LLManthropic

Anthropic: Claude Haiku 4.5 (batch)

Claude Haiku 4.5 is Anthropic’s fastest and most efficient model, delivering near-frontier intelligence at a fraction of the cost and latency of larger Claude models. Matching Claude Sonnet 4’s performance...

Anyone in the Project can @-mention Anthropic: Claude Haiku 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 Haiku 4.5 batch delivers the same intelligence as the real-time version at 50% off — $0.50/Mtok input, $2.50/Mtok output — in exchange for up to 24-hour latency. The 200K context window handles full codebases and long documents without chunking. This is the model to reach for when you're processing large volumes asynchronously: nightly data pipelines, batch content moderation, or bulk document classification. If you need answers in seconds, pay full price for real-time Haiku; if you can wait hours, this cuts your bill in half.

Best for

Overnight batch processing pipelines
High-volume content moderation queues
Bulk document classification tasks
Cost-sensitive code review automation
Large-scale data extraction jobs

Strengths

The 50% cost reduction over real-time Haiku makes this the most economical way to access Claude's reasoning on high-volume workloads. The 200K context window means you can feed entire repositories or multi-document sets in a single call, eliminating chunking logic. Batch mode guarantees throughput without rate-limit headaches — submit thousands of jobs and let Anthropic handle scheduling. Vision support (images, PDFs) extends the savings to multimodal tasks like receipt processing or diagram analysis at scale.

Trade-offs

Latency ranges from minutes to 24 hours depending on queue depth, so this is unusable for user-facing features or real-time workflows. You sacrifice the interactivity of streaming responses — jobs complete atomically or fail. Anthropic provides no SLA on turnaround time, so mission-critical overnight jobs need fallback plans. The model itself matches real-time Haiku's capabilities, which means it trails Sonnet and Opus on complex reasoning tasks where speed isn't the constraint.

Specifications

Provider: anthropic
Category: llm
Context length: 200,000 tokens
Max output: 64,000 tokens
Modalities: text, image, file
License: proprietary
Released: 2025-10-15

Pricing

Input: $0.50/Mtok
Output: $2.50/Mtok
Model ID: anthropic/claude-haiku-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

$19.36

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

Classify Support Tickets

Read this support ticket and classify it into one of these categories: [Billing, Technical, Feature Request, Bug Report]. Also assign an urgency level: [Low, Medium, High, Critical]. Return your answer as JSON with 'category' and 'urgency' fields.

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Extract Invoice Data

Extract the following fields from this invoice image: vendor name, invoice number, date, total amount, line items (description and price). Return the data as JSON. If any field is missing or unclear, set its value to null.

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Summarize Research Papers

Read this research paper and write a 150-word summary covering: (1) the main research question, (2) the methodology, (3) key findings, and (4) practical implications. Use plain language accessible to non-specialists.

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Moderate User Content

Review this user-submitted content (text and any attached images) for policy violations: hate speech, graphic violence, spam, or sexually explicit material. Return a JSON object with 'violates_policy' (boolean), 'violation_type' (string or null), and 'confidence' (low/medium/high).

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Enrich CRM Records

Given this partial company record, infer and fill in missing fields based on the provided information: industry, employee count range, headquarters location, and primary business focus. Return JSON with your best estimates and a 'confidence' score (0-100) for each field.

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