AI Token Cost Calculator

How Much Does Your AI Usage Actually Cost?

If you’re building with AI — whether vibe coding an app, running an AI agent, or integrating LLMs into a product — understanding token costs is the difference between a viable project and a budget blowout. Our AI token cost calculator lets you select from 40+ models across OpenAI, Anthropic, Google, xAI, and open-source providers, enter your expected usage, and see exact cost projections per call, per day, and per month. Token pricing changes frequently as providers compete. We verify and update pricing monthly against official API documentation. The calculator distinguishes between input tokens (your prompts), output tokens (the model’s responses), and cached input tokens (repeated context that gets discounted).

Quick comparison (April 2026): GPT-5.4 mini costs $0.15 per million input tokens. Claude Sonnet 4.6 costs $3 per million input. Claude Opus 4.6 costs $15 per million input. GPT-5.4 costs $2.50 per million input. Gemini 3 Flash costs $0.075 per million input. The price range spans over 200× between the cheapest and most expensive models.

Understanding Tokens

A token is a chunk of text that language models process — roughly 3–4 characters or about 0.75 words in English. The phrase “Hello, how are you today?” is approximately 7 tokens. A full page of text (~500 words) is roughly 670 tokens. A 2,000-word article is approximately 2,700 tokens.

Input tokens are what you send to the model — your prompt, system instructions, conversation history, and any documents or context. Input tokens are generally cheaper than output tokens because the model processes them in a single forward pass.

Output tokens are what the model generates in response. These cost more because each output token requires a separate compute step (autoregressive generation). For most models, output tokens cost 2–5× more than input tokens.

Cached input tokens are a newer pricing concept. When you send the same context repeatedly (like a system prompt or reference document), providers like Anthropic and OpenAI cache this content and charge a reduced rate — often 80–90% less than standard input pricing. Claude Opus 4.6 charges $15/million for input but only $1.50/million for cached input reads.

Context window is the maximum number of tokens a model can process in a single request (input + output combined). Larger context windows allow longer documents and conversations but cost more per request. Claude Opus 4.6 supports 200K tokens, GPT-5.4 supports 128K, and Gemini 3 Pro supports up to 2 million tokens.

2026 Model Pricing Comparison

Pricing varies enormously across model tiers. Here’s a snapshot of key models as of April 2026.

Budget tier (best for high-volume, simpler tasks): GPT-5.4 mini ($0.15/$0.60 per million input/output), GPT-5.4 nano ($0.10/$0.40), Claude Haiku 4.5 ($0.80/$4.00), and Gemini 3 Flash ($0.075/$0.30). These models handle classification, extraction, simple Q&A, and structured output efficiently at pennies per thousand requests.

Mid tier (balanced cost and capability): GPT-5.4 ($2.50/$10.00), Claude Sonnet 4.6 ($3.00/$15.00), and Gemini 3 Pro ($1.25/$5.00). These handle most production workloads including code generation, analysis, content creation, and complex reasoning.

Premium tier (maximum capability for hard problems): Claude Opus 4.6 ($15.00/$75.00), GPT-5.4 Pro ($varying), and specialized reasoning models. Use these for complex reasoning, nuanced analysis, research tasks, and situations where accuracy justifies the cost.

The cost-performance sweet spot in April 2026 is Gemini 3 Flash for high-volume simple tasks and Claude Sonnet 4.6 or GPT-5.4 for most production workloads. The choice between these depends on your specific quality requirements, latency needs, and volume.

Estimating Your Monthly AI Spend

To estimate costs, you need three numbers: average tokens per request (input + output), number of requests per day, and which model you’re using.

Solo developer / vibe coder: 50–200 requests/day, averaging 2,000 input + 1,000 output tokens per request. Using Claude Sonnet 4.6: roughly $0.30–$1.20/day or $9–$36/month. Using GPT-5.4 mini: $0.01–$0.05/day or $0.30–$1.50/month.

Small app in production: 1,000–5,000 API calls/day with 1,500 input + 500 output tokens average. Using GPT-5.4: $5–$25/day or $150–$750/month. Using Gemini 3 Flash: $0.15–$0.75/day or $4.50–$22.50/month.

Medium-scale product: 50,000+ calls/day. Costs scale linearly — model selection becomes the dominant financial decision. Switching from Claude Opus to Claude Sonnet for 80% of your traffic could reduce costs by 5× while maintaining quality for most use cases.

The biggest cost surprise for new builders is output tokens. A chatbot that generates 500-word responses uses approximately 670 output tokens per response. At Claude Sonnet’s $15/million output rate, 10,000 daily responses cost $100.50/day — $3,015/month just in output tokens.

Cost Optimization Strategies

Choose the right model for each task. Not every request needs your most powerful model. Route simple tasks (classification, extraction, formatting) to budget models (GPT-5.4 mini, Gemini Flash) and reserve premium models for complex reasoning. Many production systems use a “model router” that selects the appropriate tier based on task complexity.

Use prompt caching. If you’re sending the same system prompt, few-shot examples, or reference documents with every request, caching can cut input costs by 80–90%. Anthropic’s prompt caching for Claude and OpenAI’s caching for GPT models both work automatically when repeated content is detected.

Minimize unnecessary context. Sending your entire conversation history with every request inflates input tokens. Implement conversation summarization — periodically condense earlier messages into a short summary, reducing token count while preserving context.

Batch non-urgent requests. Anthropic and OpenAI offer batch processing APIs with 50% discounts for requests that can tolerate hours-long processing times. If you’re doing data processing, analysis, or content generation that isn’t time-sensitive, batching halves your cost.

Reduce output length. Set max_tokens appropriately and use system prompts that instruct the model to be concise. A response that’s 200 tokens instead of 500 cuts output costs by 60%.

Monitor and alert. Set up spending alerts and dashboards. A runaway loop or unexpected traffic spike can burn through budget in hours. All major providers offer usage dashboards and spending limits.

Tokens vs. Credits vs. Subscriptions

The AI pricing landscape includes multiple models.

Per-token API pricing (OpenAI, Anthropic, Google) charges based on exact usage — you pay only for what you consume. This scales linearly and is most cost-efficient for production applications.

Credit-based systems (Cursor, Bolt, Replit) bundle tokens into credits or requests. Cursor Pro includes a monthly allowance of “fast” requests (using premium models) with unlimited “slow” requests. Credits abstract the underlying token cost but may be more or less expensive depending on your usage pattern.

Flat-rate subscriptions (ChatGPT Plus at $20/month, Claude Pro at $20/month) provide access to premium models with usage caps. These are best for individual use — typically far cheaper than equivalent API usage for moderate consumption, but with rate limits that prevent production-scale use.

For builders, the key decision is whether you need API access (pay-per-token, no rate limits, programmatic integration) or consumer subscription access (fixed monthly cost, usage caps, web/app interface). Our calculator focuses on API pricing since that’s what matters for building products.