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Canary vs TokenPath

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

Canary compared with TokenPath
Detail CanaryTokenPath logo TokenPath
What it doesAI writes your code. Canary tests it. Citations, built for AI agents
StageMVP Not given
FoundedNot given Not given
Based inUS Not given
Pricing modelContact for pricing Freemium
PricingNot given Free tier for small requests with no API key required; 10 million free tokens to start; $1 per million tokens for pay-as-you-go, with discounts available at volume or with annual commitments; failed requests are never charged.
Key features
  • Automatic test generation from code diffs
  • Sandbox app testing with real browsers and APIs
  • Reproduced failures with root causes
  • Automatic regression test creation
  • GitHub PR integration with inline comments
  • Testing at every development step
  • Support for multiple coding agents
  • Slack and Linear integration
  • Comprehensive test reports with evidence
  • CLI for local testing before commits
  • Token-level attribution with confidence scores
  • Works with any AI model without access to weights or internals
  • Processes documents up to 1 million tokens
  • Returns results in under 2 seconds for 20,000-token documents
  • On-premise and air-gapped deployment options
  • PDF support with character-level mapping
  • Supports 23+ languages including Indian languages
  • Chrome browser extension for page and PDF tracing
  • Raw token-by-token heatmaps available via API
  • No data logging or training on request content
What makes it different
  • Catches bugs in AI-generated code automatically before they reach production
  • Tests code in real browsers and APIs, not just unit tests
  • Automatically converts findings into lasting regression tests
  • Works directly inside coding agents with one-line integration
  • Provides proof with replays and root causes for every failure
  • Measures attention directly from the model's computation rather than asking the model to cite itself, providing verifiable attribution
  • Achieves frontier-grade citation quality at 7x lower cost and 5-6x faster than alternatives
  • Never confused by duplicate facts—identifies the exact occurrence the answer used even when values appear multiple times
  • Lands on exact tokens down to character level, not just document chunks
  • Works on any model's output post-generation without modifying inference pipeline
Community votes00
DR (Domain Rating by Ahrefs)18 no change since last check2 no change since last check
Trust Flow (Majestic)53
Citation Flow (Majestic)3216
Referring domains (Majestic)15036

4 details filled in for both companies.

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