While everyone knows Llama 3 and Qwen, a quieter revolution has been happening in NVIDIA’s labs. They have been taking standard Llama models and “supercharging” them using advanced alignment techniques and pruning methods.

The result is Nemotron—a family of models that frequently tops the “Helpfulness” leaderboards (like Arena Hard), often beating GPT-4o while being significantly more efficient to run.

NVIDIA’s strategy is unique: they don’t just train models; they optimize them for hardware. This means you get models like the Nemotron-Super-49B, which delivers 70B-level intelligence at a fraction of the cost and memory footprint.

This guide breaks down the pricing for the Nemotron family on DeepInfra and helps you decide which one fits your budget.

1. How API Token Pricing Works

If you are new to LLM APIs, the pricing can look confusing. You aren’t paid by the request or by the minute; you are charged by the “Token”.

Here is the simple breakdown of how to calculate your costs:

• What is a Token?
  Think of a token as a piece of a word. A general rule of thumb is 1,000 tokens ≈ 750 words.
  • The word “apple” is 1 token.
  • The word “implementation” might be 2 or 3 tokens.
• Input vs. Output Costs
  You are billed separately for what you send the AI and what the AI writes back.
  • Input Tokens (Prompts): This is everything you send to the model: your instructions, the user’s question, and any documents or chat history you paste in. These are generally cheaper.
  • Output Tokens (Completions): This is the text the AI generates. These are usually more expensive (often 2x-4x the price of input) because generating new text requires significantly more computational power than reading existing text.
• The “Context” Factor
  In a conversation (like a chatbot), the AI doesn’t remember what you said 10 seconds ago. To keep the conversation going, you must re-send the entire chat history with every new message.
  • Message 1: You send 10 tokens.
  • Message 2: You send 10 tokens + previous answer + previous question.
  • Message 10: You are sending thousands of tokens of history just to ask a short question.
  • Tip: This is why a low Input Price is often more important than a low Output Price for chatbots.

2. DeepInfra Nemotron Pricing Table

DeepInfra offers the full range of NVIDIA’s Nemotron models. Because these models are optimized for NVIDIA hardware (which DeepInfra runs on), the pricing is often very aggressive, especially for the “Super” and “Nano” variants.

You can view the full list and test them here: DeepInfra Nemotron Models.

Model Name	Context Window	Input Price (per 1M)	Output Price (per 1M)
Llama-3.3-Nemotron-Super-49B-v1.5	128K	$0.10	$0.40
Llama-3.1-Nemotron-70B-Instruct	128K	$1.20	$1.20
NVIDIA-Nemotron-Nano-12B-v2-VL	128K	$0.20	$0.60
NVIDIA-Nemotron-Nano-9B-v2	128K	$0.04	$0.16

Note: Prices are per 1 million tokens. A 128K context window allows these models to process entire books or long codebases in a single prompt.

3. The “Super” Model: Why 49B is the New 70B

The most interesting model on this list is undoubtedly the Llama-3.3-Nemotron-Super-49B.

Typically, to get “70B level” performance, you have to pay for a 70B parameter model. NVIDIA used a technique called Neural Architecture Search (NAS) to take the Llama 3.3 70B model and intelligently prune (remove) the parts of the brain that weren’t contributing much to intelligence.

• The Result: A 49B parameter model that thinks like a 70B model.
• The Cost: Because it is smaller, it runs faster and cheaper. On DeepInfra, it costs just $0.10 per million input tokens. That is 12x cheaper than the standard Nemotron 70B Instruct.

If you are building a RAG application or a chatbot, the Super-49B is likely the “sweet spot” for 2025.

4. The Flagship: Nemotron-70B-Instruct

You might notice the Llama-3.1-Nemotron-70B-Instruct is significantly more expensive at $1.20/$1.20. Why?

This model wasn’t pruned for speed; it was optimized for quality. NVIDIA trained this using a special “HelpSteer2” dataset and advanced Reinforcement Learning from Human Feedback (RLHF).

While the base Llama 3.1 is smart, the Nemotron version is “better behaved.” It is less likely to refuse requests, gives more structured answers, and scores higher on “human preference” benchmarks. You pay a premium for this polish. It is best used for client-facing outputs where tone and strict instruction following are critical.

5. Real-World Cost Scenarios

Let’s see how much you would actually save by choosing the right Nemotron model.

Scenario A: The “Smart” RAG Search

• Task: Processing user queries against a large knowledge base (RAG).
• Volume: 10,000 queries/month.
• Average Context: 5,000 tokens (retrieved documents).
• Model: Nemotron-Super-49B.

Estimated Cost:

• Input: 10,000 * 5,000 = 50M tokens.
• Output: 10,000 * 500 = 5M tokens.
• Input Cost: 50M * $0.10 = $5.00
• Output Cost: 5M * $0.40 = $2.00
• Total Monthly Bill: $7.00

(If you used the standard Nemotron 70B for this, the bill would be roughly $66.00. The “Super” model saves you nearly 90%.)

Scenario B: Video Analysis (Vision)

• Task: Analyzing frames from security or operational videos to detect anomalies.
• Model: Nemotron-Nano-12B-VL.
• Volume: High throughput image processing.

Estimated Cost:

At $0.20 per million input tokens, this is one of the most affordable Vision-Language models on the market. Competitors like GPT-4o charge upwards of $2.50 for similar multimodal inputs.

Conclusion: Which Path to Choose?

The Nemotron family offers a unique value proposition: NVIDIA-grade optimization on top of Meta’s open weights.

• Go with Nemotron-Super-49B for 90% of your text-based use cases. It is the “value king” of 2025, offering near-flagship intelligence for pennies.
• Go with Nemotron-70B-Instruct only if you need the absolute highest “human preference” score and money is less of a concern than output quality.
• Go with Nemotron-Nano if you need to process video or images without breaking the bank.

By selecting the specific Nemotron variant optimized for your workload, you can achieve better-than-GPT-4o results while keeping your infrastructure costs incredibly low.