Models · model

How to use Claude Haiku 4.5 in production

A practical guide to claude-haiku-4-5, using data from the AI & LLM Index. The goal is a shortlist and a decision, not a tour of every option.

What Claude Haiku 4.5 is

Claude Haiku 4.5 is a multimodal model from Anthropic, released in 2025-06-10. Claude Haiku 4.5 is a multimodal model from Anthropic. It offers a 1,000,000-token context window, costs $13.86 per million input tokens and $74.67 per million output tokens, and averages 79.4 across the index's benchmark suite.

It is a proprietary model under a proprietary licence. That determines whether you can self-host it, fine-tune it and keep data inside your own infrastructure.

Pricing and context

Claude Haiku 4.5 costs $13.86 per million input tokens, $74.67 per million output tokens and $1.15 per million cached input tokens. The blended 3:1 figure is about $29.06 per million tokens.

The context window is 1,000,000 tokens and the maximum output is 128,000 tokens. If your prompts regularly exceed the context window, you need chunking or retrieval before price becomes the main issue.

Benchmark position

Claude Haiku 4.5 averages 79.4 across the index's scored benchmarks, ranking #135 of 600. Its strongest relative result is Arena Elo.

Use the table below to see the full benchmark profile. A single strong score is less useful than a consistent profile across the benchmarks that resemble your task.

Alternatives

The closest models by average score are Nova Pro, MiniMax-M2 Instruct, Doubao 1.5 Pro Turbo. Compare their prices before assuming the closest score is the best substitute; a small quality drop can cut unit cost substantially.

Claude Haiku 4.5 benchmark profile

BenchmarkScoreRankPercentile
MMLU-Pro84.1#7987th
GPQA Diamond74.1#16473th
SWE-bench Verified69.2#12779th
HumanEval84.7#12280th
MATH-50081.4#17870th
AIME 202576.5#13478th
LiveCodeBench71.6#15574th
IFEval85.2#18170th

Related

FAQ

Is Claude Haiku 4.5 worth it?

Claude Haiku 4.5 averages 79.4 and costs $13.86 per million input tokens. Whether it is worth it depends on whether your task benefits from its benchmark profile; test it against a cheaper alternative.

What is the most important factor in claude-haiku-4-5?

Fit to the task beats raw benchmark rank. Define the failure modes, pick the benchmarks that resemble them, then test a small shortlist on your own examples.

How many models should I evaluate?

Three is usually enough: a quality candidate, a value candidate and a fallback. More candidates add work without changing the decision.

How do I control cost?

Compare blended 3:1 price, watch output tokens, use caching where available and route simple requests to a cheaper model.