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OpenAI: GPT-5 (batch)

GPT-5 is OpenAI’s most advanced model, offering major improvements in reasoning, code quality, and user experience. It is optimized for complex tasks that require step-by-step reasoning, instruction following, and accuracy...

Anyone in the Project can @-mention OpenAI: GPT-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

GPT-5 batch delivers OpenAI's latest reasoning at 75% off standard pricing, making it viable for high-volume document processing and async analysis jobs. The 400K context window handles book-length inputs, but batch mode adds 24-hour latency — requests queue and return when capacity allows. Reach for this when you're processing thousands of documents overnight and cost matters more than real-time response.

Best for

Overnight document classification at scale
Cost-sensitive long-context summarization
Bulk image analysis for datasets
Async code review across repositories
Large-scale content moderation queues

Strengths

The 75% discount over standard GPT-5 makes this the most cost-effective way to access OpenAI's latest model for workloads that tolerate latency. The 400K context window matches the synchronous version, so you can process entire codebases or research papers in a single call. File and image modalities work in batch mode, enabling mixed-media analysis at volume. At $0.63/Mtok input, it undercuts most frontier models by 3-5x on large jobs.

Trade-offs

Batch requests sit in a queue for up to 24 hours before processing starts, making this unusable for interactive workflows or time-sensitive tasks. You lose streaming responses and can't adjust mid-flight if early output looks wrong. No public benchmarks exist yet for GPT-5, so performance claims rely on OpenAI's internal evals. If your use case needs sub-minute turnaround or conversational back-and-forth, standard GPT-5 or Claude Sonnet 4.5 will serve better despite higher cost.

Specifications

Provider: openai
Category: llm
Context length: 400,000 tokens
Max output: 128,000 tokens
Modalities: text, image, file
License: proprietary
Released: 2025-08-07

Pricing

Input: $0.63/Mtok
Output: $5.00/Mtok
Model ID: openai/gpt-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

$34.10

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 email and classify it into one of these categories: billing, technical, feature request, or account access. Also rate urgency as low, medium, or high. Return JSON with 'category' and 'urgency' fields.

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Summarize Research Papers

Summarize this research paper in 200 words. Focus on the core hypothesis, methodology, key results, and limitations. Write for a technical audience familiar with the field.

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Extract Invoice Data

Extract the following fields from this invoice image: vendor name, invoice number, date, line items with quantities and prices, subtotal, tax, and total. Return as JSON.

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Generate Product Descriptions

Write a 100-word product description for this item based on the specs and image provided. Highlight key features, use cases, and benefits. Optimize for search while keeping tone conversational.

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Code Review Across Repos

Review this code for security vulnerabilities, focusing on SQL injection, XSS, and authentication bypasses. For each issue found, cite the line number and suggest a fix.

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