Opus 5
Anthropic's flagship 1M-context frontier multimodal foundation model, engineered for deep reasoning, autonomous coding agents, and complex long-horizon system execution.
Empirical Evaluation Results
Architectural Profile & Capabilities
Claude Opus 5 represents Anthropic's flagship frontier foundation model, engineered specifically for high-leverage cognitive workflows, autonomous agentic programming, complex visual comprehension, and deep scientific reasoning. Positioned against frontier competitors like OpenAI's GPT-6 Astra / GPT-5.6 Sol and Google's Gemini 3.8 Flash, Opus 5 integrates Anthropic's adaptive extended thinking natively into the core generation pipeline, allowing dynamic calibration of reasoning effort. The model natively features a 1,000,000-token context window with an unprecedented 128,000 synchronous output token capacity, eliminating artificial truncation on large-scale code synthesis and end-to-end repository migrations. On empirical benchmarks, Opus 5 claims top positions, achieving 63.1 on the Artificial Analysis Intelligence Index, 68.1 on the Artificial Analysis Coding Agent Index, 96.0% on SWE-bench Verified, 90.4% on ARC-AGI-2, and 93.7% on GPQA Diamond. Priced aggressively at $5.00/1M input and $25.00/1M output, Opus 5 brings frontier Opus-tier capability to enterprise agent orchestration at half the cost structure of predecessor architectures.
Dense-sparse hybrid autoregressive multimodal transformer utilizing grouped-query attention (GQA), rotary positional embeddings (RoPE), cross-attention vision projection adapters, and native test-time compute scaling (adaptive dynamic reasoning / thinking tokens) with effort level configuration. Supports full 1M context in a single unified architecture with a 128K max synchronous output window.
Recommended Workloads & Primary Use Cases
- •High test-time compute latency: Deliberate inference profile (50-60 tps) with substantial Time-To-First-Token (TTFT) when adaptive extended thinking operates at high or max effort.
- •Significant cost divergence on high reasoning workloads: While base tokens are priced aggressively at $5/1M in and $25/1M out, reasoning tokens are billed as output tokens, rapidly multiplying transaction costs on iterative agentic loops.
- •Context caching dependency: Operating at the upper boundary of the 1,000,000-token context window demands prompt caching architectures; cold prefill on 1M contexts induces substantial TTFT bottlenecks.
- •Terminal drift in long agentic loops: Despite leading Terminal-Bench 4.0, extended shell interaction loops without explicit state checks can suffer from cumulative environmental desynchronization.
Calls to Opus 5 are routed through strict Zero Data Retention inference channels. Prompts and outputs are never stored, indexed, or monitored by Anthropic.
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