Choosing the right Claude model
Three models. One family. From lightning-fast Haiku to deep-thinking Opus — an interactive guide to understanding what each model does best, what it costs, and when to use it.
Meet the Family
Three models, each optimized for a different balance of speed, intelligence, and cost.
Think of it like choosing a vehicle.
Sometimes you need a quick scooter for short trips. Sometimes a reliable sedan for daily driving. And sometimes — a heavy-duty truck for the big jobs. Claude's three models work the same way: each is built for a different balance of intelligence, speed, and cost.
The scooter: Haiku 4.5
Haiku is the speed demon. At $0.80 per million input tokens, it's 19x cheaper than Opus. It won't write your PhD thesis, but it will classify 50,000 support tickets before lunch.
Best at: Classification, routing, real-time responses, high-volume processing.
The sedan: Sonnet 4.5
Sonnet is the workhorse. It's the model most teams default to — smart enough for coding assistants, content generation, and data analysis, but not so expensive that your CFO notices.
Best at: Code generation, content writing, data analysis, general-purpose production workloads.
The truck: Opus 4.6
Opus is for when the stakes are high and the problem is hard. Legal analysis, research synthesis, strategic planning — tasks where being wrong costs more than the model. At $15/million input tokens, you pay for the best reasoning available.
Best at: Complex reasoning, research, high-stakes decisions, novel problem solving.
The Intelligence Spectrum
How each model performs across industry-standard evaluations.
Numbers tell the story.
Imagine three students taking the same exam. One finishes first but gets a B+. Another takes a bit longer for an A. The third aces everything but needs the most time. That's essentially what's happening with benchmarks— standardized tests for AI models.
Can it actually code?
SWE-bench Verified tests whether a model can fix real bugs in production code. Opus solves 72.5% of issues — nearly 3x more than Haiku. This is why Opus powers tools like Claude Code for complex multi-file refactors.
Can it think abstractly?
ARC-AGI-2 is the hardest benchmark here — it tests whether a model can solve problems it has literally never seen before. Scores are low across the board, but Opus leads at 21.2%. This is the frontier of AI reasoning.
Can it do a real job?
OSWorld and Finance Agent test whether a model can complete real tasks — using a computer GUI, managing spreadsheets, executing financial analysis workflows. These are the benchmarks that matter most for production: not "can it answer a trivia question?" but "can it do useful work?"
What It Costs
Pay per token, scale from zero to millions of requests.
You pay per word, not per month.
Think of it like electricity: you pay for what you use. Claude charges per token — a chunk of text roughly equal to ¾ of a word. Input tokens (what you send) and output tokens (what you get back) are priced separately.
The price spread is dramatic: Opus output costs 18.75x more than Haiku output.
The hidden cost multiplier.
Here's what catches people off guard: output tokens cost 5x more than input tokens across all models. A chatbot that generates long, verbose responses will cost significantly more than one with concise, focused answers — even on the same model.
For Sonnet: 1M input tokens = $3. But 1M output tokens = $15. That's the same content costing 5x more just because the model generated it instead of reading it.
Four ways to slash your bill.
Smart teams don't just pick a model — they optimize how they use it. Prompt caching alone can cut costs by up to 90% for repeated system prompts. The Batch API gives an instant 50% discount for non-urgent work. And model routing — sending simple tasks to Haiku and complex ones to Opus — can reduce costs by 60-80%.
Combined, these strategies can reduce a $10,000/month bill to under $2,000.
Cost Calculator
Think of this like an electricity bill estimator. Plug in your usage, see what each model would cost.
Try clicking a preset scenario below, or adjust the sliders manually.
Where Each Model Shines
Like picking the right tool from a toolbox — each model excels at different jobs.
Filter by model to see what each one does best.
Customer Support
Automated ticket routing and response generation
Content Moderation
Real-time content classification and filtering
Data Extraction
Structured data extraction from unstructured text
Coding Assistant
Code completion, debugging, and refactoring
Content Writing
Blog posts, marketing copy, social media content
Data Analysis
Analyzing datasets, generating insights and reports
API Integration
Building and testing API connections
Translation
Multi-language translation with context preservation
Research Synthesis
Multi-source research analysis and literature review
Strategic Planning
Complex business analysis and strategic recommendations
Legal Analysis
Contract review, compliance checking, legal research
Scientific Writing
Academic papers, grant proposals, technical documentation
Find Your Model
Like a sommelier recommending wine — answer 5 questions and we'll suggest the best model for your palate.
What matters most for your application?
The Decision Map
Quick reference for choosing the right model by scenario.
A simple decision framework.
Think of choosing a model like choosing a restaurant. Fast food (Haiku) for quick meals. A solid bistro (Sonnet) for most occasions. A Michelin-star restaurant (Opus) when the experience really matters. The map on the right shows which model fits each scenario.
Haiku: when speed trumps depth.
High-volume classification, real-time responses, simple routing decisions. Haiku handles these at a fraction of the cost. A customer support system processing 50,000 tickets per day would cost $160/month on Haiku vs. $3,000/month on Opus.
Sonnet: the default choice.
If you're building a coding assistant, generating marketing content, or analyzing data — Sonnet is almost always the right starting point. It handles code generation with 93% accuracy on HumanEval and provides excellent reasoning at a fraction of Opus's cost.
Opus: when accuracy is everything.
Legal contract review where a missed clause costs millions. Research synthesis where nuance matters. Strategic planning where the recommendation drives real decisions. Opus is for when the cost of being wrong exceeds the cost of the model.