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Count tokens and estimate costs across many LLMs for planning and budgeting.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "io.github.waqarulwahab/llm-cost-estimator" MCP server from askskill: Run: claude mcp add 'io-github-waqarulwahab-llm-cost-estimator' -- npx -y llm-cost-estimator-mcp
Count the tokens for this prompt and expected output, then estimate the cost on GPT-4o, Claude, and Gemini, and compare them in a table. Prompt: {paste content here}Provides token counts, per-model cost estimates, and a clear comparison table.
I plan to process 5,000 similar texts, each with about 1,200 input tokens and 300 output tokens. Estimate the total cost across major models and recommend cost-effective options.
Shows total batch cost ranges, model differences, and budgeting recommendations.
Here is my application request template. First calculate token usage, then estimate the cost per request and for 100,000 requests across different models, and identify what drives the cost most. Template: {paste template here}Delivers per-request and large-scale cost analysis, highlighting the most optimizable expensive parts.
Estimate Claude token usage, model costs, and caching break-even offline.
Count tokens, estimate costs, optimize prompts, and compare LLM pricing.
Search live LLM pricing, compare models, and estimate usage costs.
Track token costs across major LLMs with optimization and savings insights.
Track AI model pricing, compare costs, and monitor token usage in real time.
Discover live models and pricing to route tasks to compatible low-cost LLMs.