You wouldn't use a hammer for every job. So why use one AI model for everything?
GPT-5 is great at some things. Claude excels at others. Gemini has its own strengths. The best developers in 2025 aren't loyal to one model — they're fluent in all of them.
Welcome to the multi-model era.
The Model Landscape
OpenAI: GPT-5 Family
GPT-5 — The flagship. Best-in-class for:
- Complex reasoning chains
- Code generation breadth
- General knowledge tasks
GPT-5 Mini — 80% of the capability, 20% of the cost. Perfect for:
- Quick completions
- Simple refactoring
- Boilerplate generation
GPT-5 Nano — Lightning fast. Use for:
- Autocomplete
- Inline suggestions
- Real-time assistance
Anthropic: Claude 4.5 Family
Claude 4.5 Opus — The thoughtful one. Excels at:
- Long-context understanding (200K tokens)
- Nuanced code review
- Complex refactoring
- When accuracy > speed
Claude 4.5 Sonnet — The sweet spot. Great for:
- Daily coding tasks
- Balanced speed/quality
- Most development work
Claude 4.5 Haiku — Fast and cheap. Use for:
- Quick questions
- Simple completions
- High-volume tasks
Google: Gemini 3 Family
Gemini 3 Pro — The multimodal beast. Shines at:
- Image understanding
- Diagram interpretation
- Design-to-code tasks
Gemini 3 — Solid all-rounder. Good for:
- General development
- Google ecosystem integration
- Alternative perspective
Why Multi-Model Matters
Different Tasks, Different Strengths
Task: Write a quick utility function
Best: GPT-5 Nano or Claude Haiku
Why: Speed matters, complexity doesn't
Task: Refactor a 500-line module
Best: Claude 4.5 Opus
Why: Long context, careful analysis needed
Task: Convert a Figma design to code
Best: Gemini 3 Pro
Why: Multimodal understanding
Task: Debug a complex race condition
Best: Claude 4.5 Sonnet or GPT-5
Why: Reasoning depth required
Task: Generate 20 test cases
Best: GPT-5 Mini
Why: Volume task, cost matters
The Consensus Approach
What if models disagreed?
You: "Review this authentication implementation"
GPT-5: "Looks secure, maybe add rate limiting"
Claude: "SQL injection vulnerability on line 47"
Gemini: "Consider OAuth instead of custom auth"
Three perspectives. One gets critical bug. Consensus > single opinion.
Cost Optimization
Running GPT-5 for everything is expensive. Smart routing:
- Simple tasks → Nano/Haiku ($0.001 per request)
- Medium tasks → Sonnet/Mini ($0.01 per request)
- Complex tasks → Opus/GPT-5 ($0.10 per request)
10x cost difference. Same quality for appropriate tasks.
Model Selection Strategy
By Task Type
Code Generation
- GPT-5 — Breadth of knowledge
- Claude Sonnet — Clean, idiomatic code
- Gemini — Good for Google tech stack
Code Review
- Claude Opus — Catches subtle issues
- GPT-5 — Good pattern recognition
- Use both for critical code
Debugging
- Claude — Excellent at reasoning through issues
- GPT-5 — Broad knowledge of edge cases
- Gemini — Good for stack traces
Documentation
- Claude — Clear, well-structured writing
- GPT-5 — Comprehensive coverage
- Either works well
Refactoring
- Claude Opus — Long context, careful changes
- GPT-5 — Good at pattern application
- Validate with both
By Context Length
- < 4K tokens: Any model works
- 4K - 32K tokens: GPT-5 or Claude Sonnet
- 32K - 100K tokens: Claude preferred
- 100K+ tokens: Claude Opus required
By Speed Requirements
- Real-time (< 500ms): Nano/Haiku only
- Interactive (< 2s): Mini/Sonnet
- Batch (no limit): Opus/GPT-5
Practical Multi-Model Workflows
The Review Pipeline
1. Developer writes code
2. Claude Haiku: Quick lint check
3. GPT-5 Mini: Security scan
4. Claude Sonnet: Logic review
5. If critical: Claude Opus deep review
6. Aggregate findings, prioritize
The Debug Flow
1. Error occurs
2. Gemini: Analyze stack trace + screenshots
3. Claude: Reason through potential causes
4. GPT-5: Search knowledge for similar issues
5. Synthesize into actionable fix
The Generation Cascade
1. GPT-5: Generate initial implementation
2. Claude: Review and refine
3. GPT-5 Mini: Generate tests
4. Claude Haiku: Quick validation
5. Ship
Tools That Support Multi-Model
Single-Model Tools (Limited)
- ChatGPT — GPT only
- Claude.ai — Claude only
- Gemini — Gemini only
Multi-Model Platforms
- Poe — Multiple models, consumer-focused
- OpenRouter — API aggregator
- Orbit — Native multi-model UDE
The future is model-agnostic. Your tools should be too.
The Critique Mode Revolution
What happens when models review each other?
Traditional: Single Model Review
You: "Is this code secure?"
Model: "Yes, looks good"
You: Ships bug to production
Multi-Model Critique
Agent 1 (GPT-5): "Implementation looks solid"
Agent 2 (Claude): "Wait — race condition on line 34"
Agent 3 (Gemini): "Also, the error handling is incomplete"
Consensus: "Fix race condition and add error handling"
Three models. Three perspectives. Bugs caught before shipping.
This is Critique Mode — multiple AIs debating your code until consensus.
Getting Started
1. Know Your Models
Spend time with each:
- Use GPT-5 for a week
- Switch to Claude for a week
- Try Gemini for specific tasks
Understand their personalities.
2. Match Task to Model
Before prompting, ask:
- How complex is this?
- How much context is needed?
- How fast do I need it?
- How critical is accuracy?
Choose accordingly.
3. Use Multi-Model Tools
Stop copy-pasting between ChatGPT and Claude.
Use tools that let you switch models seamlessly or run them in parallel.
4. Embrace Critique Mode
For important code, get multiple opinions.
Disagreement between models often reveals the most important issues.
The Future is Plural
One model can't be best at everything. The math doesn't work.
Smart developers use GPT-5 for breadth, Claude for depth, Gemini for multimodal, and whatever comes next for whatever it does best.
Model loyalty is leaving performance on the table.
The future belongs to the model-fluid.
Pick the right tool for the job. Even when the tool is an AI.