Claude Opus 5.5 pricing for intent detection

Quick start

Call Claude Opus 5.5 with the Anthropic Python SDK:

from anthropic import Anthropic

client = Anthropic()
response = client.messages.create(
    model="claude-opus-5-5",
    max_tokens=100,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.003200 per call at standard rates
# Input:  $4.00/MTok
# Output: $20.00/MTok

How Claude Opus 5.5 compares on cost

Per-call cost for all models scoring 50+ on intent detection benchmarks. Hover for details.

Cheapest viable option: GPT 5.6 Luna at $0.000180/call (94% less, 91/100 quality).

What drives intent detection cost

Intent classification sits in the hot path of a conversational product, so latency constrains model choice more than price does: batch processing is not available to you when the user is waiting. That makes the fast tier the natural fit on both axes. The intent list is constant and belongs in the cached prefix.

For Claude Opus 5.5 specifically, that means output tokens cost 5.0x more than input, at $20.00 vs $4.00 per MTok. At this use case’s 300/100 token split, output accounts for 63% of the $0.003200 per-call cost.

Claude Opus 5.5 quality benchmarks for intent detection

Benchmark data for intent detection is pending for Claude Opus 5.5. Test against your own evaluation suite before moving to production.

Performance across all task types

Claude Opus 5.5 scores by task type. Intent Detection highlighted.

Confidence scores are a third-party benchmark snapshot from week 29 of 2026. This snapshot is 10 weeks old, so treat the scores as directional and validate against your own evals.

About Claude Opus 5.5

Added by the model-onboarding job on 2026-09-24. Prices confirmed by the provider pricing page and the LiteLLM cross-check. Release date from the provider model list. Not yet benchmarked, so never recommended.

Other models from Claude

You can compare Claude Opus 5.5 head-to-head against any other model on our model comparison pages, or track how pricing has changed over time on the pricing history tracker.

Frequently asked questions