GPT-5 nano vs Nemotron Nano 9B: API Cost Comparison
Compare the API pricing, context windows, features, and real-world cost projections for GPT-5 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
Fills in typical token counts for a workload type
Tokens in each prompt sent to the model
Tokens generated in each response
Total API calls per month
Showing costs for 2 models. Cheapest: Nemotron Nano 9B at $1.20/month.
| Alert | |||||||
|---|---|---|---|---|---|---|---|
BestNemotron Nano 9B | Nvidia NIM | $1.20 | $0.000120 | $0.40 | $0.80 | 52.0% | Alerts coming soon |
GPT-5 nano | OpenAI | $2.50 | $0.000250 | $0.50 | $2.00 | — | Alerts coming soon |
Monthly Cost Comparison
Price Comparison at a Glance
All prices are in USD per 1 million tokens ($/M tokens). Lower is cheaper.
| Pricing Type | GPT-5 nano | Nemotron Nano 9B | Cheaper |
|---|---|---|---|
| Input (standard) | $0.05/M | $0.04/M | Nemotron Nano 9B |
| Output | $0.40/M | $0.16/M | Nemotron Nano 9B |
| Cached input | $0.01/M | N/A | — |
Prices last verified: 2026-03-06 – 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-5 nanoMonthly | Nemotron Nano 9BMonthly | GPT-5 nanoPer request | Nemotron Nano 9BPer request |
|---|---|---|---|---|
| 1K requests/mo | $0.25 | $0.12 | $0.000250 | $0.000120 |
| 10K requests/mo | $2.50 | $1.20 | $0.000250 | $0.000120 |
| 100K requests/mo | $25.00 | $12.00 | $0.000250 | $0.000120 |
| 1M requests/mo | $250.00 | $120.00 | $0.000250 | $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 tokensWhen to Choose GPT-5 nano
by OpenAI
- Text classification
- Data extraction
Input / 1M tokens
$0.05/M
Output / 1M tokens
$0.40/M
Context window
400,000
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-5 nano | Nemotron Nano 9B |
|---|---|---|
| Context window | 400,000 tokens | 128,000 tokens |
| Max output tokens | 128,000 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) | No | No |
| Extended reasoning | No | No |
| Fine-tuning | No | No |
| Rate limit (req/min) | 10,000 | Not published |
GPT-5 nano notes
Cheapest GPT-5 model. 1M context at budget pricing.
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-5 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 $25.00/month for GPT-5 nano — a 52% 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-5 nano or Nemotron Nano 9B?
GPT-5 nano has a larger context window at 400,000 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-5 nano and Nemotron Nano 9B support the Batch API?
Neither GPT-5 nano nor Nemotron Nano 9B currently supports a batch API with discounted pricing. For batch-eligible alternatives, consider models from OpenAI, Anthropic, or Google that include batch API support.
Which model offers better prompt caching?
GPT-5 nano supports prompt caching at $0.01/M for cached input, while Nemotron Nano 9B does not offer prompt caching. For RAG applications or chatbots with large, repeated context, GPT-5 nano's caching capability can substantially reduce effective costs.
What are the best use cases for GPT-5 nano vs Nemotron Nano 9B?
Both models are well-suited for Text classification. GPT-5 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-5 nano vs Nemotron Nano 9B?
At 1,000 input tokens and 500 output tokens per request — a typical conversational workload — GPT-5 nano costs $0.000250 per request and Nemotron Nano 9B costs $0.000120 per request. At 100,000 requests/month, that translates to $25.00 and $12.00 respectively. Use the interactive calculator to adjust these parameters for your actual workload.
GPT-5 nano vs Nemotron Nano 9B: Summary
When comparing GPT-5 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 $25.00/month for GPT-5 nano.
Both models are priced in USD per million tokens, the standard unit across all major AI API providers. GPT-5 nano charges $0.05/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-5 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-5 nano supports up to 400,000 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.
Related Comparisons
Explore other model comparisons from the same providers or performance tiers.
Related Provider Pages
View complete pricing tables and model lineups for the providers behind these models.
Relevant Use Cases
See cost recommendations for workloads where GPT-5 nano or Nemotron Nano 9B is recommended.