How it compares
Token waste comes in three kinds. Most tools address one. This page defines each kind and gives a feature-by-feature comparison against the tools most often weighed against Token Optimizer.
Three kinds of token waste
Section titled “Three kinds of token waste”Structural waste is what gets billed before any conversation starts: an oversized CLAUDE.md, unused skills, duplicate system reminders, a stale MEMORY.md with entries past the attention horizon, dead MCP servers. It is often the largest share in high-waste setups, and it compounds, because a leaner prefix means a smaller cache-read bill on every turn that follows.
Runtime waste accumulates mid-session: verbose command output, oversized MCP results, and files re-read into context that were already there. Proxy compressors intercept these on the way in and shrink or evict them.
Behavioral waste is the pattern layer that no single session reveals: letting the cache expire, compacting too late, looping on a failing approach, running a top-tier model where a cheaper one would do, switching models mid-session and killing the cache. Token Coach analyzes 30 days of session history to surface these patterns and distinguishes a model-switch cache drop (expected) from a config-change cache drop (fixable).
For how Token Optimizer audits and scores each kind, see How it works.
Comparison table
Section titled “Comparison table”The table covers the five tools most often compared to Token Optimizer. Status: 🟢 supported, 🟡 partial, 🔴 not supported.
| Feature | Token Optimizer | Headroom | RTK | context-mode | /context |
|---|---|---|---|---|---|
| Tool output compression | 🟢 30+ CLI families, credential-safe, toggleable | 🟢 6 algorithms incl. model-based | 🟢 100+ command filters | 🟢 Sandbox + summary | 🔴 |
| First-read file skeletons | 🟢 History-validated, fail-open, full original retrievable | 🔴 | 🔴 | 🔴 | 🔴 |
| Tabular/JSON compression | 🟢 Value-preserving columnar | 🟢 SmartCrusher | 🔴 | 🟡 Generic summary | 🔴 |
| Read dedup and delta diffs | 🟢 Re-reads serve diff only | 🔴 | 🔴 | 🔴 | 🔴 |
| Compaction survival | 🟢 Progressive checkpoints, restore, tool-output digest | 🔴 | 🔴 | 🟡 Session guide only | 🔴 |
| Conversation history | 🟢 Progressive checkpoints + compaction restore | 🔴 | 🔴 | 🟡 Session guide | 🔴 |
| Model routing and behavioral coaching | 🟢 11 detectors, subagent cost breakdown, anti-patterns | 🔴 | 🔴 | 🔴 | 🟡 Basic suggestions |
| Historical trend analysis | 🟢 30-day trends, quality/cost/cache/duration correlation, model-switch detection | 🔴 | 🔴 | 🔴 | 🔴 |
| Loop and spin detection | 🟢 Catches behavioral loops before they burn | 🔴 | 🔴 | 🔴 | 🔴 |
| Context quality scoring | 🟢 7-signal quality score with grades | 🔴 | 🔴 | 🔴 | 🟡 Capacity % only |
| Structural waste audit | 🟢 Per-component (CLAUDE.md, skills, MCP, memory) | 🔴 | 🔴 | 🔴 | 🟡 Summary only |
| CLAUDE.md and MEMORY.md health | 🟢 8 auditors + attention-curve scoring | 🔴 | 🔴 | 🔴 | 🔴 |
| Measures if compression helped | 🟢 Local telemetry, before/after tokens, dollar savings | 🔴 | 🟡 rtk gain (token counts only) | 🔴 | 🔴 |
| Fleet-level cross-agent analysis | 🟢 | 🔴 | 🔴 | 🔴 | 🔴 |
| Cache-safe | 🟢 Never modifies existing context prefix | 🟡 Proxy mode rewrites in-flight | 🟢 Pre-shell only | 🟡 MCP overhead | 🟢 |
| Zero baseline context overhead | 🟢 External process, no context injection | 🔴 Injects instructions | 🟢 Shell-level only | 🔴 MCP server overhead | 🟢 Native |
| Zero runtime dependencies | 🟢 Pure stdlib (Python/TypeScript) | 🟡 Python + Rust + optional model | 🟢 Single Rust binary | 🟡 SQLite adapter required | 🟢 N/A |
| Zero telemetry | 🟢 | 🟢 | 🟡 Opt-in | 🟡 Varies | 🟢 |
| Multi-platform | 🟢 Claude Code, VS Code, Codex, OpenClaw, OpenCode, Hermes, Copilot | 🟢 Claude Code, Cursor, Codex, Aider, Copilot | 🟢 14 integrations | 🟢 15 integrations | 🔴 Claude Code only |
What /context shows versus what Token Optimizer does
Section titled “What /context shows versus what Token Optimizer does”/context reports that your context is 73% full. Token Optimizer reports which 12K tokens are spent on skills you never use, flags orphaned MEMORY.md topic files past the attention horizon, checkpoints decisions before compaction destroys them, and gives a quality score that tracks how much the session degrades as context fills. The built-in command shows the problem; Token Optimizer fixes it and measures the result.