LLManthropic

Anthropic: Claude Sonnet 4.5 (batch)

Claude Sonnet 4.5 is Anthropic’s most advanced Sonnet model to date, optimized for real-world agents and coding workflows. It delivers state-of-the-art performance on coding benchmarks such as SWE-bench Verified, with...

Anyone in the Project can @-mention Anthropic: Claude Sonnet 4.5 (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 4.5 batch delivers the same reasoning quality as the real-time version at 50% off both input and output tokens. The catch: jobs queue for up to 24 hours, making this strictly for workloads where latency doesn't matter. If you're processing large document sets, running nightly analysis pipelines, or batch-scoring customer interactions, this is the most cost-effective way to access frontier reasoning. Skip it for anything user-facing or time-sensitive.

Best for

Overnight document processing pipelines
Large-scale content moderation queues
Batch analysis of customer support logs
Cost-sensitive research dataset labeling
Scheduled report generation workflows

Strengths

Inherits Sonnet 4.5's strong reasoning across code, analysis, and long-context tasks while cutting costs in half. The 1M token context window handles book-length documents or entire codebases in a single pass. Vision capabilities process screenshots and diagrams alongside text. Output quality matches the real-time API exactly — you're trading time for money, not accuracy. At $1.50 input and $7.50 output per million tokens, this undercuts GPT-4o batch pricing while maintaining competitive performance on reasoning benchmarks.

Trade-offs

Jobs can take up to 24 hours to complete, disqualifying this for interactive use cases or anything user-facing. You lose streaming responses and real-time feedback entirely. Batch jobs require upfront planning — you can't iterate quickly or adjust mid-flight. The 50% discount only matters if you're processing enough volume to notice; small teams running occasional queries won't see meaningful savings. No public benchmarks yet distinguish batch from real-time performance, though Anthropic claims identical output quality.

Specifications

Provider: anthropic
Category: llm
Context length: 1,000,000 tokens
Max output: 64,000 tokens
Modalities: text, image, file
License: proprietary
Released: 2025-09-29

Pricing

Input: $1.50/Mtok
Output: $7.50/Mtok
Model ID: anthropic/claude-sonnet-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

$58.08

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

Analyze Support Ticket Patterns

Review these 200 support tickets and identify the top 5 recurring technical issues. For each issue, provide the number of occurrences, common user pain points, and suggested resolution priority. Format as a table with columns: Issue, Count, User Impact, Priority.

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Score Content Moderation Queue

Evaluate this content against our community guidelines: no hate speech, no graphic violence, no spam. Assign a score from 1-10 where 1 is clear violation and 10 is fully compliant. Provide the score, the primary concern if any, and whether it requires human review.

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Extract Structured Data from Contracts

Extract the following from this contract: parties involved, effective date, termination date, payment terms, key obligations for each party, and any renewal clauses. Return as JSON with fields: parties (array), effectiveDate, terminationDate, paymentTerms (object), obligations (array of objects with party and description), renewalClause (string or null).

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Generate Product Descriptions at Scale

Write a 150-word product description for this item based on the provided specs. Tone: professional but approachable. Include key features, primary use case, and one benefit-focused sentence. Optimize for search terms related to [category]. End with a call-to-action.

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Summarize Research Papers for Database

Summarize this research paper in 200 words covering: research question, methodology, key findings, limitations, and practical implications. Use clear section headers. Focus on what practitioners need to know, not academic jargon.

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