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Anthropic: Claude Opus 4.8 (batch)

Claude Opus 4.8 is Anthropic's most capable generally available model in the Opus family. It supports text, image, and file inputs with text output, with reasoning support and a 1M-token...

Anyone in the Project can @-mention Anthropic: Claude Opus 4.8 (batch) with the team's shared context - pooled credits, one chat, one memory.

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Verdict

Claude Opus 4.8 batch is Anthropic's flagship reasoning model optimized for asynchronous workloads where you can wait 12-24 hours for results. It delivers Opus-tier intelligence at half the standard API cost ($2.50/$12.50 per Mtok vs $5/$25). Reach for this when you're processing large document sets, running evals overnight, or batch-generating training data where latency doesn't matter. If you need answers in seconds, use the real-time version instead.

Best for

Overnight document analysis pipelines
Large-scale evaluation runs
Training data generation at volume
Cost-sensitive research tasks
Batch processing legal or medical files

Strengths

The 1M token context window handles entire codebases or multi-document analysis in a single call. Native file and image support means you can feed PDFs, spreadsheets, and screenshots directly without preprocessing. The 50% cost reduction over real-time Opus makes it viable for tasks that would otherwise blow your budget — think processing 500 contracts or generating 10,000 synthetic examples for fine-tuning. Quality matches standard Opus 4.8, so you're trading latency for cost, not accuracy.

Trade-offs

Batch jobs take 12-24 hours to complete, which rules out interactive use cases entirely. You can't course-correct mid-run or get partial results early. The model is identical to real-time Opus 4.8, so it inherits the same weaknesses: slower than GPT-4o on pure speed tasks, and more expensive than Sonnet 4.5 when you don't need flagship reasoning. If your workload fits in Sonnet's capabilities, you'll save more money there even at real-time pricing.

Specifications

Provider: anthropic
Category: llm
Context length: 1,000,000 tokens
Max output: 128,000 tokens
Modalities: text, image, file
License: proprietary
Released: 2026-05-27

Pricing

Input: $2.50/Mtok
Output: $12.50/Mtok
Model ID: anthropic/claude-opus-4.8: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

Contract Clause Extraction

Extract all liability clauses, payment terms, and termination conditions from the attached contracts. For each document, return a structured JSON object with document_id, clause_type, exact_text, and page_number. Flag any non-standard or high-risk language.

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Codebase Documentation

Analyze this codebase and produce comprehensive documentation. Include: (1) high-level architecture diagram in Mermaid syntax, (2) API endpoint reference with request/response schemas, (3) data flow descriptions for the three most complex features. Assume the reader is a new engineer onboarding.

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Research Paper Synthesis

Compare the methodologies and key findings across these research papers. Create a synthesis table showing: study design, sample size, primary outcome measures, and effect sizes. Then write a 300-word summary identifying areas of consensus, contradictory results, and gaps in the literature.

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Training Data Generation

Generate 5,000 question-answer pairs for fine-tuning a customer support model in the SaaS billing domain. Vary complexity (simple account questions to multi-step refund scenarios), tone (frustrated to neutral), and specificity. Each answer should be 2-4 sentences and reference realistic product features.

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Medical Chart Review

Review these medical charts and extract: primary diagnosis, secondary diagnoses, all prescribed medications with dosages, treatment timeline, and any documented adverse events. Output as structured JSON. Flag charts with incomplete medication histories or conflicting diagnosis codes.

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