Claude Opus 4.7
Anthropic's mid-tier model. Long-context specialist with 1M window.
Scores by test
Methodology →What you need to know
Claude Opus 4.7 is built for complex agentic workflows and high-reasoning tasks. It achieves perfect internal scores in tool calling, agentic planning, and strategic analysis, supported by a strong SWE-bench Verified score of 83.5% and a 97.8% on AIME 2025. These metrics indicate a model capable of autonomous software engineering and advanced mathematical reasoning.
The model handles massive datasets effectively with a 1M token context window and a perfect 5/5 rating for long context and tabular data. However, it is not a general-purpose utility model; its performance drops to 3/5 in basic classification and safety calibration, suggesting it may struggle with simple labeling tasks or strict safety guardrails compared to its reasoning capabilities.
At a blended cost of $20.00/MTok, this is a high-premium model. The pricing is steep, positioning it as a specialized tool for high-value outputs rather than a cost-effective solution for high-volume, simple API calls. You are paying for top-tier reasoning and agentic reliability rather than raw throughput or efficiency.
Use this model if you are building autonomous agents, complex data analysis pipelines, or applications requiring deep strategic reasoning across large contexts. Skip this model if your primary use case is simple text classification, basic content moderation, or if you are operating on a tight budget for high-volume requests.
Strengths — Top 3
Relative weaknesses — Bottom 3
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