemotron 3 Super up to 45x cheaper for long-context tasks.
Nemotron 3 Super charges the same flat rate regardless of context length. No surcharges. No tiers.
Real-World Cost Savings (Per Day, Estimated)
| Workload | Nemotron 3 Super | GPT-5.4 | Savings |
|---|---|---|---|
| Daily Coding Tasks (~50K tokens) | ~$0.03 | ~$0.88 | 29x cheaper |
| Document Analysis (500K input) | ~$0.43 | ~$25.00+ | 58x cheaper |
| Multi-Agent Pipeline (high volume) | ~$1.50 | ~$45.00 | 30x cheaper |
Benchmark Scores: Nemotron 3 Super vs GPT-5.4
Where Nemotron 3 Super Leads
6 On PinchBench — a new benchmark for determining how well LLM models perform as the brain of an OpenClaw agent — Nemotron 3 Super scores 85.6% across the full test suite, making it the best open model in its class.
RULER Long Context (1M tokens): RULER benchmark scores show 96.30% accuracy at 256K, 95.67% at 512K, and 91.75% at 1M tokens.
SWE-Bench Multilingual: The SWE-Bench Multilingual result (45.78% vs. GPT-OSS's 30.80%) stands out.
22 GPT-OSS-120B drops from 52% to 22% between 256K and 1M tokens. Nemotron 3 Super loses under 5 points across a 4x context increase.
23 On LiveCodeBench — an independent benchmark that tests current, non-contaminated coding problems — Nemotron 3 Super scores 81.19%.
7 Achieves 2.2x higher throughput than GPT-OSS-120B while maintaining comparable accuracy.
Where GPT-5.4 Leads (Be Honest About This)
15 GPT-5.4 scores 57 on the Artificial Analysis Intelligence Index, while 1 Nemotron 3 Super scores 36 — a **37% gap** in overall reasoning.
Computer Use: GPT-5.4 is the first general-purpose model OpenAI has released with native, state-of-the-art computer-use capabilities.
OSWorld-Verified: On the OSWorld-Verified benchmark, GPT-5.4 scored 75% — edging out the average human performance of 72.4%.
SWE-Bench Verified: GPT-5.4 achieved approximately 80% on SWE-bench Verified, while Nemotron 3 Super sits at 60.47%.
Summary Comparison Table
| Metric | Nemotron 3 Super | GPT-5.4 | Winner |
|---|---|---|---|
| Intelligence Index | 36 | 57 | 🏆 GPT-5.4 |
| PinchBench (Agent) | 85.6% | N/A | 🏆 Nemotron |
| SWE-Bench Verified | 60.47% | ~80% | 🏆 GPT-5.4 |
| RULER @1M tokens | 91.75% | N/A | 🏆 Nemotron |
| LiveCodeBench | 81.19% | N/A | 🏆 Nemotron |
| Output Speed (median) | 343 t/s | 73 t/s | 🏆 Nemotron |
| Output Price | $0.50/MTok | $15.00/MTok | 🏆 Nemotron |
| Computer Use | ❌ No | ✅ Native | 🏆 GPT-5.4 |
| Open Weights | ✅ Yes | ❌ No | 🏆 Nemotron |
How to Use Nemotron 3 Super: API & Local Setup
Option 1: Cheapest API Access (DeepInfra — $0.10/MTok Input)
curl -X POST https://api.deepinfra.com/v1/openai/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $DEEPINFRA_TOKEN" \
-d '{
"model": "nvidia/Nemotron-3-Super-120B-A12B",
"messages": [
{"role": "user", "content": "Explain quantum computing"}
],
"max_tokens": 4096
}'Option 2: Free Tier (OpenRouter)
NVIDIA Nemotron 3 Super is available on OpenRouter at $0 per million input tokens — a rate-limited free tier perfect for testing. Sign up at openrouter.ai and use model ID nvidia/nemotron-3-super-120b-a12b:free.
Option 3: Run Locally via Ollama
ollama run nemotron-3-super
Option 4: Self-Host (8x H100 or Blackwell)
| Setup | Requirements |
|---|---|
| Minimum (quantized) | 64GB RAM/VRAM with GGUF quantization |
| Recommended | 8x H100-80GB (BF16) |
| Optimal | Single B200 or DGX Spark with NVFP4 |
Best Use Cases for Nemotron 3 Super
1. AI Coding Assistants
At 60.47% on SWE-Bench Verified, Nemotron 3 Super sits ~6 points behind Qwen3.5 but delivers 2.2x the throughput. For multi-agent systems running many agents concurrently, that throughput-per-accuracy trade-off matters.
2. Document Analysis
The model features a 1M token context window for long-term agent coherence, cross-document reasoning, and multi-step task planning. At flat-rate pricing, processing 500K tokens costs ~$0.43 vs. $25+ with GPT-5.4's long-context surcharge.
3. Multi-Agent Systems
This model tackles the "context explosion" with a native 1M-token context window that gives agents long-term memory for aligned, high-accuracy reasoning.
4. Cybersecurity & IT Automation
It delivers maximum compute efficiency and accuracy for complex multi-agent applications such as software development and cybersecurity triaging.
5. Multilingual Codebases
The SWE-Bench Multilingual result (45.78% vs. GPT-OSS's 30.80%) stands out — a genuine differentiator for non-English codebases.
Download Nemotron 3 Super Free
The model is fully open with open weights, datasets, and recipes so developers can easily customize, optimize, and deploy it on their own infrastructure.
Download Links
| Format | Link |
|---|---|
| BF16 (Full Precision) | nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16 on Hugging Face |
| FP8 (Quantized) | nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8 on Hugging Face |
| NVFP4 (Blackwell Optimized) | nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 on Hugging Face |
| NVIDIA API Catalog | build.nvidia.com — Free tier available |
| Ollama | ollama run nemotron-3-super |
Final Verdict: Should You Switch to Nemotron 3 Super?
✅ Switch to Nemotron 3 Super if:
- You run high-volume coding or agent tasks where cost matters more than peak accuracy
- You need 1M context for document analysis without paying GPT-5.4's 2x surcharge
- You build multi-agent systems that need fast, cheap tokens at scale
- You want to self-host for data privacy — fully open weights under NVIDIA's permissive license
- Multilingual code is a priority (45.78% vs 30.80% on SWE-Bench Multilingual)
❌ Stick with GPT-5.4 if:
- You need maximum reasoning intelligence (57 vs 36 on Intelligence Index)
- Your workflow requires native computer use (screen control, clicking, typing)
- You need configurable reasoning effort (5 levels from none to xhigh)
- You depend on OpenAI's ecosystem (DALL-E, Sora, ChatGPT integrations)
- SWE-Bench Verified accuracy is critical (~80% vs 60.47%)
The Bottom Line
For most developers and enterprises running high-volume, cost-sensitive workloads, Nemotron 3 Super represents a paradigm shift in AI economics. 8Priced at $0.1 per million input tokens and $0.5 per million output tokens, it is one of the most cost-efficient options in its class.
But don't oversell it — GPT-5.4 is genuinely smarter, with a 37% higher intelligence score and capabilities (computer use, reasoning effort control) that Nemotron doesn't offer. The right choice depends on your specific workload, budget, and deployment requirements.
Frequently Asked Questions
Q: What is Nemotron 3 Super and how is it different from GPT-5.4?
A: NVIDIA Nemotron 3 Super is a 120B-parameter open hybrid MoE model, activating just 12B parameters for maximum compute efficiency. Unlike GPT-5.4 (closed-source, $15/MTok output), Nemotron 3 Super is fully open at $0.50/MTok output — 30x cheaper. However, GPT-5.4 scores 57 on the Intelligence Index vs Nemotron's 36, making GPT-5.4 significantly more capable on complex reasoning.
Q: How does LatentMoE architecture work?
A: LatentMoE projects tokens into a compressed latent space for expert routing, enabling 4x more experts at the same inference cost. Tokens are compressed from 4096 to 1024 dimensions before routing, then projected back. Each cycle activates 22 experts from 512 total.
Q: Where can I get the cheapest Nemotron 3 Super API access?
A: Pricing starts at $0.100 per million input tokens and $0.500 per million output tokens at DeepInfra. OpenRouter offers a $0 free tier for testing.
Q: Can I run Nemotron 3 Super on my local machine?
A: Full precision (BF16) requires 8x H100-80GB GPUs. For running on a single B200 or DGX Spark, use the NVFP4 quantized version. Ollama also supports local deployment with quantized formats for consumer hardware.
Q: Is GPT-5.4's 1M context window the same as Nemotron's?
A: Both support ~1M tokens, but there's a critical pricing difference. For GPT-5.4, prompts with >272K input tokens are priced at 2x input and 1.5x output for the full session. Nemotron 3 Super charges the same flat rate at any context length — making it dramatically cheaper for long-context tasks.
Q: How does RULER benchmark performance compare at 1M tokens?
A: Nemotron 3 Super shows 96.30% accuracy at 256K, 95.67% at 512K, and 91.75% at 1M tokens — one of the best long-context retention scores in any model. GPT-OSS-120B drops from 52% to 22% between 256K and 1M tokens.
Q: What about Nemotron 3 Nano vs Super?
A: Nemotron 3 Nano was introduced in December as the smaller sibling (30B/3B active). Super (120B/12B) adds Multi-Token Prediction, 1M context (vs 128K), and higher reasoning accuracy. Nano is ideal for edge deployment; Super targets enterprise agentic workflows. Both share the same hybrid architecture and open-weights license.
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📅 Last Updated: April 5, 2026 | Current Affair Editorial Team