Anthropic

Haiku 5.5 API Pricing

Haiku 5.5 has tracked API rates of $0.1 per 1M input tokens and $0.5 per 1M output tokens. Cached input is billed at $0.01 per 1M tokens. Rates are in USD from the AI IQ dataset; see the Haiku 5.5 model profile for capability scores.

Input / 1M
$0.1
Output / 1M
$0.5
Effective / 1M I/O
$0.2865
Cost Rank
#17

Price List

RatePrice (USD)Notes
Input$0.1per 1M tokens
Output$0.5per 1M tokens
Cached input (read)$0.01per 1M tokens
Cache write$0.125per 1M tokens
Cache write (1-hour)$0.2per 1M tokens
Blended input + output$0.61M in + 1M out at list price
Context window1M tokens

Pricing References and Billing Caveats

These are the tracked rates, not a live quote. Hosted-provider choice, region, long-context tiers, caching, batch discounts, and temporary promotions can change the actual bill. Open-weight availability does not mean hosted inference is free. Workload examples assume the listed standard input and output rates, with no cache hits or discounts.

Anthropic pricing catalog — check current offerings and rate tiers. This catalog may not list historical models or third-party hosted endpoints.

Publisher-reported pricing notes

  • Higher pricing applies to prompts over 100,000 tokens; listed token prices use the lower prompt-length tier.
  • Effort settings are configurations of one model, not separate commercial models.

Official release and model references

These references document the model and its release; not every reference is a pricing table. Confirm the applicable endpoint and billing tier with your inference provider before purchase.

What a Workload Costs

Computed straight from the list prices above — token counts are illustrative workload sizes, not measurements of Haiku 5.5.

WorkloadCostTokens
Short chat turn$0.000351K in / 500 out
Summarize a long document$0.011100K in / 2K out
Agentic coding session$0.100500K in / 100K out
1M input + 1M output tokens$0.6001M in / 1M out

Effective Cost

In AI IQ's scoring, Haiku 5.5's list price is adjusted by a token-usage multiplier of 0.477, giving an effective cost of $0.2865 per 1M input + output tokens. That places it #17 of 148 models on the effective-cost ranking (lower is cheaper). Some models spend far more tokens than others on the same task, so effective cost compares what a unit of work really costs. Method details are on the methodology page; see all models on the cost charts and the falling cost of intelligence over time.

Compare