Cross Model Review

Cross-Model Review

What This Does

Uses multiple AI models to review the same code, architecture, or decision from different perspectives. Each model brings different training data, reasoning patterns, and blind spots. Cross-model review catches issues that single-model review misses and provides higher confidence when models agree.

Instructions

1. Identify what needs review. Collect the artifact to review:

   • Code files, diffs, or PRs
   • Architecture decisions or design documents
   • Technical approaches or trade-off analyses
   • Specific questions where you want diverse opinions

2. Select review perspectives. Choose 2-3 models based on the task:

   • Claude (Opus/Sonnet): Deep reasoning, nuanced analysis, safety awareness
   • Gemini Pro: Large context window, broad knowledge, different training data
   • GPT-4/GPT-5: Different architectural patterns, alternative approaches
   • Open models (Llama, Codestral): Community-aligned perspectives, cost-free validation

3. Craft the review prompt. Send the same review request to each model with:

   • The artifact under review (code, doc, decision)
   • Specific review criteria (correctness, security, performance, maintainability)
   • Request for structured output (findings categorized by severity)
   • Ask each model to note areas of LOW confidence in their review

4. Dispatch reviews in parallel. Use subagents or tool calls to query multiple models simultaneously. Each review should be independent — models should not see each other's reviews.

5. Synthesize results. Compare the reviews:

   • Agreement zone: Issues flagged by 2+ models — highest confidence
   • Unique findings: Issues flagged by only one model — investigate further
   • Contradictions: Models disagree — present both perspectives with reasoning
   • Blind spots: Areas no model flagged — consider if coverage is sufficient

6. Deliver the cross-model report. Present the synthesized findings with attribution.

Output Format

# Cross-Model Review: {Subject}

## Models Used
- {Model 1}: {what it reviewed, its strengths for this task}
- {Model 2}: {what it reviewed, its strengths for this task}
- {Model 3}: {what it reviewed, its strengths for this task}

## Consensus Findings (2+ models agree)
| # | Finding | Severity | Models | Recommendation |
|---|---------|----------|--------|----------------|
| 1 | {issue} | {HIGH/MED/LOW} | {which models} | {fix} |

## Unique Findings (single model)
| # | Finding | Source Model | Confidence | Recommendation |
|---|---------|-------------|------------|----------------|
| 1 | {issue} | {model} | {H/M/L} | {fix or investigate} |

## Contradictions
| Topic | Model A Says | Model B Says | Resolution |
|-------|-------------|-------------|------------|
| {topic} | {position} | {position} | {which to follow and why} |

## Overall Assessment
- Consensus confidence: {HIGH/MEDIUM/LOW}
- Key risk areas: {list}
- Recommended actions: {prioritized list}

Tips

• Cross-model review is most valuable for security reviews, architecture decisions, and complex algorithms
• Models tend to have different strengths: use the right model for the right review aspect
• If all models agree, your confidence should be high — but remember they may share common blind spots
• For cost control, use free-tier models (Ollama, Groq, Cloudflare Workers) for initial review and paid models for targeted deep dives
• The contradiction section is often the most valuable — it reveals genuine trade-offs
• Don't use this for trivial code changes — the overhead is only worthwhile for consequential decisions