Claude 4 Opus (Non-reasoning)

The most effective cost-saving technique is to not use Opus for every task. Implement a multi-model system, or 'cascade,' where queries are first handled by a cheaper, faster model like Claude 3.5 Sonnet or Haiku.

• Use the cheaper model for initial triage, simple Q&A, and filtering out requests that don't require advanced reasoning.
• Develop logic to identify complex queries (based on keywords, length, or user feedback) and escalate only those to Claude 4 Opus.
• This ensures you are only paying the premium Opus price for the tasks that genuinely need its superior intelligence.

Claude 4 Opus has a 5x price difference between input and output tokens ($15 vs $75). Design your application to take advantage of this. Structure your prompts and workflows to be input-heavy and output-light.

• Good Use Cases: Classification, data extraction, sentiment analysis, scoring, or any task where the model processes a lot of information to produce a short, structured answer (e.g., JSON).
• Bad Use Cases: Summarizing short text into long text, rewriting articles to be longer, or generating verbose, conversational responses.
• Explicitly instruct the model to be concise in your prompts: "Answer in one sentence," or "Provide only the final numerical answer."

Many applications receive redundant queries. Calling the Opus API for the same question multiple times is an unnecessary expense. Implement a robust caching layer to store and retrieve previous responses.

• Use a fast key-value store like Redis or Momento.
• Create a cache key based on the prompt content (or a hash of it). For user-specific queries, include a user ID in the key.
• Before calling the API, check if a valid response exists in the cache. This can dramatically reduce API calls in high-traffic applications, saving both money and latency.

Well-crafted prompts not only produce better results but also save money. Focus on two areas: reducing input tokens and, more importantly, controlling output tokens.

• Input: Be as concise as possible in your instructions. Remove filler words and redundant examples.
• Output: This is where the biggest savings are. Use prompt instructions to strictly define the output format and length. For example, instead of asking "Explain this concept," ask "Explain this concept in three bullet points."
• Requesting structured data like JSON or XML with specific keys forces the model to be concise and predictable, cutting down on expensive output tokens.

Claude 4 Opus is the most advanced and intelligent large language model created by Anthropic. It is the flagship model in the Claude 4 family, designed for handling highly complex tasks that require deep reasoning, analysis, and creativity. It features a 200,000 token context window and multimodal (image and text) input capabilities.

Opus and GPT-4 Turbo are direct competitors at the frontier of AI capabilities. Key differences include:

• Pricing: Opus is significantly more expensive, especially for output tokens ($75/M for Opus vs. ~$30/M for GPT-4 Turbo).
• Context Window: Opus has a larger standard context window at 200k tokens compared to GPT-4 Turbo's 128k.
• Safety: Anthropic places a heavy emphasis on its Constitutional AI training, which some users find results in a more cautious or 'refusal-prone' model, but one that is highly reliable for enterprise safety.
• Performance: Benchmarks vary, but both models perform at the highest level of intelligence. Opus is often praised for its nuanced writing and analysis, while GPT-4 is noted for its strong coding and reasoning skills.

The "(Non-reasoning)" tag on this page refers to the specific benchmark category from which some of the performance data was sourced. It does not mean the model itself is incapable of reasoning—quite the opposite. This benchmark likely focuses on tasks related to knowledge retrieval, comprehension, and instruction-following, while excluding tests that require complex, multi-step logical deduction. This helps isolate certain performance characteristics but doesn't represent the model's full capabilities.

The premium price of Claude 4 Opus reflects its position as a frontier model. The costs associated with training and, more importantly, serving a model of this size and complexity are immense. Anthropic has priced it as a premium product, targeting use cases where its top-tier intelligence provides enough value to justify the cost. The high price also implicitly encourages users to use it judiciously, reserving it for tasks that truly require its power, and to use cheaper models like Sonnet or Haiku for more routine tasks.

The massive 200,000-token context window is a key differentiator. It enables workflows that are impossible with smaller models, such as:

• Legal and Financial Document Analysis: Feeding an entire long contract or quarterly report into the model for summarization, Q&A, or risk identification.
• Full Codebase Analysis: Providing the model with an entire codebase to ask questions about dependencies, identify bugs, or suggest refactoring improvements.
• Academic Research: Processing multiple research papers at once to synthesize findings and identify trends.
• Extended Conversations: Maintaining a coherent, long-running conversation with a chatbot without losing track of details mentioned hours or days earlier.

Constitutional AI (CAI) is a research and training framework developed by Anthropic to create helpful, harmless, and honest AI systems. Instead of relying solely on extensive human feedback to police the model's behavior, the model is trained to align itself with a 'constitution'—a set of explicit principles and rules. During training, the model learns to critique and revise its own responses based on these constitutional principles, effectively teaching itself to be safer and more aligned with human values without constant human supervision for every potential harm.