Gemini 2.0 Flash vs GPT-4.1 nano: API Cost Comparison
Compare the API pricing, context windows, features, and real-world cost projections for Gemini 2.0 Flash (Google) and GPT-4.1 nano (OpenAI). Use the interactive calculator below to compute your exact monthly cost based on your token usage and request volume.
Prices verified Mar 24, 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: Gemini 2.0 Flash at $3.00/month.
| Alert | |||||||
|---|---|---|---|---|---|---|---|
BestGemini 2.0 Flash | $3.00 | $0.000300 | $1.00 | $2.00 | — | 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 | Gemini 2.0 Flash | GPT-4.1 nano | Cheaper |
|---|---|---|---|
| Input (standard) | $0.10/M | $0.10/M | — |
| Output | $0.40/M | $0.40/M | — |
| Cached input | $0.03/M | $0.03/M | — |
| Batch input | $0.05/M | $0.05/M | — |
| Batch output | $0.20/M | $0.20/M | — |
Prices last verified: 2026-03-08 – 2026-03-24
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 | Gemini 2.0 FlashMonthly | GPT-4.1 nanoMonthly | Gemini 2.0 FlashPer request | GPT-4.1 nanoPer request |
|---|---|---|---|---|
| 1K requests/mo | $0.30 | $0.30 | $0.000300 | $0.000300 |
| 10K requests/mo | $3.00 | $3.00 | $0.000300 | $0.000300 |
| 100K requests/mo | $30.00 | $30.00 | $0.000300 | $0.000300 |
| 1M requests/mo | $300.00 | $300.00 | $0.000300 | $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 Gemini 2.0 Flash
by Google
- General chatbot
- Customer support
- Text classification
Input / 1M tokens
$0.10/M
Output / 1M tokens
$0.40/M
Context window
1,048,576
Tier
mid
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
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 | Gemini 2.0 Flash | GPT-4.1 nano |
|---|---|---|
| Context window | 1,048,576 tokens | 1,047,576 tokens |
| Max output tokens | 8,192 tokens | 32,768 tokens |
| Performance tier | Mid | Budget |
| Vision / image input | Yes | Yes |
| Function calling | Yes | Yes |
| JSON mode | Yes | Yes |
| Prompt caching | Yes | Yes |
| Batch API (50% discount) | Yes | Yes |
| Extended reasoning | No | No |
| Fine-tuning | No | No |
| Rate limit (req/min) | 4,000 | 10,000 |
Gemini 2.0 Flash notes
Supports real-time streaming, image generation, and native audio I/O.
GPT-4.1 nano notes
Lowest cost OpenAI model. Best for high-volume structured extraction.
Frequently Asked Questions
Is Gemini 2.0 Flash cheaper than GPT-4.1 nano?
Gemini 2.0 Flash and GPT-4.1 nano cost the same at standard usage (1,000 input + 500 output tokens, 100K requests/month): $30.00/month each. For different token ratios, use the calculator above.
Which model has a larger context window, Gemini 2.0 Flash or GPT-4.1 nano?
Gemini 2.0 Flash has a larger context window at 1,048,576 tokens, compared to 1,047,576 tokens for GPT-4.1 nano. A larger context window is important for processing long documents, multi-turn conversations, or large codebases without truncation.
Do Gemini 2.0 Flash and GPT-4.1 nano support the Batch API?
Yes — both Gemini 2.0 Flash and GPT-4.1 nano support batch API processing, which offers a 50% discount on input and output costs in exchange for up to 24-hour turnaround. Ideal for offline workloads like bulk document processing or nightly classification pipelines.
Which model offers better prompt caching?
Both Gemini 2.0 Flash and GPT-4.1 nano support prompt caching. Gemini 2.0 Flash caches repeated input at $0.03/M (vs $0.10/M standard), while GPT-4.1 nano caches at $0.03/M (vs $0.10/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 Gemini 2.0 Flash vs GPT-4.1 nano?
Both models are well-suited for Text classification. Gemini 2.0 Flash is particularly strong for General chatbot, Customer support. GPT-4.1 nano is favored for Data extraction. 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 Gemini 2.0 Flash vs GPT-4.1 nano?
At 1,000 input tokens and 500 output tokens per request — a typical conversational workload — Gemini 2.0 Flash costs $0.000300 per request and GPT-4.1 nano costs $0.000300 per request. At 100,000 requests/month, that translates to $30.00 and $30.00 respectively. Use the interactive calculator to adjust these parameters for your actual workload.
Gemini 2.0 Flash vs GPT-4.1 nano: Summary
When comparing Gemini 2.0 Flash and GPT-4.1 nano for API cost, the right choice depends on your workload's token profile, required features, and tolerance for latency. Both models cost the same at standard usage volumes (1,000 input + 500 output tokens per request at 100,000 requests/month) at $30.00/month.
Both models are priced in USD per million tokens, the standard unit across all major AI API providers. Gemini 2.0 Flash charges $0.10/M for input tokens and $0.40/M for output tokens. GPT-4.1 nano 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 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: Gemini 2.0 Flash supports up to 1,048,576 tokens in a single request, versus 1,047,576 tokens for GPT-4.1 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.
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 Gemini 2.0 Flash or GPT-4.1 nano is recommended.