Claude Opus 4

Text & chattUtgÄttInte tillgÀnglig
av AnthropicModell-ID: claude-opus-4

Anthropic's most powerful model. Exceptional at complex analysis, agentic tasks, and extended reasoning.

Status
Inte tillgÀnglig
Kontext
200 000 tokens
Max. utdata
32 000 tokens
Inmatning → utmatning
Text → Text
Utvecklare
Anthropic
Uppdaterad
23 september 2026

Claude Opus 4 Àr för nÀrvarande otillgÀnglig

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Nyare version tillgÀnglig: Claude Opus 4.8

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Playground

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Chatt

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Om Claude Opus 4

Kort sagtFrÄn och med 23 september 2026

Claude Opus 4 Àr en modell av Anthropic i kategorin Text & chatt. Claude Opus 4 Àr för nÀrvarande inte tillgÀnglig pÄ Railwail. Kontextfönstret innehÄller 200 000 tokens, och ett svar kan vara upp till 32 000 tokens lÄngt. Nyare version: Claude Opus 4.8.

Bakgrund

Om Anthropic

Grundat 2021 · San Francisco, USA

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.

Besök Anthropic

Arkitektur

Decoder-only Transformer (frontier reasoning model)

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.

Parameter
Undisclosed (estimated multi-hundred billion parameters dense)
Kontext
200 000 tokens

Funktioner

  • 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.

TrÀning & licens

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.

Licens: Proprietary commercial license via Anthropic API, Amazon Bedrock and Google Vertex AI. Commercial use permitted under Anthropic's Usage Policy.

SĂ€kerhetstestning: Deployed under Responsible Scaling Policy ASL-3 safeguards with constitutional classifiers, automated red-teaming, third-party evaluations by METR and Apollo Research, and a Bug Bounty programme.

KÀnda begrÀnsningar

  • 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
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Priser

Inte tillgÀnglig för nÀrvarande. Det finns ingen pris för denna modell för nÀrvarande, sÄ den kan inte köras.

05

API

Anropa Claude Opus 4 med din Railwail API-nyckel. AnvÀnd detta modell-ID i begÀran:

För nÀrvarande otillgÀnglig

Modellen har inget verifierat pris eller Àr inaktiverad; API-anrop avvisas.

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Specifikationer

Modell-ID
claude-opus-4
Utvecklare
Anthropic
Kategori
Text & chatt
Inmatning
Text
Utmatning
Text
Kontextfönster
200 000 tokens
Max. utmatning
32 000 tokens
Livscykel
UtgÄtt
Modellstorlek
Undisclosed (estimated multi-hundred billion parameters dense)
Licens
Proprietary commercial license via Anthropic API, Amazon Bedrock and Google Vertex AI. Commercial use permitted under Anthropic's Usage Policy.
KataloginlÀgg uppdaterat
23 september 2026

Taggar

  • flagship
  • reasoning
  • agentic
07

Exempelprompts

Exempel frÄn Railwail-katalogen. De genererades inte live pÄ denna sida.
  • Deep Research

    Analyze the economic implications of universal basic income. Consider arguments from both proponents and critics, citing specific pilot programs.
    Visa exempelsvar

    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.

  • Agentic Planning

    I need to migrate a monolithic Django app to microservices. Create a step-by-step migration plan with risk mitigation strategies.
    Visa exempelsvar

    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.

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AnvÀndningsfall

Vad det anvÀnds till

  • Autonomous software engineering agents
  • Long-document legal and financial analysis
  • Scientific research synthesis
  • Regulated-industry customer support copilots
  • Computer-use automation
  • Complex multi-step planning
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Vanliga frÄgor

Vad Àr Claude Opus 4?

Claude Opus 4 Àr en modell av Anthropic i kategorin Text & chatt. Den finns i Railwail-katalogen men kan inte köras för nÀrvarande.

Vad kostar Claude Opus 4 pÄ Railwail?

Claude Opus 4 kan inte köras pÄ Railwail för nÀrvarande, sÄ det finns inget aktuellt pris. TillgÀngliga alternativ med priser visas lÀngre ned pÄ denna sida.

Hur stort Àr kontextfönstret för Claude Opus 4?

Kontextfönstret för Claude Opus 4 innehÄller 200 000 tokens. Ett svar kan vara upp till 32 000 tokens lÄngt.

Hur snabb Àr Claude Opus 4?

Det finns Ànnu inte tillrÀckligt mÄnga uppmÀtta körningar av Claude Opus 4 pÄ Railwail för att ange en körningstid. Det beror pÄ inmatningen, instÀllningarna och belastningen hos leverantören.

Är Claude Opus 4 bĂ€ttre Ă€n Claude Opus 4.8?

Det beror pÄ uppgiften. Claude Opus 4 (Anthropic) och Claude Opus 4.8 (Anthropic) Àr bÄda modeller i kategorin Text & chatt. JÀmförelsesidan visar deras priser och specifikationer sida vid sida.

JÀmför Claude Opus 4 och Claude Opus 4.8

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