Claude Opus 5.5 pricing for content moderation

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: ~$0.004000 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 content moderation benchmarks. Hover for details.

Cheapest viable option: GPT 5.6 Luna at $0.000220/call (95% less, 91/100 quality).

What drives content moderation cost

The highest-volume, lowest-per-call task on this list: short input, a label out. Per-call cost is nearly negligible, which means total cost is a function of throughput alone. At moderation volume the difference between tiers is the entire budget, and a fast-tier model handling the clear cases with escalation for the ambiguous ones beats one model handling everything.

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 500/100 token split, output accounts for 50% of the $0.004000 per-call cost.

Claude Opus 5.5 quality benchmarks for content moderation

Benchmark data for content moderation 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. Content Moderation 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