# Legora reviewed 41 documents in minutes with GPT-6 Astra

Source: [OpenAI](https://openai.com/index/legora-financial-statement-review-with-astra)  
Feed7 permalink: https://feed7.dev/p/legora-financial-statement-review-with-astra-1muuhfh  
Published: 2026-09-03T12:00:00.000Z  
Trust: Official Source (official_source)

## Why Included

Legora says GPT-6 Astra reviewed 41 documents within minutes, caught every planted error, and improved its workflow result by nearly 40%, though the underlying measure is unspecified.

## Source Summary

Legora applied **GPT-6 Astra** to a financial-review workflow covering **41 documents**. It finished in minutes, found **all four planted errors**, and improved performance by **nearly 40%**.

## Practical Implication

Builders designing document agents should evaluate the full workflow: retrieval across many files, error detection, completion time, and whether planted issues are consistently recovered.

## Agent-Ready Context

Legora applied **GPT-6 Astra** to a financial-review workflow covering **41 documents**. It finished in minutes, found **all four planted errors**, and improved performance by **nearly 40%**.

Builders designing document agents should evaluate the full workflow: retrieval across many files, error detection, completion time, and whether planted issues are consistently recovered.

The material does not define the performance metric, baseline, document complexity, or exact runtime. Planted-error recall also does not establish accuracy on unstructured real-world mistakes.

## Connected Context

Feed7 judgment across 691 accumulated Signals:

This provides a concrete multi-document test for Astra and suggests evaluating retrieval, detection, and runtime together rather than judging the model alone. Finding every planted error is encouraging, but undefined baselines and metrics—and the gap between planted and naturally occurring mistakes—leave real-world financial-review reliability unresolved.

- [Ling 3.0 Flash Fin now available on AI Gateway for free](https://feed7.dev/p/ling-3-0-flash-fin-now-available-on-ai-gateway-for-free-0oahhnw) — Ling offers a finance-specialized comparison route, while Legora supplies the kind of document-level workflow evaluation needed to compare it with a general model such as Astra.
- [You Only Pass Once: Answering and Abstaining Together in a Single Forward Pass of a Frozen Language Model](https://feed7.dev/p/2608-14465v1-0tpy8xd) — YOPO adds abstention as a reliability mechanism that planted-error recall alone does not test, highlighting a separate requirement for handling uncertain financial-review findings.

## Context Map

- Layer: model
- Domains: data, research
- Topics: reasoning, model-selection, agent-evals

## Uncertainty

- The material does not define the performance metric, baseline, document complexity, or exact runtime. Planted-error recall also does not establish accuracy on unstructured real-world mistakes.

## Agent Instruction

Use this item as source-backed context. Do not invent claims beyond the linked source. If this item conflicts with another source, call out the conflict.