Claude Opus 4
Anthropic's most powerful model. Exceptional at complex analysis, agentic tasks, and extended reasoning.
Claude Opus 4 is text & chat AI model from Anthropic, priced at €150.00 per 1M input tokens with a 200K tokens context window.
0.7
Examples
See what Claude Opus 4 can generate
Analyze the economic implications of universal basic income. Consider arguments from both proponents and critics, citing specific pilot programs.
UBI has been tested in several notable pilot programs with mixed but informative results. Finland's 2017-2018 experiment gave 2,000 unemployed citizens €560/month and found improved well-being and modest employment gains, though critics noted the small sample size. Stockton, California's SEED program ($500/month to 125 residents) showed recipients were more likely to find full-time employment than the control group, challenging the 'laziness' argument. Proponents argue UBI provides a safety net that encourages entrepreneurship and addresses job displacement from automation. Critics counter that large-scale funding—estimated at $3+ trillion annually for the US—would require massive tax restructuring, and inflationary pressures could erode the benefit's purchasing power.
I need to migrate a monolithic Django app to microservices. Create a step-by-step migration plan with risk mitigation strategies.
Phase 1 (Weeks 1-4): Audit and map all domain boundaries in your monolith. Identify bounded contexts using domain-driven design principles. Document all inter-module dependencies and shared database tables. Phase 2 (Weeks 5-8): Implement the Strangler Fig pattern—deploy an API gateway that routes traffic to either the monolith or new services. Extract the least-coupled domain first (often authentication or notifications) as a proof of concept. Phase 3 (Weeks 9-16): Gradually extract remaining services. For each: create a dedicated database, implement an anti-corruption layer for data sync, and use event-driven communication (Kafka/RabbitMQ) instead of synchronous calls. Risk mitigation: Run services in parallel with the monolith ('dark launching') before cutting over. Implement distributed tracing (Jaeger/Zipkin) from day one. Keep rollback paths open by maintaining the monolith until each service proves stable in production for at least 2 sprint cycles.
Pricing
API Integration
Use our OpenAI-compatible API to integrate Claude Opus 4 into your application.
npm install railwailimport railwail from "railwail";
const rw = railwail("YOUR_API_KEY");
// Simple — just pass a string
const reply = await rw.run("claude-opus-4", "Hello! What can you do?");
console.log(reply);
// With message history
const reply2 = await rw.run("claude-opus-4", [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "Explain quantum computing simply." },
]);
console.log(reply2);
// Full response with usage info
const res = await rw.chat("claude-opus-4", [
{ role: "user", content: "Hello!" },
], { temperature: 0.7, max_tokens: 500 });
console.log(res.choices[0].message.content);
console.log(res.usage);Deep dive — Anthropic's Claude Opus 4
Anthropic was founded in 2021 by Dario Amodei (CEO) and Daniela Amodei (President) after both left OpenAI together with several senior researchers including Tom Brown, Sam McCandlish and Jared Kaplan. The company is structured as a Public Benefit Corporation focused on AI safety research and the deployment of reliable, interpretable, and steerable AI systems. Anthropic's foundational research includes Constitutional AI (2022), Sparse Autoencoders for interpretability, and the influential paper 'Scaling Laws for Neural Language Models'. The Claude model family launched in March 2023, with major releases including Claude 2 (2023), Claude 3 family (Haiku, Sonnet, Opus in March 2024), Claude 3.5 Sonnet (June 2024), Claude 3.7 Sonnet with extended thinking (February 2025) and Claude 4 family (May 2025). Investors include Google ($3B+), Amazon (committed up to $8B), Spark Capital and Lightspeed, with total funding exceeding $15 billion and a 2025 valuation above $60 billion. Anthropic also publishes Responsible Scaling Policies and is a leading voice in ASL safety levels.
Visit Anthropic →Claude Opus 4 is Anthropic's flagship reasoning model released in May 2025, optimized for agentic coding, multi-hour autonomous tasks and deep research. It uses a decoder-only Transformer architecture trained on a diverse multilingual corpus including curated web data, code repositories, books and licensed datasets, with an explicit data cutoff of March 2025. The model is post-trained with a combination of Reinforcement Learning from Human Feedback (RLHF), Constitutional AI (CAI) and Reinforcement Learning from AI Feedback (RLAIF), where a constitution of principles guides self-critique and revision. Opus 4 supports two operating modes: standard fast responses and extended thinking mode in which the model produces an internal reasoning trace before its final answer. Tool use, parallel tool calling and long-running agent loops are first-class behaviours, with native support for Anthropic's computer use API (screen control, mouse, keyboard). Vision grounding was trained jointly with text so that images, charts and PDFs can be reasoned about within the same context window. The model was evaluated against ASL-3 safeguards under Anthropic's Responsible Scaling Policy and shipped with classifier-based safety filters covering CBRN, cyberweapons, and child safety.
- Parameters
- Undisclosed (estimated multi-hundred billion parameters dense)
- Context
- 200K tokens
- State-of-the-art coding performance on SWE-bench Verified (~72% on agentic eval at launch)
- Extended thinking mode for chain-of-thought reasoning on hard math and science problems
- Sustained multi-hour autonomous coding sessions with tool use and self-correction
- Native computer use: screenshots, mouse and keyboard control of a virtual desktop
- 200K token context window with strong needle-in-haystack recall across the full window
- Multimodal input: text, images, PDFs, charts and diagrams in a single prompt
- Parallel tool calling and stable multi-turn agent loops
- Constitutional-AI alignment with low refusal rate on benign questions
- Strong instruction following including XML tags, schemas and strict JSON output
- Multilingual fluency across English, German, French, Spanish, Japanese, Chinese and more
- Best for: complex coding agents, research synthesis from large document sets, regulated-industry copilots, autonomous workflows.
Pretrained on a curated mix of publicly available internet text, licensed third-party data, code from public repositories and human-generated conversations. Anthropic does not disclose exact token counts but the corpus is multi-trillion tokens with heavy filtering for quality, deduplication and copyright. The data cutoff is March 2025. Post-training uses RLHF, Constitutional AI and RLAIF on a much smaller, high-quality set of human-rated comparisons and synthetic constitutional critiques.
License: Proprietary commercial license via Anthropic API, Amazon Bedrock and Google Vertex AI. Commercial use permitted under Anthropic's Usage Policy.
Known limitations
- No native audio or video input
- Knowledge cutoff March 2025; lacks real-time information without tool use
- Higher latency and cost than Sonnet/Haiku tiers
- Will refuse some legitimate red-team and security research prompts
- No fine-tuning available to external customers
Frequently asked questions
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