ChatML VS ccusage

• Structured Format
  ChatML provides a structured format for writing prompts that align with the expectations of language models, potentially improving the predictability and quality of responses.
• Enhanced Control
  The format allows users to specify how they want their models to behave using tokens, providing enhanced control over the conversation.
• Scalability
  ChatML is designed to be scalable, accommodating diverse conversational needs ranging from simple queries to complex dialogues.
• Standardization
  Offers a standardized way to compose prompts and design interactions with AI models, which can streamline the development process.

Possible disadvantages

• Learning Curve
  Users might face a learning curve as they need to familiarize themselves with the ChatML format and the specific tokens used.
• Compatibility
  As a new format, there might be compatibility issues with existing systems or integrations that are not yet adapted to support ChatML.
• Dependence on Format
  Over-reliance on the ChatML format might limit creativity in prompting if not used judiciously, causing users to think within the constraints of the format.

• Easy to use CLI
  ccusage provides a simple command-line interface for analyzing Claude Code usage from local JSONL files, making it quick to get started without complex setup.
• Detailed cost breakdown
  It offers detailed reports on token usage and costs broken down by day, month, session, or billing blocks, helping users understand exactly where their usage and spending are going.
• No API key required
  Since it reads local usage data files directly rather than calling an API, it doesn't require additional API keys or credentials to generate reports.
• Open source and free
  ccusage is an open-source tool, allowing users to inspect, modify, and contribute to the codebase, and use it without licensing costs.
• Multiple output formats
  It supports various output formats including tables and JSON, making it flexible for both human review and integration into other scripts or dashboards.

Possible disadvantages

• Limited to Claude Code
  ccusage is specifically designed for analyzing Claude Code usage data, so it isn't useful for tracking usage of other AI tools or platforms.
• Depends on local data files
  The tool relies on local JSONL log files generated by Claude Code, so if those files are missing, corrupted, or in an unexpected format, the analysis may be incomplete or inaccurate.
• Requires Node.js environment
  Since it's a CLI tool typically run via Node.js or npm/npx, users need to have a compatible JavaScript runtime installed, which may be a barrier for non-developers.
• Manual updates needed
  As Claude Code's data format or features evolve, ccusage may require manual updates to stay compatible, and users need to keep the tool updated themselves.
• Limited visualization options
  The tool primarily focuses on text-based or tabular output, lacking built-in rich graphical visualizations that some users might want for deeper analysis.

No analysis of ccusage yet.

Tracking ChatML since Mar 2026.

• I built an AI usage tracker. Here are five alternatives, and why I took a different approach

  Ccusage reads local coding-agent logs and produces token and estimated-cost reports. Its documentation covers Claude Code, Codex and other supported CLIs, including JSON output, cache accounting and an offline mode using cached pricing.... - Source: dev.to / 2 days ago

• Mistral Large 4

  You can confirm the allowed usage amounts with something like [ccusage](https://ccusage.com/) or [tokscale](https://tokscale.ai/). - Source: Hacker News / 26 days ago

• tokenmaxxing: track AI coding usage across tools and devices

  Tokenmaxxing is our open-source CLI and public leaderboard for bringing those daily totals together. It builds on ccusage and syncs usage to one public profile. - Source: dev.to / about 1 month ago