GPT-4.1 nano vs Nemotron Nano 9B: API Cost Comparison

Compare the API pricing, context windows, features, and real-world cost projections for GPT-4.1 nano (OpenAI) and Nemotron Nano 9B (Nvidia NIM). Use the interactive calculator below to compute your exact monthly cost based on your token usage and request volume.

Prices verified Mar 11, 2026

Interactive Cost Calculator

Select Models (2/10)

OpenAIGPT-4.1 nanoNvidia NIMNemotron Nano 9B

Use Case Preset

Fills in typical token counts for a workload type

Avg Input Tokens / Request

Tokens in each prompt sent to the model

Avg Output Tokens / Request

Tokens generated in each response

Monthly Requests

Total API calls per month

Cheapest: Nemotron Nano 9B at $1.20/mo — save 60.0% vs GPT-4.1 nano

							Alert
BestNemotron Nano 9B	Nvidia NIM	$1.20	$0.000120	$0.40	$0.80	60.0%	Alerts coming soon
GPT-4.1 nano	OpenAI	$3.00	$0.000300	$1.00	$2.00	—	Alerts coming soon

Monthly Cost Comparison

Price Comparison at a Glance

All prices are in USD per 1 million tokens ($/M tokens). Lower is cheaper.

Pricing Type	GPT-4.1 nano	Nemotron Nano 9B	Cheaper
Input (standard)	$0.10/M	$0.04/M	Nemotron Nano 9B
Output	$0.40/M	$0.16/M	Nemotron Nano 9B
Cached input	$0.03/M	N/A	—
Batch input	$0.05/M	N/A	—
Batch output	$0.20/M	N/A	—

Prices last verified: 2026-03-08 – 2026-03-11

Cost Breakdown by Usage Volume

Estimated monthly costs at different request volumes, assuming 1,000 input tokens and 500 output tokens per request. Adjust in the calculator above for your specific use case.

Volume	GPT-4.1 nanoMonthly	Nemotron Nano 9BMonthly	GPT-4.1 nanoPer request	Nemotron Nano 9BPer request
1K requests/mo	$0.30	$0.12	$0.000300	$0.000120
10K requests/mo	$3.00	$1.20	$0.000300	$0.000120
100K requests/mo	$30.00	$12.00	$0.000300	$0.000120
1M requests/mo	$300.00	$120.00	$0.000300	$0.000120

Green values indicate the lower-cost option at each volume tier. Cost per request is calculated at 1,000 input + 500 output tokens using standard (non-batch, non-cached) pricing.

Price History

Input price per 1M tokens

When to Choose GPT-4.1 nano

by OpenAI

Text classification
Data extraction

Input / 1M tokens

$0.10/M

Output / 1M tokens

$0.40/M

Context window

1,047,576

Tier

budget

When to Choose Nemotron Nano 9B

by Nvidia NIM

Customer support
Text classification
Summarization

Input / 1M tokens

$0.04/M

Output / 1M tokens

$0.16/M

Context window

128,000

Tier

budget

Key Differences Beyond Price

Cost is only one factor in choosing an AI model. Context window size, rate limits, supported features, and latency all affect whether a model fits your use case.

Capability	GPT-4.1 nano	Nemotron Nano 9B
Context window	1,047,576 tokens	128,000 tokens
Max output tokens	32,768 tokens	8,192 tokens
Performance tier	Budget	Budget
Vision / image input	Yes	No
Function calling	Yes	Yes
JSON mode	Yes	Yes
Prompt caching	Yes	No
Batch API (50% discount)	Yes	No
Extended reasoning	No	No
Fine-tuning	No	No
Rate limit (req/min)	10,000	Not published

GPT-4.1 nano notes

Lowest cost OpenAI model. Best for high-volume structured extraction.

Nemotron Nano 9B notes

Nemotron Nano 9B on NVIDIA NIM. Ultra-low-cost compact model for high-volume tasks requiring fast, efficient inference at minimal per-token cost.

Frequently Asked Questions

Is GPT-4.1 nano cheaper than Nemotron Nano 9B?

At standard usage (1,000 input tokens, 500 output tokens, 100,000 requests/month), Nemotron Nano 9B costs $12.00/month versus $30.00/month for GPT-4.1 nano — a 60% saving. Your actual savings will vary based on your token profile; output-heavy workloads amplify differences in output pricing.

Which model has a larger context window, GPT-4.1 nano or Nemotron Nano 9B?

GPT-4.1 nano has a larger context window at 1,047,576 tokens, compared to 128,000 tokens for Nemotron Nano 9B. A larger context window is important for processing long documents, multi-turn conversations, or large codebases without truncation.

Do GPT-4.1 nano and Nemotron Nano 9B support the Batch API?

GPT-4.1 nano supports the Batch API (50% discount for async processing), while Nemotron Nano 9B does not. If your workload tolerates up to 24-hour latency, routing to GPT-4.1 nano with batch pricing could significantly cut costs versus Nemotron Nano 9B's standard rate.

Which model offers better prompt caching?

GPT-4.1 nano supports prompt caching at $0.03/M for cached input, while Nemotron Nano 9B does not offer prompt caching. For RAG applications or chatbots with large, repeated context, GPT-4.1 nano's caching capability can substantially reduce effective costs.

What are the best use cases for GPT-4.1 nano vs Nemotron Nano 9B?

Both models are well-suited for Text classification. GPT-4.1 nano is particularly strong for Data extraction. Nemotron Nano 9B is favored for Customer support, Summarization. At the same quality level, the lower-cost model is usually preferable; use this page's calculator to compare total monthly spend at your volume.

What is the cost per request for GPT-4.1 nano vs Nemotron Nano 9B?

At 1,000 input tokens and 500 output tokens per request — a typical conversational workload — GPT-4.1 nano costs $0.000300 per request and Nemotron Nano 9B costs $0.000120 per request. At 100,000 requests/month, that translates to $30.00 and $12.00 respectively. Use the interactive calculator to adjust these parameters for your actual workload.

GPT-4.1 nano vs Nemotron Nano 9B: Summary

When comparing GPT-4.1 nano and Nemotron Nano 9B for API cost, the right choice depends on your workload's token profile, required features, and tolerance for latency. Nemotron Nano 9B offers lower total cost at standard usage volumes (1,000 input + 500 output tokens per request at 100,000 requests/month) at $12.00/month, compared to $30.00/month for GPT-4.1 nano.

Both models are priced in USD per million tokens, the standard unit across all major AI API providers. GPT-4.1 nano charges $0.10/M for input tokens and $0.40/M for output tokens. Nemotron Nano 9B charges $0.04/M input and $0.16/M output. If your workload is output-heavy (more tokens generated than consumed as input), the model with the lower output price compounds cost savings significantly at scale.

Prompt caching is supported by GPT-4.1 nano but not Nemotron Nano 9B. For workloads with large, repeated system prompts or document context — such as RAG pipelines or multi-turn conversations with a fixed knowledge base — prompt caching can reduce effective input costs by 60–90%, which may change the cost ranking between these two models at your specific usage pattern.

Context window capacity differs between the two: GPT-4.1 nano supports up to 1,047,576 tokens in a single request, versus 128,000 tokens for Nemotron Nano 9B. A larger context window is essential for document summarization, large codebase analysis, and multi-document retrieval-augmented generation (RAG) applications.

Use the interactive calculator at the top of this page to enter your actual token usage and monthly request volume for a precise cost comparison tailored to your workload. Adjust for batch API discounts and prompt caching to find the most cost-effective option for your specific deployment.

Explore other model comparisons from the same providers or performance tiers.

GPT-4.1 nano vs GPT-5 mini GPT-4.1 nano vs GPT-5 nano GPT-4.1 nano vs GPT-4o mini GPT-4.1 mini vs GPT-4.1 nano GPT-5 mini vs Nemotron Nano 9B GPT-5 nano vs Nemotron Nano 9B GPT-4o mini vs Nemotron Nano 9B GPT-4.1 mini vs Nemotron Nano 9B

Related Provider Pages

View complete pricing tables and model lineups for the providers behind these models.

OpenAI API Pricing — All Models Nvidia NIM API Pricing — All Models

Relevant Use Cases

See cost recommendations for workloads where GPT-4.1 nano or Nemotron Nano 9B is recommended.

Customer Support Bot Data Extraction Text Classification