GPT-4.1 nano vs GPT-5 nano: API Cost Comparison
Compare the API pricing, context windows, features, and real-world cost projections for GPT-4.1 nano (OpenAI) and GPT-5 nano (OpenAI). Use the interactive calculator below to compute your exact monthly cost based on your token usage and request volume.
Prices verified Mar 8, 2026
Interactive Cost Calculator
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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 |
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 | GPT-5 nano | Cheaper |
|---|---|---|---|
| Input (standard) | $0.10/M | $0.05/M | GPT-5 nano |
| Output | $0.40/M | $0.40/M | — |
| Cached input | $0.03/M | $0.01/M | GPT-5 nano |
| Batch input | $0.05/M | N/A | — |
| Batch output | $0.20/M | N/A | — |
Prices last verified: 2026-03-06 – 2026-03-08
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 | GPT-5 nanoMonthly | GPT-4.1 nanoPer request | GPT-5 nanoPer request |
|---|---|---|---|---|
| 1K requests/mo | $0.30 | $0.25 | $0.000300 | $0.000250 |
| 10K requests/mo | $3.00 | $2.50 | $0.000300 | $0.000250 |
| 100K requests/mo | $30.00 | $25.00 | $0.000300 | $0.000250 |
| 1M requests/mo | $300.00 | $250.00 | $0.000300 | $0.000250 |
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-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 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
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 | GPT-5 nano |
|---|---|---|
| Context window | 1,047,576 tokens | 400,000 tokens |
| Max output tokens | 32,768 tokens | 128,000 tokens |
| Performance tier | Budget | Budget |
| Vision / image input | Yes | Yes |
| Function calling | Yes | Yes |
| JSON mode | Yes | Yes |
| Prompt caching | Yes | Yes |
| Batch API (50% discount) | Yes | No |
| Extended reasoning | No | No |
| Fine-tuning | No | No |
| Rate limit (req/min) | 10,000 | 10,000 |
GPT-4.1 nano notes
Lowest cost OpenAI model. Best for high-volume structured extraction.
GPT-5 nano notes
Cheapest GPT-5 model. 1M context at budget pricing.
Frequently Asked Questions
Is GPT-4.1 nano cheaper than GPT-5 nano?
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 GPT-4.1 nano — 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-4.1 nano or GPT-5 nano?
GPT-4.1 nano has a larger context window at 1,047,576 tokens, compared to 400,000 tokens for GPT-5 nano. A larger context window is important for processing long documents, multi-turn conversations, or large codebases without truncation.
Do GPT-4.1 nano and GPT-5 nano support the Batch API?
GPT-4.1 nano supports the Batch API (50% discount for async processing), while GPT-5 nano 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 GPT-5 nano's standard rate.
Which model offers better prompt caching?
Both GPT-4.1 nano and GPT-5 nano support prompt caching. GPT-4.1 nano caches repeated input at $0.03/M (vs $0.10/M standard), while GPT-5 nano caches at $0.01/M (vs $0.05/M standard). For workloads with large, repeated system prompts or document context, caching can reduce effective input costs by 60–90%.
What are the best use cases for GPT-4.1 nano vs GPT-5 nano?
Both models are well-suited for Text classification, Data extraction. GPT-4.1 nano is particularly strong for overlapping tasks. GPT-5 nano is favored for similar workflows. 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 GPT-5 nano?
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 GPT-5 nano costs $0.000250 per request. At 100,000 requests/month, that translates to $30.00 and $25.00 respectively. Use the interactive calculator to adjust these parameters for your actual workload.
GPT-4.1 nano vs GPT-5 nano: Summary
When comparing GPT-4.1 nano and GPT-5 nano 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 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. GPT-5 nano charges $0.05/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 both models. 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 400,000 tokens for GPT-5 nano. 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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