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anthropic--claude-4.7-opus

Claude Opus 4.7 represents Anthropic's latest refinement of its flagship agentic model family, engineered specifically for long-running asynchronous workflows and complex enterprise automation. Building on the coding and agentic foundations established by its predecessor, the model brings adaptive reasoning that adjusts to task complexity, enabling it to manage sophisticated multi-step operations with sustained consistency. Its tool-use capabilities and support for structured outputs position it as a practical backbone for production AI agents that need to interact with external systems, handle attachments, and produce deterministic results across extended execution windows.

The model's performance profile shows exceptional reliability with a 99% benchmark success rate, minimizing failures during demanding agentic runs. Across key capabilities, it achieves top-quartile accuracy in instruction following, coding, reasoning, and mathematics, with general knowledge and ethics benchmarks reaching near-perfect scores. Though its pricing sits at premium levels, practical deployment data shows substantial token volume and monthly spend in real-world agentic systems, reflecting its fit for enterprise workflows where dependable multi-step reasoning outweighs cost considerations.

SAP AI Coreanthropic--claude-4.7-opusclaude-opus

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Provider
SAP AI Core
Model key
anthropic--claude-4.7-opus
Release date
Apr 16, 2026
Last updated
Apr 16, 2026
Knowledge cutoff
2026-01-31
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$5.00
Output token cost
$25.00

Limits

Output tokens
128,000 tokens
Context window
1,000,000 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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CoverageBenchmark

The Princeton HAL Reliability Dashboard's predictability page for τ-bench (airline, clean) explicitly lists Claude 4.7 Opus as a benchmarked model, posting an Expected Calibration Error of 0.066 and an AUROC of 0.695. These calibration metrics indicate that the model's expressed confidence aligns closely with whether i On the dashboard's agent leaderboard aggregation for the predictability dimension, Claude Opus 4.7 ranks first with 84.6% accuracy, a 0.89 reliability score, 0.87 predictability, 0.93 aggregated calibration, 0.70 AUROC, and 0.87 Brier score — leading Claude Sonnet 4, Claude Opus 4.5, and GPT-5.5. The page provides tech

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