Llm API Cost Comparator Calculator
Compare API costs across GPT-4o, Claude, Gemini, Llama, and Mistral by token count and use case.
Formula
Cost = (input_tokens × input_rate + output_tokens × output_rate) / 1,000,000
Each API call cost is calculated by multiplying input tokens by the input rate per million tokens plus output tokens by the output rate per million tokens. Total costs scale with the number of daily requests.
Worked Examples
Example 1: Customer Support Chatbot
Problem: A company runs a chatbot handling 5,000 requests/day. Average: 800 input tokens, 400 output tokens. Compare GPT-4o mini vs Claude 3 Haiku.
Solution: GPT-4o mini: (800×$0.15 + 400×$0.60)/1M = $0.00036/req\nDaily: $0.00036 × 5000 = $1.80 | Monthly: $54\n\nClaude 3 Haiku: (800×$0.25 + 400×$1.25)/1M = $0.0007/req\nDaily: $0.0007 × 5000 = $3.50 | Monthly: $105
Result: GPT-4o mini: $54/mo | Claude 3 Haiku: $105/mo | GPT-4o mini saves 49%
Example 2: Legal Document Analysis
Problem: A law firm analyzes 50 contracts/day with 10,000 input tokens and 2,000 output tokens each. Compare GPT-4o vs Claude 3.5 Sonnet.
Solution: GPT-4o: (10000×$2.50 + 2000×$10.00)/1M = $0.045/req\nDaily: $0.045 × 50 = $2.25 | Monthly: $67.50\n\nClaude 3.5 Sonnet: (10000×$3.00 + 2000×$15.00)/1M = $0.06/req\nDaily: $0.06 × 50 = $3.00 | Monthly: $90.00
Result: GPT-4o: $67.50/mo | Claude 3.5 Sonnet: $90/mo | GPT-4o is 25% cheaper for this workload
Frequently Asked Questions
How are LLM API costs calculated?
LLM API costs are calculated based on the number of tokens processed, split into input tokens (your prompt) and output tokens (the model's response). Providers charge per million tokens, with separate rates for input and output. Input tokens are typically cheaper because the model only reads them, while output tokens cost more because they require generation. For example, if a model charges $3/M input and $15/M output, and you send 1,000 input tokens and receive 500 output tokens, the cost would be (1000 x $3 + 500 x $15) / 1,000,000 = $0.0105. Costs can add up quickly at scale, so comparing providers for your specific use case is essential.
Which LLM offers the best value for most use cases?
The best value depends heavily on your use case and quality requirements. For high-quality reasoning and complex tasks, GPT-4o and Claude 3.5 Sonnet offer strong performance at moderate cost. For simple tasks like classification, summarization, or basic Q&A, smaller models like GPT-4o mini, Gemini 1.5 Flash, or Claude 3 Haiku provide excellent quality at a fraction of the cost. Open-source models like Llama 3.1 can be self-hosted for zero per-token cost but require GPU infrastructure. A common strategy is to use cheaper models for the majority of requests and route only complex queries to premium models, achieving an optimal balance of cost and quality.
How can I reduce my LLM API costs?
Several strategies can significantly reduce LLM API costs. First, prompt engineering: shorter, more focused prompts reduce input tokens. Second, caching: store responses for identical or similar queries to avoid redundant API calls. Third, model routing: use cheaper models for simple tasks and premium models only for complex ones. Fourth, batching: some providers offer batch APIs at 50% discount for non-time-sensitive workloads. Fifth, fine-tuning: a fine-tuned smaller model can match larger model quality at lower cost. Sixth, setting max_tokens limits prevents runaway output costs. Seventh, using streaming and stopping generation when you have enough output. Finally, consider self-hosting open-source models if your volume justifies the infrastructure cost.
What is the difference between context window size and cost?
The context window is the maximum number of tokens (input plus output) that a model can process in a single request. Larger context windows like Gemini's 2M or Claude's 200K allow you to send more text at once but do not necessarily mean higher per-token costs. However, you pay for every token in the context, so sending a full 200K-token prompt is expensive regardless of the per-token rate. Some providers charge higher rates for prompts exceeding certain thresholds (e.g., Gemini charges more above 128K tokens). For cost optimization, only include relevant context rather than stuffing the entire window. Techniques like RAG (Retrieval-Augmented Generation) help by retrieving only the most relevant text chunks.
How do I estimate AI API costs?
API costs are based on token usage: Cost = (Input Tokens * Input Price + Output Tokens * Output Price) / 1,000,000. For example, at 3 dollars per million input tokens and 15 dollars per million output tokens, processing 1,000 requests averaging 500 input and 200 output tokens costs about 4.50 dollars. Batch processing and caching can reduce costs 30-50%.
How do I interpret the result?
Results are displayed with a label and unit to help you understand the output. Many calculators include a short explanation or classification below the result (for example, a BMI category or risk level). Refer to the worked examples section on this page for real-world context.