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Anthropic: Claude Opus 4.6 (batch)

Opus 4.6 is Anthropic’s strongest model for coding and long-running professional tasks. It is built for agents that operate across entire workflows rather than single prompts, making it especially effective...

Anyone in the Project can @-mention Anthropic: Claude Opus 4.6 (batch) with the team's shared context - pooled credits, one chat, one memory.

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Verdict

Claude Opus 4.6 batch is Anthropic's flagship reasoning model optimized for asynchronous workloads where you can wait 12-24 hours for results. It delivers the same capability as real-time Opus 4.6 at half the cost — $2.50 input versus $5.00. Reach for this when you're processing large document sets, running evals overnight, or generating training data where latency doesn't matter. The trade-off is pure: you sacrifice immediacy for budget. If your workflow can tolerate batch delays, this is the most cost-effective way to access Anthropic's strongest reasoning.

Best for

Overnight document analysis pipelines
Large-scale synthetic data generation
Batch evaluation of model outputs
Cost-sensitive long-context summarization
Asynchronous research report drafting

Strengths

The million-token context window handles entire codebases or multi-document research corpora in one pass. Multimodal support means you can batch-process screenshots, diagrams, and PDFs alongside text. At $2.50 input, this is half the cost of real-time Opus while maintaining identical reasoning quality — critical when you're processing thousands of documents or running repeated evals. The batch API accepts up to 10,000 requests per submission, making it practical for production-scale workflows.

Trade-offs

Batch jobs complete in 12-24 hours, which rules out interactive use cases entirely. You lose the ability to stream responses or adjust mid-generation. If a prompt fails, you wait hours to retry. The output cost of $12.50/Mtok remains unchanged from real-time Opus, so savings only apply to input-heavy workloads. For anything requiring human-in-the-loop feedback or sub-minute turnaround, you'll need the real-time API despite the 2x cost premium.

Specifications

Provider: anthropic
Category: llm
Context length: 1,000,000 tokens
Max output: 128,000 tokens
Modalities: text, image, file
License: proprietary
Released: 2026-02-04

Pricing

Input: $2.50/Mtok
Output: $12.50/Mtok
Model ID: anthropic/claude-opus-4.6: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

Batch Code Review

Review this codebase for security vulnerabilities, code smells, and architectural anti-patterns. For each issue found, cite the file path, line numbers, severity level, and a specific remediation recommendation.

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Multi-Document Synthesis

I've provided 40 research papers on climate adaptation strategies. Extract the five most-cited interventions, their reported effectiveness ranges, and any contradictory findings across studies. Structure your response as a comparative table.

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Training Data Generation

Generate 50 diverse customer support scenarios for a SaaS billing system. For each scenario, include the customer question, relevant account context, and an ideal support response. Vary complexity from simple password resets to complex invoice disputes.

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Compliance Document Analysis

Extract all material obligations, deadlines, and penalty clauses from this 200-page contract. For each obligation, specify the responsible party, deliverable, due date, and consequence of non-compliance. Output as structured JSON.

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Batch Image Annotation

For each screenshot provided, identify: 1) the application or website shown, 2) the primary user task being performed, 3) any UI errors or accessibility issues visible, 4) suggested improvements. Format as one JSON object per image.

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