Claude Opus 4

Tekstas ir pokalbisNutrauktasNeprieinama
iš AnthropicModelio ID: claude-opus-4

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

Būsena
Neprieinama
Kontekstas
200 000 žetonų
Maks. išvestis
32 000 žetonų
Įvestis → išvestis
Tekstas → Tekstas
Kūrėjas
Anthropic
Atnaujinta
2026 m. rugsėjo 23 d.

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Galima naujesnė versija: Claude Opus 4.8

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Pokalbis

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Apie Claude Opus 4

TrumpaiNuo 2026 m. rugsėjo 23 d.

Claude Opus 4 yra Anthropic modelis iš Tekstas ir pokalbis kategorijos. Claude Opus 4 šiuo metu nėra prieinamas per Railwail. Konteksto langas saugo 200 000 žetonų, o vienas atsakymas gali būti iki 32 000 žetonų ilgio. Naujesnė versija: Claude Opus 4.8.

Fonas

Apie Anthropic

Įkurta 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.

Apsilankyti Anthropic

Architektūra

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.

Parametrai
Undisclosed (estimated multi-hundred billion parameters dense)
Kontekstas
200 000 žetonų

Galimybės

  • 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.

Mokymas ir licencija

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.

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

Saugumo testai: 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.

Žinomos ribotybės

  • 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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Kainos

Šiuo metu nepasiekiama. Šiuo metu šiam modeliui nėra nustatytos kainos, todėl jo negalima paleisti.

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API

Iškvieskite Claude Opus 4 naudodami savo Railwail API raktą. Šioje užklausoje naudokite šią modelio ID:

Šiuo metu nepasiekiama

Modelis neturi patikrintos kainos arba yra išjungtas; API skambučiai atmetami.

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Specifikacijos

Modelio ID
claude-opus-4
Kūrėjas
Anthropic
Įvestis
Tekstas
Išvestis
Tekstas
Konteksto langas
200 000 žetonų
Maks. išvestis
32 000 žetonų
Ciklas
Nutrauktas
Modelio dydis
Undisclosed (estimated multi-hundred billion parameters dense)
Licencija
Proprietary commercial license via Anthropic API, Amazon Bedrock and Google Vertex AI. Commercial use permitted under Anthropic's Usage Policy.
Katalogo įrašas atnaujintas
2026 m. rugsėjo 23 d.

Žymos

  • flagship
  • reasoning
  • agentic
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Pavyzdiniai užklausos tekstai

Pavyzdžiai iš Railwail katalogo. Jie nebuvo sugeneruoti tiesiogiai šiame puslapyje.
  • Deep Research

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

    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.
    Rodyti pavyzdinį atsakymą

    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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Panaudojimo atvejai

Kam tai naudojama

  • 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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Dažnai užduodami klausimai

Kas yra Claude Opus 4?

Claude Opus 4 yra Anthropic modelis iš Tekstas ir pokalbis kategorijos. Jis yra Railwail kataloge, tačiau šiuo metu negali būti paleistas.

Kiek kainuoja Claude Opus 4 Railwail?

Claude Opus 4 šiuo metu negali būti paleistas Railwail, todėl nėra dabartinės kainos. Toliau šiame puslapyje pateikiamos galimos alternatyvos su kainomis.

Koks yra Claude Opus 4 konteksto langas?

Claude Opus 4 konteksto langas saugo 200 000 žetonų. Vienas atsakymas gali būti iki 32 000 žetonų ilgio.

Koks yra Claude Opus 4 greitis?

Claude Opus 4 Railwail platformoje dar nėra pakankamai išmatuotų vykdymų, kad būtų galima nurodyti vykdymo laiką. Jis priklauso nuo įvesties, nustatymų ir teikėjo apkrovos.

Ar Claude Opus 4 geriau nei Claude Opus 4.8?

Tai priklauso nuo užduoties. Claude Opus 4 (Anthropic) ir Claude Opus 4.8 (Anthropic) yra abu modeliai iš Tekstas ir pokalbis kategorijos. Palyginimo puslapyje jų kainos ir specifikacijos rodomos viena šalia kitos.

Palyginti Claude Opus 4 ir Claude Opus 4.8

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