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- Token Optimizer focuses on reducing the context the model consumes. It compresses tool outputs, avoids redundant file reads, preserves context across compaction, and identifies configuration waste. It offers a broader, immediately useful toolkit for day-to-day token management.

- Microsoft TokenEconomizer focuses on reducing unnecessary work. It learns reusable repository-specific lessons, removes consistently unused tools, and detects unproductive agent loops. Its main advantage is adapting to recurring patterns across sessions, although some learning features are disabled by default.

Performance and trade-offs: Microsoft reports 18–31% cost reductions in its research experiments, with generally similar task success rates. Token Optimizer demonstrates savings in specific compression benchmarks, but its broader savings estimates are less conclusive. These results are not directly comparable, and neither project has demonstrated superiority in a head-to-head test.

Recommendation

Choose Token Optimizer for comprehensive context reduction, continuity, and token-usage visibility.

Choose TokenEconomizer for learning from repeated workflows and preventing unnecessary agent activity.

Consider combining them for complementary savings, subject to compatibility and testing.