LLManthropic

Anthropic: Claude Sonnet 5 (batch)

Sonnet 5 is Anthropic's most capable Sonnet-class model, with frontier performance across coding, agents, and professional work. It supports adaptive thinking with selectable reasoning effort levels (low, medium, high, max,...

Anyone in the Project can @-mention Anthropic: Claude Sonnet 5 (batch) with the team's shared context - pooled credits, one chat, one memory.

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Starter is free forever - 1 Project, 100 credits/month, 1 MCP. No card.

Verdict

Claude Sonnet 5 batch delivers the same reasoning and multimodal capabilities as the real-time version at 50% lower cost, trading immediate responses for 24-hour processing windows. Best for teams running regular analysis jobs—document processing pipelines, bulk content moderation, overnight research synthesis—where latency doesn't matter but budget does. If you need answers in seconds, use the real-time API; if you can wait until morning, batch cuts your bill in half.

Best for

Overnight document analysis pipelines
Bulk content moderation workflows
Cost-sensitive research synthesis
Scheduled report generation
Large-scale data extraction jobs

Strengths

Inherits Claude Sonnet 5's strong reasoning and vision capabilities while cutting costs 50% on both input and output tokens. The 1M token context window handles book-length documents or hundreds of screenshots in a single job. Batch processing suits workflows that already run on schedules—nightly ETL pipelines, weekly content audits, monthly compliance reviews. You get the same model quality as real-time Sonnet 5, just delivered asynchronously.

Trade-offs

Results arrive within 24 hours, not seconds—unworkable for interactive use cases or user-facing features. You lose the ability to stream responses or adjust mid-conversation. Batch jobs can't be canceled once submitted, so prompt errors waste the full token budget. Teams without existing scheduled workflows will need to rethink their architecture to benefit. If your use case demands sub-minute latency or conversational back-and-forth, the cost savings evaporate because you'll need the real-time API anyway.

Specifications

Provider: anthropic
Category: llm
Context length: 1,000,000 tokens
Max output: 128,000 tokens
Modalities: text, image, file
License: proprietary
Released: 2026-06-30

Pricing

Input: $1.00/Mtok
Output: $5.00/Mtok
Model ID: anthropic/claude-sonnet-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

$38.72

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

Bulk Invoice Extraction

Extract the following fields from each attached invoice: vendor name, invoice number, date, line items with descriptions and amounts, subtotal, tax, and total. Return results as a JSON array with one object per invoice.

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Content Moderation Queue

Review each piece of content against our community guidelines. For each item, return: (1) violates_policy (true/false), (2) violation_type if applicable, (3) severity (low/medium/high), (4) recommended_action, and (5) brief explanation. Be consistent across all items.

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Research Paper Synthesis

Read all attached research papers and produce a synthesis document covering: main research questions, methodologies used, key findings, points of consensus, areas of disagreement, and gaps for future research. Organize by theme, not by paper.

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Screenshot Documentation

For each screenshot, describe: what UI element or feature is shown, its purpose, how a user would interact with it, and any visible state or configuration. Write in present tense for end-user documentation. Maintain consistent terminology across all descriptions.

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Contract Clause Analysis

Compare each contract against our standard template. Flag any clauses that deviate in: payment terms, liability caps, termination conditions, IP ownership, or confidentiality obligations. For each deviation, quote the specific language and explain how it differs from standard.

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