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Clearcote vs Preference Model

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

Clearcote compared with Preference Model
DetailClearcote logo ClearcotePreference Model logo Preference Model
What it doesOpen-source anti-detect Chromium for Playwright Open-sourcing Karotte, a framework for building robust reinforcement learning environments
StageEstablished Not given
FoundedNot given Not given
Based inNot given Not given
Pricing modelFreemium Open source
PricingFree tier for one browser with GitHub account (auto-renewing 30-day key). Pro subscription at $49/month or $441/year for up to 250 concurrent browsers. Hosted browsers billed at €1 per GB of traffic with no plan fees, minimum prepaid €5. Not given
Key features
  • Engine-level fingerprint control for Canvas, WebGL, audio, fonts and hardware
  • Playwright and Puppeteer SDKs for Python, Node and .NET
  • Persistent identities with same seed producing same device every session
  • TLS and HTTP/2 fingerprinting matched to Chrome version
  • Proxy support with automatic timezone and language matching
  • Hosted cloud browsers billed per GB with residential IPs
  • Docker and CDP endpoint support
  • MCP server for AI agents including Claude, Cursor and Cline
  • WebRTC proxy IP matching
  • Sandboxed execution with non-privileged user isolation
  • Process termination before grading
  • Suspicious file detection and rejection
  • Out-of-memory and fork-bomb prevention
  • Hardened through one million evaluation runs
  • Red-team tested for reward hacking vulnerabilities
  • Scalable environment execution
  • Robust benchmark building support
What makes it different
  • Fingerprints set in Chromium engine rather than injected into pages
  • Fully open-source and reproducible from public source code
  • Same engine available self-hosted, containerized, or hosted in cloud
  • Drop-in replacement for Playwright and Puppeteer with minimal code changes
  • Cross-realm consistency with agreement between main thread, workers and iframes
  • Provides secure defaults and primitives specifically designed to prevent reward hacking in RL training
  • Hardened through real-world testing at scale with over one million evaluation runs
  • Designed for training environments where defenses must withstand continuous optimization attempts
  • Addresses AI alignment at the highest leverage point through better environment construction
  • Built and tested internally by frontier AI research team
Free planNot given Not given
Open sourceNot given Not given
PlatformsNot given Not given
Public APINot given Not given
Community votes00
DR (Domain Rating by Ahrefs)26 +1 (up) since last check29 +1 (up) since last check
Trust Flow (Majestic)1911
Citation Flow (Majestic)3124
Referring domains (Majestic)16398

4 details filled in for both companies.

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