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Claude Haiku 4.5: token counter & pricing

Anthropic · exact (uses official tokenizer) · pricing as of 2026-05-31.

Updated · By · Methodology

Provider
Anthropic
API model ID
claude-haiku-4-5-20251001
Context window
200,000 tokens
Input price
$1.00 per 1M tokens
Output price
$5.00 per 1M tokens
Tokenizer accuracy
exact (uses official tokenizer)
Pricing as of
2026-05-31

Open the counter to count tokens for Claude Haiku 4.5 in real time.

What is Claude Haiku 4.5?

Claude Haiku 4.5 is Anthropic's fastest and cheapest Claude model, designed for high-volume workloads where you need Claude's quality without paying frontier prices. At $1 input / $5 output per million tokens, it is 3× cheaper than Sonnet on both input and output.

How tokens are counted here

Like the other Claude pages here, Haiku counts come from Anthropic's /v1/messages/count_tokens endpoint, relayed through our serverless proxy with nothing logged or stored along the way. The result is exact, the same number Anthropic bills against, and Haiku shares its tokenizer with Sonnet 4.6 and earlier Claude models.

Exact counts matter more at Haiku's end of the market than anywhere else. High-volume pipelines live close to their rate limits, and Anthropic enforces those limits in tokens per minute, not requests per minute. If your batch job is sized off a rough words-times-1.3 estimate, a 10% undercount is the difference between a smooth run and a wall of 429 errors at 2 a.m. Measure a representative sample here before you size the pipeline.

When to use Haiku

When not to use Haiku: anything requiring multi-step reasoning, long-form generation, or careful instruction-following on subtle constraints. Use Sonnet there.

Pricing notes

At $1/$5 per million, Haiku is in the same league as GPT-4o mini ($0.15/$0.60) and Gemini 2.5 Flash ($0.30/$2.50). GPT-4o mini is the cheapest of the three by a wide margin; Gemini Flash sits in the middle; Haiku costs the most but tends to win on instruction-following and refusal behavior. Match the model to the task, not just the price.

What Claude Haiku costs in production

Run the numbers on a consumer chatbot with 10,000 daily active users, each averaging 5 turns a day at 200 input tokens and 100 output tokens per turn:

The same traffic on Gemini 2.5 Flash ($0.30/$2.50) bills $90 + $375 = $465/month, less than half. On Claude Sonnet 4.6 ($3/$15) it bills $900 + $2,250 = $3,150/month. At this scale the model choice is a four-figure monthly decision, which is why teams eval Haiku against Flash on their actual transcripts rather than picking on brand.

Migrating from Haiku 3

This one is not a drop-in swap. The model string changes from the dated Haiku 3 identifier to claude-haiku-4-5-20251001, and the per-token price went up: Haiku 4.5 is a meaningfully more capable model sold at a higher rate, not a price-matched refresh. Budgets built on Haiku 3 economics need re-running with $1/$5. What you get back is much stronger reasoning and instruction-following, which often lets Haiku 4.5 absorb tasks you previously escalated to Sonnet, so the net bill can land lower even though the unit price rose. Token counts are stable across the migration since the tokenizer is shared.

Claude Haiku vs the obvious alternative

Gemini 2.5 Flash is the head-to-head, covered in detail at Claude Haiku vs Gemini 2.5 Flash. Flash costs $0.30/$2.50 against Haiku's $1/$5, so Google wins price about 2x to 3x, while Haiku is typically stronger on following structured-output instructions exactly. If your pipeline retries on malformed JSON, those retries can eat the price gap.

Common questions

Is Haiku good enough for production?

Yes, for the workloads it's designed for. Run a labeled eval set; if Haiku hits your accuracy bar, ship it. Most teams discover their tasks didn't need Sonnet.

Why does Haiku sometimes refuse benign requests Sonnet handles?

Smaller Claude models can be more conservative on edge cases. If you're hitting refusal walls on a legitimate workload, the fix is usually a clearer system prompt rather than upgrading to Sonnet.

How does Haiku's tokenization compare to other Claude models?

Haiku 4.5, Sonnet 4.6, and earlier Claude models share a common tokenizer, token counts match across them for the same input.

Claude Opus 4.8 is the exception: it uses a new tokenizer that can produce up to 35% more tokens than the older Claude tokenizer for the same text (per Anthropic). If you compare Opus 4.8 to Haiku in our counter, expect a higher count on Opus, that's the tokenizer change, not the size class.

Compare Claude Haiku 4.5 to other models

Detailed comparisons