Cross-Review FAQs

Question 1

Does Cross-Review help with tracking AI usage and costs?

Accepted Answer

Absolutely. It features a Live Dashboard and Cost Tracking, offering real-time monitoring of requests, per-model statistics, cache performance, and detailed cost summaries for all integrated AI providers.

Question 2

How does Cross-Review ensure reliable advice from multiple AIs?

Accepted Answer

It leverages a Semantic Consensus Algorithm to cluster semantically similar diagnoses, even if worded differently. This approach provides a more intelligent and trustworthy verdict by identifying agreement across multiple independent AI analyses.

Question 3

What developer-specific functionalities does Cross-Review provide?

Accepted Answer

Cross-Review includes specialized tools like `review_content` for multi-LLM code or text peer review, and `get_dev_guidance` to receive immediate, consensus-based advice for resolving development blockers and errors.

Question 4

Can I control which AI models are used for specific requests?

Accepted Answer

Yes, Cross-Review offers per-request model selection. You can choose 'fast' (cheapest 2 models), 'balanced' (3 models), 'thorough' (all models), or specify a custom set of models by ID to optimize for cost or thoroughness.

Question 5

What is Cross-Review?

Accepted Answer

Cross-Review is a powerful tool that queries 5+ leading AI models (like OpenAI, Gemini, DeepSeek, Mistral) simultaneously to gather diverse perspectives and synthesize them into a unified, reliable expert verdict, particularly useful for development tasks.

Cross-Review

Cross-Review

Key Features

Use Cases

Key Features

Use Cases