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Anthropic: Claude Opus 4.1 (batch)

Claude Opus 4.1 is an updated version of Anthropic’s flagship model, offering improved performance in coding, reasoning, and agentic tasks. It achieves 74.5% on SWE-bench Verified and shows notable gains...

Anyone in the Project can @-mention Anthropic: Claude Opus 4.1 (batch) with the team's shared context - pooled credits, one chat, one memory.

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Verdict

Claude Opus 4.1 batch is Anthropic's flagship reasoning model optimized for asynchronous workloads where you can tolerate 12-24 hour turnaround. It delivers Opus-tier intelligence—superior to Sonnet on complex analysis, multi-step reasoning, and nuanced writing—at 50% off standard pricing. Reach for this when you're processing large document sets, running nightly data pipelines, or generating long-form content where same-hour response isn't critical. If you need answers in seconds, pay full price for realtime Opus; if you can wait, this is the sharpest tool per dollar.

Best for

Overnight document analysis pipelines
Batch content generation at scale
Cost-sensitive research summarization
Asynchronous code review workflows
Multi-document synthesis jobs

Strengths

Inherits Opus 4.1's reasoning depth—strongest in Anthropic's lineup for tasks requiring multi-step logic, handling ambiguity, and maintaining coherence across 200K token contexts. The 50% cost reduction makes it viable for high-volume jobs that would bankrupt a team on realtime pricing: think processing hundreds of PDFs nightly or generating thousands of marketing variants. Image and file handling match realtime Opus, so you can feed it screenshots, charts, and mixed-media documents without capability loss.

Trade-offs

Batch API means 12-24 hour latency—non-negotiable. You submit jobs, they queue, results arrive later. This kills any interactive use case: no chatbots, no live debugging sessions, no rapid iteration. You also lose streaming responses, so you can't watch output arrive token-by-token. For teams without batch infrastructure or patience, the cost savings evaporate against the operational friction. If your workflow depends on sub-minute feedback loops, standard Opus or Sonnet 4.5 are the only options despite higher per-token costs.

Specifications

Provider: anthropic
Category: llm
Context length: 200,000 tokens
Max output: 32,000 tokens
Modalities: image, text, file
License: proprietary
Released: 2025-08-05

Pricing

Input: $7.50/Mtok
Output: $37.50/Mtok
Model ID: anthropic/claude-opus-4.1: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

$290.40

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 Synthesis

Review these five research papers and produce a 2-page executive summary identifying consensus findings, contradictions, and gaps in the literature. Cite specific papers by author and year.

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Batch Content Variants

Given this product description and brand voice guide, write 50 unique ad headlines (10-12 words each) that emphasize different benefits. Maintain tone consistency across all variants.

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Code Architecture Review

Examine this 15-file Python module for architectural anti-patterns, tight coupling, and violation of SOLID principles. Provide a prioritized refactoring roadmap with code examples.

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Legal Document Comparison

Compare these two 80-page contracts and identify every substantive change: added clauses, deleted terms, modified definitions, and shifted liability. Output a redline summary table.

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Dataset Annotation Pipeline

Classify each customer support ticket into primary issue (billing, technical, account), sentiment (positive, neutral, negative, escalated), and urgency (low, medium, high, critical). Explain ambiguous cases.

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