AI Model API Cost Calculator

Getting an accurate estimate only takes a few seconds.

1. Select your models: Pick the models you want to compare from the dropdown (up to 3)

2. Estimate your volume: Enter your expected API calls per day or month or just words amount.

3. Compare: See which model is cheaper for your use case. The comparison table will update automatically if you change the models selected.

AI API pricing boils down to a fundamental unit: the token. A token represents a chunk of data (usually about 4 characters of English text). You are billed for two things:

• Input Tokens (The Prompt): The text, context, or documents you send to the model. These are cheap to process.

• Output Tokens (The Generation): The text the model generates and sends back to you. Generating text requires massive computational power, making output tokens typically 3x to 8x more expensive than input tokens.

• Thinking Tokens (Reasoning): For advanced reasoning models (like the OpenAI o-series or DeepSeek-R1), you also pay for the hidden tokens the model generates internally while "thinking" before it gives you the final output.

Let's look at what actual production workloads cost as of YEAR:

• Customer Support Chatbot: Running 10,000 conversations a day (using an efficient model like GPT-5-mini or Gemini 2.5 Flash) with an average of 500 input tokens and 150 output tokens costs roughly $10 to $15 per month.
• Enterprise Document Summarization: Sending 50-page legal contracts (approx. 50,000 input tokens) and asking for a 500-token summary using a premium model like Claude 4.5 Opus or GPT-5.2 will cost about $0.15 to $0.20 per single request. If you do this 1,000 times a day, expect a $4,500+ monthly bill.
• Coding Assistants: Passing thousands of lines of code and asking for rewrites requires high intelligence. Using Claude 4.5 Sonnet or GPT-5.2 pro for heavy coding tasks can easily burn through $2 to $5 per developer, per day.

If your estimated bill is giving you a heart attack, you need to optimize your architecture.

• Model Routing: Don't use a Ferrari to go to the grocery store. Route simple tasks (classification, basic Q&A) to cheap models (GPT-5-nano, Gemini 2.5 Flash Lite) and only trigger premium models (Claude 4.5 Sonnet, GPT-5.2) when complex reasoning is actually required. This alone cuts costs by 60-70%. Tools like OpenRouter help do that automatically.

• Prompt Caching: If you are repeatedly sending the same massive system instructions or reference documents, use Prompt Caching. Most major providers now offer a 90% discount on cached input tokens.

• Batch APIs: If your task isn't urgent (like overnight data processing), use Batch APIs. You send the requests in bulk, wait up to 24 hours for completion, and get a 50% flat discount on the entire job.

Yes, analyzing images and audio costs money, but the pricing mechanism is different from text.

• Images: Images are converted into a fixed grid of tokens based on their resolution. For example, processing a standard 1024x1024 image costs roughly 1,000 to 1,300 tokens (which equates to about $0.001 to $0.003 per image, depending on the provider).

• Audio: Audio is heavily dense. Providers either charge a flat rate per second of audio (e.g., $0.01 per minute) or convert the audio waveforms directly into massive token counts. Audio outputs are significantly more expensive than text outputs.

The raw API call is just the tip of the iceberg. True production costs are heavily influenced by the infrastructure required to run the AI.

• Vector Databases (RAG): If you are building Retrieval-Augmented Generation, you have to store your data somewhere. Managed vector databases often charge by the gigabyte per day (e.g., $0.10/GB/day) plus search query fees.

• Embedding Models: Before you can search your documents, you have to embed them. Every document you ingest incurs an embedding API cost.

• The "Retry" Spiral: If an API call fails or times out, your system will likely retry it. A 2% failure rate with automated retries can quietly add thousands of wasted tokens to your daily bill.

• Latency: Slower models cost you user retention. Time-to-first-token is a hidden cost; if an application hangs for 10 seconds while generating an answer, the financial hit comes from churned users, not the API bill.