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Anthropic: Claude Opus 4.5 (batch)

Claude Opus 4.5 is Anthropic’s frontier reasoning model optimized for complex software engineering, agentic workflows, and long-horizon computer use. It offers strong multimodal capabilities, competitive performance across real-world coding and...

Anyone in the Project can @-mention Anthropic: Claude Opus 4.5 (batch) with the team's shared context - pooled credits, one chat, one memory.

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Verdict

Claude Opus 4.5 batch delivers Anthropic's most capable reasoning at half the cost of real-time Opus, trading speed for economics on large-scale jobs. With a 200K context window and multimodal support, it handles complex document analysis, code review across repositories, and vision tasks where you can wait 12-24 hours for results. Reach for this when you need Opus-tier intelligence on hundreds or thousands of prompts and latency doesn't matter.

Best for

Batch processing large document sets
Overnight code review across repos
Cost-sensitive multimodal analysis
Research tasks with flexible deadlines
High-volume content moderation

Strengths

Opus 4.5 batch inherits the full reasoning capability of real-time Opus — Anthropic's flagship model — while cutting per-token costs by 50%. The 200K context window lets you feed entire codebases or multi-document research packets in a single call. Multimodal support means you can mix screenshots, diagrams, and text without stitching tools. For teams running nightly pipelines or processing user-generated content at scale, the economics shift dramatically compared to synchronous inference.

Trade-offs

Batch jobs complete in 12-24 hours, making this unsuitable for interactive workflows or user-facing features. You lose the ability to stream responses or adjust mid-generation. Anthropic hasn't published Opus 4.5 benchmarks yet, so direct performance comparisons to GPT-4o or Gemini 1.5 Pro remain anecdotal. If your use case needs sub-second latency or real-time feedback loops, you're forced back to the real-time tier at double the cost.

Specifications

Provider: anthropic
Category: llm
Context length: 200,000 tokens
Max output: 64,000 tokens
Modalities: file, image, text
License: proprietary
Released: 2025-11-24

Pricing

Input: $2.50/Mtok
Output: $12.50/Mtok
Model ID: anthropic/claude-opus-4.5: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

Multi-Document Research Synthesis

I've attached 12 research papers on transformer architectures. Extract the three most-cited optimization techniques across all papers, then summarize each technique in two paragraphs with specific citation to which papers discuss it. Output as a markdown table with columns: Technique, Description, Papers.

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Codebase Security Audit

Audit this repository for SQL injection risks, hardcoded credentials, and insecure deserialization patterns. For each finding, provide the file path, line number, severity (critical/high/medium), and a two-sentence remediation recommendation. Output as JSON array.

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Screenshot UI Feedback

I've attached 8 screenshots of our onboarding flow. Identify any UI inconsistencies (button styles, spacing, typography) and accessibility issues (contrast, touch target size). For each issue, reference the specific screenshot and suggest a fix in one sentence.

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Legal Contract Comparison

Compare these five vendor contracts and identify any clauses where our liability exceeds $1M, where termination notice differs from our standard 30 days, or where IP ownership is ambiguous. Output a table with columns: Vendor, Clause Type, Our Risk, Recommendation.

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Batch Content Moderation

Classify this user-generated post for policy violations: hate speech, graphic violence, self-harm, spam, or none. If a violation exists, quote the specific phrase and explain why it violates policy in one sentence. Output as JSON with fields: classification, violating_phrase, reasoning.

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