Claude Opus 4.5 is Anthropic’s frontier reasoning model optimized for complex software engineering, agentic workflows, and long-horizon computer use. It offers strong multimodal capabilities, competitive performance across real-world coding and reasoning benchmarks, and improved robustness to prompt injection. The model is designed to operate efficiently across varied effort levels, enabling developers to trade off speed, depth, and token usage depending on task requirements. It comes with a new parameter to control token efficiency, which can be accessed using the OpenRouter Verbosity parameter with low, medium, or high.
Opus 4.5 supports advanced tool use, extended context management, and coordinated multi-agent setups, making it well-suited for autonomous research, debugging, multi-step planning, and spreadsheet/browser manipulation. It delivers substantial gains in structured reasoning, execution reliability, and alignment compared to prior Opus generations, while reducing token overhead and improving performance on long-running tasks.
Evaluations
18
across 16 benchmarks
Latency
1.2s
Context length
200k
tokens
Cost
$5 · $25
input · output per 1M tokens
Key takeaways
Claude Opus 4.5 is a Transformers-based frontier reasoning model by Anthropic, optimized for complex software engineering, agentic workflows, and long-horizon computer use. It excels at autonomous research, debugging, multi-step planning, and spreadsheet/browser manipulation.
The model features multimodal capabilities (text+image to text), an extended context length of 200,000 tokens, a parameter for token efficiency (OpenRouter Verbosity), and supports advanced tool use, context management, and multi-agent setups.
Capabilities include strong mathematical problem-solving, robust logical reasoning, consistent high accuracy in Python programming tasks, and effective string manipulations. Limitations include struggles with nuanced spatial reasoning, occasional arithmetic errors, difficulties with complex number properties, and inabilities to infer indirect root causes in payment failure reasoning.
Claude Opus 4.5 achieved 96.95% accuracy on the LLM Truthfulness benchmark (Human Evaluation) and demonstrated excellent proficiency in Python programming tasks. It also performed strongly in specialized scientific domains like Mathematics and Biology/Medicine, providing detailed and logical explanations.
The model performed poorly on 'Humanity's Last Exam' (Factual Reasoning Historical Facts) with 13.59% accuracy and Payment Failure Reasoning with 35.18% accuracy. Common failure modes include errors in precise numerical calculations, misinterpretation of complex multi-part questions, and logical errors in complex algorithmic tasks.
