Comparison
Valuein vs Calcbench
Better PIT accuracy, open APIs, and AI-native delivery
The key difference
Calcbench excels at XBRL extraction for human analysts. Valuein is built for programmatic access: a Bulk Data API, Python SDK, and an MCP Server that lets AI agents query 111M+ facts directly.
Outcomes that matter
What you actually get done — not just a feature checklist.
| The job | Valuein | Calcbench |
|---|---|---|
| Ship an IC-ready thesis | Minutes via MCP or the SDK | Manual extraction workflow |
| Backtest that survives out-of-sample | Verifiable PIT + all delisted names | Snapshot PIT, active-company focus |
| Let AI agents do the work | 57 MCP tools + 22 SOPs | No MCP support |
| Time to first query | Self-serve — minutes | Custom enterprise onboarding |
Feature Comparison
| Feature | Valuein | Calcbench |
|---|---|---|
| Pricing | $0–$49–$499/month | Custom enterprise pricing |
| Point-in-Time | accepted_at on every row — verifiable | Snapshot-based PIT |
| Survivorship Bias | All delisted companies, all history | Active companies primary focus |
| AI Agent Access | MCP Server with 57 structured tools | No MCP support |
| Open API | Public Bulk Data API + Python SDK on PyPI | API available; pricing not public |
| Parquet Format | Columnar Parquet — DuckDB/Spark native | JSON and CSV endpoints |
Where Calcbench is stronger
- Calcbench has stronger inline XBRL extraction for 10-Q footnotes
- Calcbench covers more derived metrics out-of-the-box
Best for
Quants and engineers who need clean time-series fundamentals in Parquet format and want AI-agent integration via MCP.
Start comparing today
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