Claude Model Comparison 2026 — Haiku vs Sonnet vs Opus

Which Claude model should you use?

Most tasks

Claude 3.5 Sonnet

Best balance of speed and quality. Fast enough for production, smart enough for complex tasks. $3/$15 per 1M tokens.

Cost-sensitive / high volume

Claude 3.5 Haiku

Cheapest of the 3.5 generation. Still very capable - not a downgrade compared to older Claude 3 models. $0.80/$4 per 1M tokens.

Fastest response

Claude 3.5 Haiku

Lowest latency in the Claude lineup. Good for chatbots, real-time features, or anything where you need sub-second responses.

Full model comparison

Model	Context	Input (per 1M)	Output (per 1M)	Best for
Claude 3.5 Sonnet Latest claude-3-5-sonnet-20241022	200K	$3.00	$15.00	Production apps, coding, analysis, most tasks
Claude 3.5 Haiku Fastest Cheapest v3.5 claude-3-5-haiku-20241022	200K	$0.80	$4.00	High-volume pipelines, chatbots, cost-sensitive use cases
Claude 3 Opus claude-3-opus-20240229	200K	$15.00	$75.00	Heavy reasoning tasks where cost doesn't matter (older gen)
Claude 3 Sonnet claude-3-sonnet-20240229	200K	$3.00	$15.00	Older gen balanced option - use 3.5 Sonnet instead
Claude 3 Haiku Cheapest overall claude-3-haiku-20240307	200K	$0.25	$1.25	Ultra-cheap classification, tagging, simple tasks

All models have 200K context windows. Prices are per 1 million tokens as of March 2026. "Input" = tokens you send. "Output" = tokens the model generates. Output is usually 3-5x more expensive.

Token cost calculator

Enter how many input and output tokens you expect per month and see what each model costs.

Input tokens (monthly)

Output tokens (monthly)

Quick answers

Claude 3.5 Haiku vs Claude 3 Haiku

3.5 Haiku is significantly smarter than the older Claude 3 Haiku. It costs more ($0.80 vs $0.25 per 1M input) but the quality difference is real. Use Claude 3 Haiku only if you're very cost-constrained and the task is extremely simple.

Claude 3 Opus - still worth it?

Probably not for most things. It was the top model of its generation but Claude 3.5 Sonnet is faster, cheaper, and competitive on most benchmarks. Opus at $15/$75 per 1M is hard to justify unless you have a very specific use case that demands it.

What's the context window?

All Claude models here support 200K tokens - roughly 150,000 words or about 500 pages of text. That's enough for most use cases including long documents, full codebases, and extended conversations.

How is output priced higher than input?

Generating tokens is computationally more expensive than reading them. As a rough rule: in most real-world usage you'll have 5-10x more input tokens than output, so the total cost is usually dominated by input unless you're generating very long responses.