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Claude Opus 4.7

Claude Opus 4.7 is a hybrid reasoning model for coding agents, enterprise AI workflows, long-context analysis, and complex multi-step tasks.

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In Plain English

Claude Opus 4.7 is a hybrid reasoning model for coding agents, enterprise AI workflows, long-context analysis, and complex multi-step tasks.

OverviewFeaturesPricingUse CasesLimitationsFAQAlternatives

Overview

Claude Opus 4.7 is Anthropic's premium Opus model for AI agent builders, priced for API use at $15 per million input tokens and $75 per million output tokens, and built for difficult coding, long-context analysis, multimodal input, and complex multi-step workflow execution.

Anthropic announced Claude Opus 4.7 on April 16, 2026 and describes it as generally available, with improvements over Opus 4.6 for advanced software engineering, difficult coding tasks, long-running work, instruction following, verification, vision, professional documents, slides, and user-interface generation. The model is most relevant to teams building coding agents, code-review systems, document automation, research agents, and enterprise copilots that need stronger planning, instruction following, tool use, and long-context recall than cheaper general-purpose models provide.

The model's core cost profile is premium: Anthropic lists Opus-class Claude API pricing at $15 per million input tokens and $75 per million output tokens. Prompt caching and batch processing can reduce costs for eligible repeated-context or asynchronous workloads under Anthropic's published pricing rules, while Claude app subscriptions remain separate from direct API metering.

For product planning, Claude Opus 4.7 is best treated as an escalation model for difficult work rather than a default model for every request. It is a strong fit for large codebases, long documents, multi-document reasoning, technical review, and long-running agent workflows where quality improvements can justify higher token costs. It is less practical as the default for routine chat, short summarization, high-volume content generation, or simple extraction.

Teams adopting Claude Opus 4.7 should pair it with evaluations, routing, prompt caching, spending limits, and fallbacks so the premium model is used where it earns its cost. Anthropic's documentation and product pages are the source of truth for current model IDs, context limits, output limits, deployment channels, feature availability, and account eligibility.

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Key Features

Long-context workflows+

Claude Opus 4.7 is designed for long-context workflows where teams need to reason over large repositories, extensive documents, long chat histories, or multi-step agent state. Exact context and output limits should be verified in Anthropic's current model documentation before production planning.

Adaptive reasoning+

Claude Opus 4.7 is positioned for complex coding, agentic workflows, and enterprise tasks that benefit from stronger planning and validation. Teams should test how its reasoning behavior performs on their own workloads rather than relying on benchmark claims alone.

High-output agent workflows+

Claude Opus 4.7 can be used for long responses, agent runs, and document-heavy generation within Anthropic's current model limits. Teams should test practical latency, cost, and platform-specific request behavior before relying on large outputs in production workflows.

Multi-platform deployment+

Developers can use Claude Opus 4.7 through Anthropic-supported access paths where available. Exact model IDs, account entitlements, and deployment channels should be verified in Anthropic's current documentation for the target environment.

Cost controls for repeated and batch work+

Although Claude Opus 4.7 is a premium model, Anthropic supports cost-control mechanisms such as prompt caching and batch processing for eligible workloads. These controls matter for agent builders because large prompts, repeated repository context, and asynchronous document jobs can otherwise become expensive quickly.

Pricing Plans

Free

$0

  • ✓Claude chat access
  • ✓Web, iOS, Android, and desktop access listed in plan comparison
  • ✓Basic access to Claude features subject to usage limits
  • ✓Entry-level plan for trying Claude before upgrading

Pro

$20/month or $200/year

  • ✓Everything in Free
  • ✓More usage
  • ✓Access to Claude Code where available
  • ✓Additional Claude productivity features where available
  • ✓Projects to organize chats and documents
  • ✓Access to Research where available
  • ✓Ability to use more Claude models
  • ✓Claude integrations where available

Max

$100/month for Max 5x or $200/month for Max 20x

  • ✓Everything in Pro
  • ✓5x or 20x Pro capacity depending on Max tier
  • ✓Higher output limits for eligible tasks
  • ✓Early access to selected advanced Claude features
  • ✓Priority access at high traffic times

Team Standard Seat

$30/seat/month billed monthly or $25/seat/month billed annually; 5-seat minimum

  • ✓For teams
  • ✓Team access to Claude features
  • ✓More usage than individual entry-level access
  • ✓Team workspace access
  • ✓Designed for small groups and departments

Team Premium Seat

$150/seat/month billed monthly or annually

  • ✓For teams
  • ✓All Team Standard features
  • ✓More usage than standard seats
  • ✓Higher-capacity team access
  • ✓Designed for heavier Claude users inside teams

Enterprise

Custom enterprise pricing

  • ✓For large businesses operating at scale
  • ✓Enterprise Claude features
  • ✓Seat and usage-based pricing depending on contract
  • ✓Enterprise deployment and administration
  • ✓Designed for company-wide AI workflows

Claude Opus 4.7 API

$15 per 1M input tokens and $75 per 1M output tokens

  • ✓Premium Opus-class API access where available
  • ✓Long-context workflow support subject to current model limits
  • ✓Batch-processing support for eligible workloads
  • ✓Prompt caching for repeated context where eligible
  • ✓Prompt cache reads may reduce repeated-context costs where eligible
  • ✓Batch processing discounts may apply for eligible jobs
  • ✓Regional inference options where available
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Best Use Cases

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Senior engineering teams delegating difficult repository work, such as refactoring a large codebase, fixing a concurrency bug, writing tests, and validating the result across multiple files before handing the change back for review.

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AI-agent builders creating multi-tool workflows that need planning, memory, retries, and recovery from tool failures, such as a research agent that searches, reads documents, creates a spreadsheet, drafts slides, and reports missing data rather than inventing it.

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Enterprise document analysis teams working with long contracts, spreadsheets, decks, and knowledge bases where long-context support can keep source material, instructions, and prior analysis available in a single workflow.

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Financial, legal, or life-sciences teams that need careful reasoning over specialized documents, technical diagrams, patents, review tables, or ambiguous source material and are willing to pay premium token rates for higher accuracy.

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Developers building coding products, IDE agents, code-review systems, or CI/CD automation where a premium long-context Claude model can be used for harder task completion, tool reasoning, recall, and long-running execution.

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Teams using multiple model tiers where Claude Opus 4.7 acts as the escalation model for hard tasks after cheaper models such as Sonnet or Haiku handle drafting, classification, or lower-risk generation.

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Claude Opus 4.7 doesn't handle well:

  • ⚠Premium API pricing makes it costly for always-on, high-volume consumer chat or simple content generation.
  • ⚠Adaptive reasoning features may reduce the need to manually tune reasoning effort, but teams should confirm the current controls available for Claude Opus 4.7 in Anthropic's documentation.
  • ⚠Claude plan usage limits apply outside direct API metering, and plan availability can vary by workspace, seat type, and Anthropic policy changes.
  • ⚠Large context windows do not eliminate the need for retrieval design, prompt discipline, evals, or source citation checks in production systems.
  • ⚠The strongest public positioning is focused on coding, agents, enterprise documents, and multimodal reasoning; it is less clearly differentiated for simple classification, short answers, or low-latency transactional tasks.

Pros & Cons

✓ Pros

  • ✓Designed for long-context work, making it suitable for large codebases, long documents, and multi-session enterprise workflows that smaller-context models may struggle to keep in one request.
  • ✓Anthropic lists Opus as a premium model family, with cost controls such as prompt caching and batch processing that can help reduce repeated-context and asynchronous workload costs.
  • ✓Strong fit for coding-agent workflows where planning, tool use, code review, and multi-file reasoning are more important than lowest possible latency or token cost.
  • ✓Useful for enterprise deployments because Anthropic lists Claude access through API, Claude plans, and enterprise-oriented channels, though exact availability should be verified for each environment.
  • ✓Can support complex agent work, implementation plans, long reports, and document-heavy automation runs when configured within current model limits.
  • ✓Anthropic positions Claude Opus 4.7 for coding, agentic workflows, enterprise documents, professional content, vision, and multimodal reasoning; teams should still validate performance against their own tasks.

✗ Cons

  • ✗Output-token pricing is materially expensive for high-volume chat, summarization, or content-generation workloads where a cheaper Sonnet or Haiku model may be sufficient.
  • ✗Anthropic describes Opus models as best for demanding tasks where performance matters most, so Claude Opus 4.7 is not positioned as the fastest or cheapest model for simple automation.
  • ✗Teams should verify the current reasoning controls in Anthropic's model documentation because feature names, limits, and availability can vary by model and API surface.
  • ✗Claude plan access depends on usage limits, and Anthropic states that limits, prices, and plans are subject to change, which can complicate predictable budgeting for teams using Claude rather than direct API metering.
  • ✗Enterprise-grade value depends heavily on prompt engineering, tool integration, caching, and evaluation; the model can still be overkill if the task does not require long context, long-horizon planning, or frontier coding performance.

Frequently Asked Questions

What is Claude Opus 4.7 best used for?+

Claude Opus 4.7 is best used for demanding AI-agent and coding workflows: production-ready code generation, multi-tool agents, large-codebase reasoning, complex document work, and enterprise workflows that need long-context consistency. Anthropic positions Opus 4.7 for advanced software engineering, difficult coding tasks, long-running work, instruction following, verification, vision, professional documents, slides, and user-interface generation. Compared to the 870+ AI tools in our directory, it is better suited to high-stakes reasoning work than routine copywriting or simple summarization. If a task can be handled reliably by a cheaper model, Opus 4.7 is usually best reserved for escalation or final review.

How much does Claude Opus 4.7 cost?+

Anthropic's published Opus-class API pricing is $15 per million input tokens and $75 per million output tokens. Prompt caching and batch processing can reduce costs for eligible workloads under Anthropic's current pricing rules. Claude app subscriptions are separate from direct API metering, and teams should verify current plan pricing, usage limits, and seat requirements on Anthropic's official pricing and support pages before purchasing.

What are the context and output limits for Claude Opus 4.7?+

Claude Opus 4.7 is intended for long-context and complex-output workflows, but teams should verify the current context window, maximum output limit, and request constraints in Anthropic's model documentation before designing production systems around specific ceilings. Developers should still design retrieval, truncation, and token-counting safeguards because practical cost and latency can become limiting before theoretical maximum limits do.

How does Claude Opus 4.7 compare with Claude Sonnet 4.6?+

Claude Opus 4.7 is positioned for the hardest reasoning, coding, and agentic tasks, while Claude Sonnet 4.6 is typically the better speed-and-cost balance for routine coding, chat, and agent steps. Choose Opus when the task repeatedly fails on cheaper models or demands deep planning; choose Sonnet when latency, throughput, and cost matter more.

Can developers build products with Claude Opus 4.7?+

Yes. Developers can build products with Claude Opus 4.7 through Anthropic-supported API access where available, subject to current Anthropic model documentation and account eligibility. The model's premium reasoning profile makes it practical for coding agents, research agents, document automation, and enterprise copilots. Teams should still build evaluations, rate controls, prompt caching, and fallback routing because Opus 4.7 is a premium model.
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What's New in 2026

Anthropic announced Claude Opus 4.7 on April 16, 2026 as generally available and positioned it as its latest Opus model, with stated improvements over Opus 4.6 in advanced software engineering, difficult long-running tasks, instruction following, verification, higher-resolution vision, professional documents, slides, and interface generation. Teams should verify current model IDs, context limits, output limits, deployment channels, and feature availability in Anthropic's official Claude model documentation before making production architecture or procurement decisions.

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Quick Info

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Website

www.anthropic.com/claude/opus
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