GPT-5 nano vs Nemotron Super 49B: API Cost Comparison
Compare the API pricing, context windows, features, and real-world cost projections for GPT-5 nano (OpenAI) and Nemotron Super 49B (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
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Showing costs for 2 models. Cheapest: GPT-5 nano at $2.50/month.
| Alert | |||||||
|---|---|---|---|---|---|---|---|
BestGPT-5 nano | OpenAI | $2.50 | $0.000250 | $0.50 | $2.00 | 16.7% | Alerts coming soon |
Nemotron Super 49B | Nvidia NIM | $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-5 nano | Nemotron Super 49B | Cheaper |
|---|---|---|---|
| Input (standard) | $0.05/M | $0.10/M | GPT-5 nano |
| Output | $0.40/M | $0.40/M | — |
| 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 Super 49BMonthly | GPT-5 nanoPer request | Nemotron Super 49BPer request |
|---|---|---|---|---|
| 1K requests/mo | $0.25 | $0.30 | $0.000250 | $0.000300 |
| 10K requests/mo | $2.50 | $3.00 | $0.000250 | $0.000300 |
| 100K requests/mo | $25.00 | $30.00 | $0.000250 | $0.000300 |
| 1M requests/mo | $250.00 | $300.00 | $0.000250 | $0.000300 |
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 Super 49B
by Nvidia NIM
- General chatbot
- Content Creation
- Summarization
Input / 1M tokens
$0.10/M
Output / 1M tokens
$0.40/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 Super 49B |
|---|---|---|
| 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 Super 49B notes
Nemotron Super 49B on NVIDIA NIM. Budget-tier foundation model with TensorRT-LLM optimization for high-throughput inference on NVIDIA infrastructure.
Frequently Asked Questions
Is GPT-5 nano cheaper than Nemotron Super 49B?
At standard usage (1,000 input tokens, 500 output tokens, 100,000 requests/month), GPT-5 nano costs $25.00/month versus $30.00/month for Nemotron Super 49B — a 17% 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 Super 49B?
GPT-5 nano has a larger context window at 400,000 tokens, compared to 128,000 tokens for Nemotron Super 49B. A larger context window is important for processing long documents, multi-turn conversations, or large codebases without truncation.
Do GPT-5 nano and Nemotron Super 49B support the Batch API?
Neither GPT-5 nano nor Nemotron Super 49B 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 Super 49B 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 Super 49B?
GPT-5 nano is best suited for Text classification, Data extraction, while Nemotron Super 49B is optimized for General chatbot, Content Creation, Summarization. Choose based on which use case matches your primary workload — and validate with the cost calculator above to confirm the total monthly spend fits your budget.
What is the cost per request for GPT-5 nano vs Nemotron Super 49B?
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 Super 49B costs $0.000300 per request. At 100,000 requests/month, that translates to $25.00 and $30.00 respectively. Use the interactive calculator to adjust these parameters for your actual workload.
GPT-5 nano vs Nemotron Super 49B: Summary
When comparing GPT-5 nano and Nemotron Super 49B for API cost, the right choice depends on your workload's token profile, required features, and tolerance for latency. GPT-5 nano offers lower total cost at standard usage volumes (1,000 input + 500 output tokens per request at 100,000 requests/month) at $25.00/month, compared to $30.00/month for Nemotron Super 49B.
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 Super 49B charges $0.10/M input and $0.40/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 Super 49B. 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 Super 49B. 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.
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Relevant Use Cases
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