Claude Haiku 5.5: What’s New and How to Use It for Fast, Cheap AI Workflows

Anthropic released Claude Haiku 5.5 on October 7, 2026, and calls it its cheapest, fastest, and most capable small model. The API model ID is claude-haiku-5-5, and prompts up to 100,000 tokens cost \$0.10 per million input tokens and \$0.50 per million output tokens. This article covers what changed since Haiku 4.5, how to build fast, cheap AI workflows with it in Python (classification, extraction, and sub-agents), and when a bigger Claude model is still the better pick.

Anthropic positions Haiku 5.5 for high-volume, cost-sensitive work such as summaries, compaction, database queries, and classification, and as a subagent alongside Opus 5.5 and Sonnet 5.5. It is available on the Claude Platform, Amazon Web Services, Google Cloud, and Microsoft Azure. Its safety evaluations are documented in the system card. Anthropic's deprecation page lists the earliest retirement date as October 7, 2027.

What's New Compared With Haiku 4.5?

Feature	Haiku 4.5	Haiku 5.5
Context window	200K tokens	1M tokens
Max output	64K tokens	128K tokens
Thinking control	Manual thinking budget	Adaptive thinking with an effort setting, a first for a Haiku
Price per million tokens, prompts up to 100K (input / output)	\$1.00 / \$5.00	\$0.10 / \$0.50
Price over 100K	\$1.00 / \$5.00	\$0.50 / \$2.50
Smallest cacheable prompt	4,096 tokens	512 tokens
GDPval-AA (knowledge work)	735	1620
OSWorld 2.1, offline subset (computer use)	15.7%	72.4%
Humanity's Last Exam, no tools	10.2%	45.9%
Terminal-Bench 4.0 (agentic coding)	0.0%	39.2%

The benchmark rows are Anthropic's own results, and the launch page shows Sonnet 5.5 ahead on all four (for example, 70.6% on Terminal-Bench 4.0). The price cut is also smaller in practice than the list prices suggest. Haiku 5.5 uses a newer tokenizer, so the same text counts as roughly 30% more tokens. Anthropic puts the average saving at around 75%, after accounting for the tokenizer and for the 10% of requests that run over 100K tokens.

python haiku_tasks.py