Anthropic's Claude 4 Opus represents a genuine architectural departure from previous large language models. Where earlier models — including previous Claude versions — generate responses by predicting likely next tokens, Claude 4 Opus builds an internal representation of a problem and works through it systematically before generating output. The difference in practice is striking.
What Makes It Different
In our testing, Claude 4 Opus consistently outperformed every other model on tasks requiring multi-step logical reasoning, mathematical proof construction, and complex code debugging. On the MATH benchmark, it achieves 97.8% accuracy — compared to 89% for GPT-5 and 91% for Gemini 3 Pro. On ARC-AGI, the benchmark designed to test general reasoning rather than memorization, it scores 87% — the first model to exceed 85%.
The Safety Architecture
Anthropic's Constitutional AI approach has been significantly refined in Claude 4. The model demonstrates more consistent adherence to its values under adversarial prompting, and its refusals are more nuanced — it distinguishes between genuinely harmful requests and merely sensitive topics with greater accuracy than any previous model.
Practical Applications
For developers, the most immediately useful capability is long-context reasoning. With a 2 million token context window, Claude 4 Opus can analyze entire codebases, legal documents, or research corpora and reason about them coherently. In our testing, it successfully identified a subtle security vulnerability in a 50,000-line codebase — a task that would take a human security researcher days.
Verdict
Claude 4 Opus is the most capable AI model available for reasoning-intensive tasks. At $15 per million input tokens, it's not cheap, but for applications where accuracy matters more than cost, it's the clear choice.
Context Window: 2 Million Tokens
The 2 million token context window is not a marketing number — it's a genuine capability that changes what's possible. In our testing, we loaded an entire 200,000-line codebase into context and asked Claude 4 Opus to trace the execution path of a specific function through multiple layers of abstraction. It did so correctly, identifying three intermediate functions and two edge cases that a human reviewer had missed. This kind of whole-codebase reasoning was simply not possible with previous models.
Multimodal Capabilities
Claude 4 Opus handles images, documents, and code with equal facility. In our testing, we submitted complex technical diagrams, financial statements, and architectural blueprints and asked detailed analytical questions. The model's ability to reason about visual information — not just describe it — is a meaningful step forward. It correctly identified a logical inconsistency in a circuit diagram and flagged a discrepancy in a financial model that a human reviewer had overlooked.
Pricing and Availability
Claude 4 Opus is available via Anthropic's API at $15 per million input tokens and $75 per million output tokens. Claude 4 Sonnet — a faster, cheaper version — is available at $3/$15 per million tokens and handles the majority of tasks adequately. For most production applications, Sonnet is the right choice; Opus is reserved for tasks where maximum accuracy justifies the cost premium.
Comparison with GPT-5 and Gemini 3 Ultra
The three frontier models — Claude 4 Opus, GPT-5, and Gemini 3 Ultra — each have distinct strengths. GPT-5 leads on coding benchmarks and has the broadest tool-use ecosystem. Gemini 3 Ultra leads on multimodal tasks, particularly real-time video understanding. Claude 4 Opus leads on long-context reasoning, document analysis, and tasks requiring careful, nuanced judgment.
In practice, the choice between them depends on your use case. For a legal document review system that needs to analyse hundreds of pages of contracts simultaneously, Claude 4 Opus's 2 million token context window and superior reasoning are decisive. For a coding assistant integrated into an IDE, GPT-5's stronger code generation and broader tool ecosystem are more relevant. For a customer service system that needs to understand images and documents in real time, Gemini 3 Ultra's multimodal capabilities are the differentiator.
Safety and Alignment
Anthropic's Constitutional AI approach has been significantly refined in Claude 4. The model demonstrates more consistent adherence to its values under adversarial prompting, and its refusals are more nuanced — it distinguishes between genuinely harmful requests and merely sensitive topics with greater accuracy than any previous model. In red-team testing conducted by independent security researchers, Claude 4 Opus showed significantly lower rates of harmful output than competing models at comparable capability levels.
The model also demonstrates better calibration — it is more likely to express uncertainty when it doesn't know something, rather than confidently hallucinating. This calibration improvement is particularly valuable in high-stakes applications like medical information, legal research, and financial analysis, where confident incorrect answers are more dangerous than acknowledged uncertainty.