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Anthropic: Claude Opus 4.7 (batch)

Opus 4.7 is the next generation of Anthropic's Opus family, built for long-running, asynchronous agents. Building on the coding and agentic strengths of Opus 4.6, it delivers stronger performance on...

Anyone in the Project can @-mention Anthropic: Claude Opus 4.7 (batch) with the team's shared context - pooled credits, one chat, one memory.

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Verdict

Claude Opus 4.7 batch delivers Anthropic's flagship reasoning at half the cost when you can tolerate 24-hour turnaround. The 1M token context window handles entire codebases or document collections in a single pass, and vision support means you can throw screenshots or diagrams into the mix. Trade latency for budget: if your workflow can queue requests overnight—research synthesis, bulk content analysis, dataset labeling—this is the model to reach for. Real-time tasks need the standard API.

Best for

Overnight research synthesis across documents
Bulk content moderation with 24hr SLA
Codebase-wide refactoring analysis
Dataset labeling with vision inputs
Cost-sensitive long-context summarization

Strengths

The 1M token context window swallows entire repositories, legal briefs, or multi-document research packets without chunking. Vision support handles mixed-media inputs—screenshots, charts, diagrams—alongside text. At $2.50 input and $12.50 output per million tokens, batch pricing cuts standard Opus costs roughly in half. Anthropic's constitutional AI training shows up in nuanced reasoning tasks: the model handles ambiguous instructions and ethical edge cases better than most peers.

Trade-offs

Batch processing means 24-hour maximum turnaround, disqualifying any real-time or interactive use case. Without public benchmarks yet, you're flying blind on head-to-head performance against GPT-4o or Gemini 1.5 Pro—internal testing required. The $12.50 output cost still stings on generation-heavy tasks; if you're producing long-form content at scale, cheaper models may pencil out better even at lower quality. No streaming, no function calling in batch mode.

Specifications

Provider: anthropic
Category: llm
Context length: 1,000,000 tokens
Max output: 128,000 tokens
Modalities: text, image, file
License: proprietary
Released: 2026-04-16

Pricing

Input: $2.50/Mtok
Output: $12.50/Mtok
Model ID: anthropic/claude-opus-4.7:batch

Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool - one plan, one balance for everyone.

Team cost calculator

Seats5 peopleMessages / seat / day80Avg turn size2 ktokOutput share30 %

Estimated monthly spend

$96.80

17.6M tokens / month
5 seats · 80 msgs/day

Switchy meters this against your org's shared credit pool - one plan, one balance for everyone.

Providers

Provider-level routing data is not available yet for this model.

Performance

Performance snapshots are collected daily. Check back after the next ingestion run.

Benchmarks

Public benchmark scores are not available yet for this model. Check back after the next ingestion run.

Works well with

Top MCPs

Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.

How Switchy teams use it

Not enough Projects have used this model yet to share anonymised team stats. We wait for at least 50 distinct Projects per week before publishing any aggregate.

Starter prompts

Codebase Architecture Review

Review this codebase for architectural debt. Identify the three highest-impact refactoring opportunities, explain the current pain points, and sketch a migration path for each. Focus on maintainability and test coverage gaps.

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Multi-Document Research Synthesis

Synthesize these research papers into a 2-page executive summary. Highlight consensus findings, flag contradictions, and identify the three most promising areas for follow-up research. Use section headers for readability.

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Screenshot-Based UI Audit

Audit these UI screenshots for accessibility issues. Flag WCAG violations, note confusing navigation patterns, and suggest three concrete improvements for each screen. Prioritize changes by user impact.

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Legal Document Comparison

Compare these two contracts and produce a redline summary. Highlight material changes in liability, termination clauses, and payment terms. Flag any new obligations that increase our risk exposure.

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Bulk Content Moderation

Review these user submissions against our content policy. For each item, assign a risk level (low/medium/high), cite the specific policy section, and recommend approve/flag/remove. Explain edge cases in 1-2 sentences.

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