What does TokenWise do?

TokenWise is a measurement-driven model router for Claude Code that automatically routes subtasks to the cheapest capable model (Haiku for simple work, Sonnet for scoped reasoning, Opus for synthesis), logs every routed task with real token and cost data to a local NDJSON file, and includes an A/B test mode to verify routing decisions against your actual workload.

How can I test whether a task is suitable for a cheaper model before enabling auto-routing?

Use the `/tokenwise:ab " "` subcommand to run the same task across multiple model tiers and generate a markdown comparison that scores quality, allowing you to validate whether Haiku or Sonnet is sufficient before trusting auto-routing.

Does TokenWise send data to Anthropic or any external service?

No — TokenWise is zero-telemetry by design. All logs stay local in `.tokenwise/log.ndjson`, and no analytics endpoint exists in the source.

What are the built-in safety limits for model routing?

Haiku never spawns further subagents, max spawn depth is 2, subagents needing a smarter model return to the parent rather than escalating, tasks under 100 characters with no file context run inline, and subagent context over 30k tokens automatically bumps to a higher tier.

Is TokenWise compatible with other AI providers besides Anthropic?

No — TokenWise is Anthropic-only by design. For cross-vendor routing, use LiteLLM or OpenRouter instead.

Skill

Optimize Claude Code Model Routing and Costs

Name: TokenWise - Measurement-Driven Model Router
Availability: OnlineOnly
Author: CodeShuX

A Claude Code skill that routes subtasks to the cheapest capable model (Haiku, Sonnet, or Opus), logs token costs to NDJSON, and A/B tests routing decisions

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Spark score

out of 100

Updated last month

Source checked Aug 10, 2026

Version 15.12.0

Models

claude 3 5 haikuclaude 3 5 sonnetclaude 3 opus

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Why it matters

Dramatically reduce Claude Code token spend by intelligently routing tasks to the most cost-effective model (Haiku, Sonnet, or Opus) without sacrificing output quality. Validate routing decisions with built-in A/B testing and gain visibility into token usage.

Outcomes

What it gets done

Automatically route subtasks to the cheapest capable Claude model.

Log detailed token and cost data for each routed task.

Perform A/B tests to compare model quality on specific tasks.

Audit Opus token consumption and identify optimization opportunities.

Install

Add it to your toolbox

Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/ag-tokenwise | bash

After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.

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Overview

TokenWise - Measurement-Driven Model Router

TokenWise is a Claude Code skill that routes subtasks to the cheapest model that can handle them - Haiku for grunt work, Sonnet for scoped reasoning, Opus only for synthesis. It logs every routed task to local NDJSON with real token and cost numbers. An A/B test subcommand runs the same task across multiple tiers and scores quality, verifying routing decisions against your actual workload. Use TokenWise when running Claude Code workflows with mixed task complexity and you want to cut API costs without sacrificing quality. It's especially valuable when you need evidence that cheaper models meet your quality bar, using A/B tests on real workloads rather than estimates.

What it does

TokenWise routes subtasks to the cheapest model that can handle them: Haiku for routine grunt work, Sonnet for scoped reasoning tasks, and Opus reserved only for synthesis. Every routed task is logged to a local NDJSON file with real token counts and cost numbers. An A/B test subcommand runs the same task across multiple model tiers and scores quality, verifying routing decisions against your actual workload rather than relying on estimates.

When to use - and when NOT to

Use TokenWise when you're running repetitive Claude Code workflows with mixed task complexity and want to minimize API costs without sacrificing quality. It's ideal for projects where some subtasks are straightforward (data formatting, simple transformations) while others require deeper reasoning or synthesis. The A/B testing feature is particularly valuable when you need to validate that cheaper models truly meet your quality bar before committing to automated routing.

Do not use TokenWise if all your tasks genuinely require top-tier reasoning (Opus) or if the overhead of routing logic and logging outweighs the cost savings on small, infrequent jobs. It's also not appropriate when you need deterministic model selection for compliance or auditing purposes, since routing is driven by task characteristics rather than fixed rules.

Inputs and outputs

You provide subtasks within your Claude Code workflow. TokenWise routes each subtask to Haiku, Sonnet, or Opus. The skill logs every routed task to a local NDJSON file with real token counts and cost numbers. When using the A/B test subcommand, you provide a single task and the skill runs it across multiple tiers and scores quality.

Who it's for

TokenWise is for users of Claude Code who want to control API spend without manually triaging every subtask. The A/B testing capability lets you verify routing decisions with real performance data from your own workload rather than relying on vendor benchmarks or assumptions.

Source README

A Claude Code skill that auto-routes subtasks to the cheapest model that can handle them (Haiku for grunt work, Sonnet for scoped reasoning, Opus only for synthesis), then logs every routed task to a local NDJSON with real token + cost numbers. Includes an A/B test subcommand that runs the same task across multiple tiers and scores quality, so the routing decisions are verified against the user's real workload - not estimated.

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