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orbench vs DidWork

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

orbench compared with DidWork
Detailorbench logo orbenchDidWork logo DidWork
What it doesTest LLMs on your own tasks before you switch models Independent verification for AI agent outcomes
StageNot given Startup
FoundedNot given Not given
Based inNot given Not given
Pricing modelPaid Freemium
PricingStandard: $99 one-time for 2 machines, including 1 year of updates. Pro coming soon. Hobbyist: free forever with 250 checks/month; Pro: $9/month with 2,500 checks/month, $0.01 per extra check
Key features
  • Benchmark any OpenRouter model on your own prompts, CSVs or Hugging Face datasets
  • Rubric-based scoring by AI judge, by hand, or both
  • Cost per answered task with retries and caching
  • Time to first token and output tokens per second per call
  • Shareable reports in HTML, CSV and JSON formats
  • Compare up to three past benchmarks side by side
  • Hard spending cap per run
  • Fully self-hosted on your machine
  • No SDK or code changes required
  • Independent outcome verification with VERIFIED, FAILED, or UNKNOWN status
  • First-party adapters for Stripe, GitHub, Linear, Sentry, Resend, and HTTP endpoints
  • MCP integration with Claude Code, Cursor, and any MCP host
  • GitHub Actions required check that fails on unverified claims
  • Audit trail storing claims, evidence, and verdicts separately
  • Capability inspection before agents call tools
  • Read-only credential access with AES-256-GCM encryption
  • Continuous monitoring with watches every 5-10 minutes on Pro plan
  • Evidence retention for 7 days on Hobbyist, 1 year on Pro
What makes it different
  • Tests your own tasks with real data instead of generic benchmarks
  • Comes before integration with no SDK or code changes needed
  • Reports actual cost per answered task including retries and caching
  • Entirely self-hosted with data never leaving your machine
  • One shareable report with every answer and judge reasoning
  • Refuses to guess: returns UNKNOWN rather than false positives when evidence is unreachable
  • Neutral gate between agent execution and success verification
  • Runs the same way in production as in development
  • Never needs write access, only reads authoritative state
  • Structured audit trail for debugging and agent evaluation
Community votes00
DR (Domain Rating by Ahrefs)Not checked yet no change since last check2 no change since last check
Trust Flow (Majestic)01
Citation Flow (Majestic)1623
Referring domains (Majestic)314

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