What is Claude Opus 5?

Claude Opus 5 is Anthropic’s advanced Opus model designed for high-value coding, knowledge work, problem-solving, automation, scientific research, and everyday AI workflows. The model is positioned as a thoughtful and proactive system that approaches the frontier intelligence of Claude Fable 5 at half the price. Anthropic says Claude Opus 5 delivers greatly improved performance for the same cost as Opus 4.8, with base pricing of $5 per million input tokens and $25 per million output tokens. The model supports effort settings that allow customers to optimize for deeper intelligence or conserve tokens for faster and cheaper results. Claude Opus 5 performs especially well on software engineering evaluations, including tasks that require debugging, code generation, root-cause analysis, test creation, and multi-step implementation. It also shows strong results on knowledge work, business automation, computer use, novel problem solving, and research-heavy tasks. Anthropic highlights that Opus 5 is better at checking its own work, iterating until it succeeds, and building supporting tools when a task requires it. The model improves on Opus 4.8 across life sciences evaluations, including structural biology, organic chemistry, bioinformatics, molecular structure inference, and protein function tasks. Claude Opus 5 includes alignment and safety protections that aim to allow beneficial cybersecurity and biology use cases while restricting riskier exploit generation, penetration testing, and certain autonomous misuse scenarios. It is available on Claude Max as the default model, on Claude Pro as the strongest model, and through the Claude API as claude-opus-5, with a Fast mode that runs around 2.5 times the default speed.

Pricing

Price Starts At:
$5 per 1M tokens (input)
Price Overview:
$5 per million input tokens and $25 per million output tokens

Integrations

Offers API?:
Yes, Claude Opus 5 provides an API
All Claude Opus 5 Integrations

Screenshots and Video

Play Video Claude Opus 5 Screenshot 1 Claude Opus 5 Screenshot 2 Claude Opus 5 Screenshot 3

Company Facts

Company Name:
Anthropic
Date Founded:
2021
Company Location:
United States
Company Website:
claude.ai

Product Details

Deployment
SaaS
Training Options
Documentation Hub
Support
Web-Based Support

Product Details

Target Company Sizes
Individual
1-10
11-50
51-200
201-500
501-1000
1001-5000
5001-10000
10001+
Target Organization Types
Mid Size Business
Small Business
Enterprise
Freelance
Nonprofit
Government
Startup
Supported Languages
English

Claude Opus 5 Categories and Features

Large Language Models

AI Vision Models

AI Models

AI Coding Models

More Claude Opus 5 Categories

Claude Opus 5 Customer Reviews

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  • Reviewer Name: A Verified Reviewer
    Position: Developer
    Has used product for: Less than 6 months
    Uses the product: Daily
    Org Size (# of Employees): 100 - 499
    Feature Set
    Layout
    Ease Of Use
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    Opus 5 review

    Date: Jul 24 2026
    Summary

    Five stars from me. Claude Opus 5 looks like a serious upgrade for developers building coding agents, automation workflows, and AI-assisted engineering tools.

    It feels especially strong for teams that want near-frontier capability without paying the absolute top-tier price every time. For complex coding, long-running agent workflows, and professional software work, Claude Opus 5 is one of the models I would be most excited to build with.

    Positive

    Claude Opus 5 feels like a really strong model from a developer’s point of view. It is built for the kind of work I actually care about: complex coding, long-running agents, repo-level reasoning, debugging, and professional workflows that need more than a quick answer.

    I like that Anthropic is positioning it as a big improvement over Opus 4.8 while still making it more cost-effective than Fable 5. That matters a lot if you are building coding agents or internal dev tools where the model may run through a lot of tokens across planning, editing, testing, and iteration.

    The long-running agent focus is probably the biggest win. If a model can stay organized through multi-step tasks, recover from mistakes, and keep making progress without constant hand-holding, that is exactly what developers need from the next wave of AI coding tools.

    Negative

    The main downside is that I would still want to test it hard before trusting it with production code. Launch claims are useful, but real codebases are messy, full of edge cases, and usually much harder than polished benchmark tasks.

    It is also not the cheapest model in the world. Even if it is more affordable than Fable 5, developers will still need to route tasks carefully and avoid using a premium model for simple work that a faster or cheaper model could handle.

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