Model Orchestration

Use multiple AI models strategically: optimize costs, reduce bias, and leverage model-specific strengths in your workflows

Cline CLI’s --config and --thinking flags enable sophisticated multi-model workflows. Instead of using a single model for all tasks, you can route different work to different models based on cost, capability, and specialization.

Why Orchestrate Multiple Models?

Cost Optimization

By routing work to the right model for the job, you can dramatically reduce API costs. Fast, inexpensive models like Haiku and Gemini Flash handle simple tasks such as summarization, while expensive models like Opus and O1 are reserved for complex reasoning and planning. This approach can reduce costs by 10-100x on routine operations.

Bias Reduction

Different models catch different issues, so cross-validating solutions with multiple AI perspectives helps reduce blind spots that come from relying on a single model. In code reviews especially, combining viewpoints surfaces problems that any one model might miss.

Specialization

Certain models excel in specific domains: Codex and DeepSeek are strong at code generation, while GPT-4 and Claude shine at documentation and prose. Security analysis in particular benefits from combining multiple model viewpoints, since each brings different training data and heuristics to the table.

Pattern 1: CI/CD Code Review

See our production GitHub Actions workflow that uses Cline CLI for automated PR reviews: cline-pr-review.yml

Key capabilities demonstrated:

• Automated inline suggestions: Creates GitHub suggestion blocks that authors can commit with one click
• SME identification: Analyzes git history to find subject matter experts for each file
• Related issue discovery: Searches for context from past issues and PRs
• Security-first permissions: Read-only codebase access, can only post reviews
• Deep code analysis: Understands intent, compares approaches, identifies edge cases

The workflow runs on every PR and provides maintainers with comprehensive context to make faster, more informed decisions.

Pattern 2: Task Phase Optimization

Use different models for different phases of work. Route simple tasks to cheap models, complex reasoning to premium models.

Example: Issue Analysis Pipeline

# Get latest issue content
ISSUE_CONTENT=$(gh issue view $(gh issue list -L 1 | awk '{print $1}'))

# Phase 1: Quick summary with cheap model
SUMMARY=$(echo "$ISSUE_CONTENT" | cline -y --config ~/.cline-haiku \
  "summarize this issue in 2-3 sentences")

# Phase 2: Detailed plan with expensive model + thinking
PLAN=$(echo "$SUMMARY" | cline -y --thinking --config ~/.cline-opus \
  "create detailed implementation plan with edge cases")

# Phase 3: Execute with mid-tier model
echo "$PLAN" | cline -y --config ~/.cline-sonnet \
  "implement the plan from above"

Each cline invocation needs to complete before passing output to the next phase. Use shell variables to store intermediate results rather than piping cline commands directly.

Cost impact:

• Haiku: $0.80 per million input tokens
• Opus: $15 per million input tokens
• Sonnet: $3 per million input tokens

This pattern uses Opus only when needed for complex reasoning, saving ~10x on API costs compared to using Opus for everything.

Setting Up Model Configs

Create separate configuration directories for each model:

# Create config directories
mkdir -p ~/.cline-haiku ~/.cline-sonnet ~/.cline-opus

# Configure each with different models
cline --config ~/.cline-haiku auth anthropic --modelid claude-haiku-4-20250514
cline --config ~/.cline-sonnet auth anthropic --modelid claude-sonnet-4-20250514  
cline --config ~/.cline-opus auth anthropic --modelid claude-opus-4-5-20251101

# Or use different providers entirely
cline --config ~/.cline-gemini auth gemini --modelid gemini-2.0-flash-exp
cline --config ~/.cline-codex auth openai-codex --modelid gpt-5-latest

Now you can switch models per-task with --config:

cline --config ~/.cline-haiku "quick task"
cline --config ~/.cline-opus "complex reasoning task"

Pattern 3: Multi-Model Review & Consensus

Get multiple AI perspectives on the same change, then synthesize their feedback.

Example: Diff Review Pipeline

# Get the latest commit
DIFF=$(git show)

# Review 1: Gemini's perspective
echo "$DIFF" | cline -y --config ~/.cline-gemini \
    "review this diff and write your analysis to gemini-review.md"

# Review 2: Codex's perspective
echo "$DIFF" | cline -y --config ~/.cline-codex \
    "review this diff and write your analysis to codex-review.md"

# Review 3: Opus's perspective
echo "$DIFF" | cline -y --config ~/.cline-opus \
    "review this diff and write your analysis to opus-review.md"

# Synthesize all reviews into a consensus
cat gemini-review.md codex-review.md opus-review.md | cline -y \
    "summarize these 3 reviews and identify: 1) issues all models agree on, 2) issues only one model caught, 3) your final recommendation"

Why this works:

• Redundancy: Issues caught by all 3 models are high-confidence
• Coverage: Each model has blind spots; together they cover more ground
• Prioritization: Consensus issues should be fixed first
• Learning: See which model types catch which issue types

Advanced: Parallel Reviews

Run reviews in parallel for faster feedback:

# Run all reviews simultaneously
git show | cline -y --config ~/.cline-gemini "review and save to gemini-review.md" &
git show | cline -y --config ~/.cline-codex "review and save to codex-review.md" &
git show | cline -y --config ~/.cline-opus "review and save to opus-review.md" &

# Wait for all to complete
wait

# Synthesize
cat *-review.md | cline -y "create consensus review"

Parallel execution requires managing multiple Cline instances. See Multi-instance workflows for details.

Extended Thinking for Complex Tasks

Use the --thinking flag when Cline needs to analyze multiple approaches:

# Without thinking: Fast but may miss nuances
cline -y "refactor this codebase"

# With thinking: Slower but more thorough
cline -y --thinking \
    "refactor this codebase - consider: performance, maintainability, backward compatibility"

The --thinking flag allocates 1024 tokens for internal reasoning before Cline responds. Best for:

• Architectural decisions
• Security analysis
• Complex refactoring
• Multi-step planning

Best Practices

1. Profile your workload: Track which tasks are simple vs. complex
2. Match models to tasks: Use fast models for summaries, powerful models for reasoning
3. Automate switching: Script model selection based on task type
4. Monitor costs: Different models have 10-100x price differences
5. Validate important decisions: Use multi-model consensus for critical changes

Production Examples

Cost-Optimized PR Review

# Haiku: Quick summary and issue identification
gh pr view $PR | cline -y --config ~/.cline-haiku \
    "list all issues to fix, output as JSON"

# Opus with thinking: Deep analysis only if issues found
if [ -s issues.json ]; then
    cline -y --thinking --config ~/.cline-opus \
        "analyze these issues and recommend fixes"
fi

Security-Focused Multi-Model Scan

# Different models have different security perspectives
git diff main | cline -y --config ~/.cline-gemini "security review" > gemini-sec.md &
git diff main | cline -y --config ~/.cline-opus "security review" > opus-sec.md &
git diff main | cline -y --config ~/.cline-codex "security review" > codex-sec.md &
wait

# High-priority: Issues all 3 models found
cat *-sec.md | cline -y "find security issues all 3 reviews mentioned"

Related Documentation

CLI Reference

Complete documentation for —config and —thinking flags

Headless Mode

Run Cline autonomously in scripts, CI/CD pipelines, and automated workflows.

Model Selection Guide

Compare models and choose the right one for your needs

CI/CD Integration

Automate GitHub workflows with Cline CLI