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Discover live models and pricing to route tasks to compatible low-cost LLMs.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "token-scout" yet — see the docs or source repo.
Scan currently available cloud models and local Ollama instances. List models that support tool calling and have at least a 32k context window, sort them by price, and return a primary recommendation plus backups.
A ranked shortlist showing provider, capability compatibility, context length, live pricing, and recommendation priority.
Compare compatibility profiles for these models and identify which ones are unsuitable for a workflow requiring function calling and long context. Explain the risks, such as unstable tool calling or insufficient context.
A risk analysis identifying incompatible models and the likely reasons they could fail.
Recommend the best model mix for development, testing, and production environments: development should prioritize low cost and local availability; testing should prioritize stable compatibility; production should prioritize cost-performance and scalability. Provide suggested models and reasons for each environment.
Environment-specific model routing recommendations with suggested models, cost considerations, and compatibility notes for each scenario.
Search live LLM pricing, compare models, and estimate usage costs.
Get LLM provider recommendations, pricing, and service status insights.
Compare AI model pricing, simulate costs, and get plan recommendations.
Compare LLM benchmarks, pricing, and recommendations for better model selection.
Count tokens and estimate costs across many LLMs for planning and budgeting.
Track real-time LLM pricing across providers for cost comparison and planning.