Setting a custom price for a model in AgentsView
Summary
On June 9th, 2026, a method was detailed for setting custom model prices within AgentsView, Wes McKinney's Python toolkit for analyzing coding agent transcripts and token usage. This became necessary when the newly released Claude Fable 5 model was not yet included in AgentsView's default pricing database. The author reverse-engineered the tool to integrate custom pricing for Fable 5. An accompanying screenshot illustrates the cost attribution for Claude Fable 5 usage across various local projects, showing "prod_datasette_agent" as the dominant cost center at \$74.06, representing 89.3% of the total \$82.99 spent. The analysis also highlighted significant cache efficiency, reporting \$516.62 saved compared to uncached operations.
Key takeaway
For AI Engineers managing LLM agent deployments and seeking accurate cost attribution, you should anticipate the need to manually update pricing databases for newly released models like Claude Fable 5. This ensures AgentsView provides precise token usage and cost analysis across your projects. Proactively developing a process for integrating custom model pricing will maintain visibility into your operational expenses and highlight cache efficiency savings.
Key insights
AgentsView's model pricing database can be extended with custom rates for new or unsupported LLMs, enabling accurate cost tracking.
Principles
- Accurate LLM cost tracking is essential.
- Custom pricing extends analysis tools.
- Reverse engineering can adapt tools.
Method
Reverse-engineer AgentsView to identify and implement a custom pricing recipe for models not in its default database.
In practice
- Track token usage for new LLMs.
- Analyze cost attribution by project.
- Evaluate cache efficiency savings.
Topics
- AgentsView
- LLM Cost Management
- Token Usage Analysis
- Claude Fable 5
- Custom Model Pricing
- Coding Agents
Best for: MLOps Engineer, AI Engineer, Director of AI/ML
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Editorial summary, takeaway, and curation by AIssential. Original article published by Simon Willison's Weblog.