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OpenAI: GPT-5.2 (batch)

GPT-5.2 is the latest frontier-grade model in the GPT-5 series, offering stronger agentic and long context perfomance compared to GPT-5.1. It uses adaptive reasoning to allocate computation dynamically, responding quickly...

Anyone in the Project can @-mention OpenAI: GPT-5.2 (batch) with the team's shared context - pooled credits, one chat, one memory.

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Verdict

GPT-5.2 batch is OpenAI's async processing variant optimized for high-volume, non-interactive workloads where 50-hour turnaround is acceptable. You get 50% cost savings versus real-time GPT-5.2 ($0.88/$7.00 vs $1.75/$14.00 per Mtok) in exchange for delayed results. The 400K context window handles book-length documents and large codebases. Reach for this when you're processing datasets, generating synthetic training data, or running evals overnight—skip it for anything user-facing or time-sensitive.

Best for

Overnight dataset labeling and classification
Bulk document summarization pipelines
Synthetic training data generation
Large-scale code analysis and refactoring
Batch evaluation runs for model testing

Strengths

The 50% discount makes this the most cost-effective way to access GPT-5.2's reasoning capabilities when latency doesn't matter. The 400K context window lets you process entire codebases or technical manuals in a single call without chunking. File and image support means you can queue mixed-media jobs—PDFs, screenshots, CSVs—without preprocessing. For teams running nightly pipelines or generating synthetic data at scale, the economics are compelling.

Trade-offs

The 50-hour maximum turnaround makes this unusable for interactive workflows or anything user-facing. You lose streaming entirely, so debugging long-running jobs requires waiting for full completion. OpenAI provides no SLA on actual completion time within that 50-hour window, so critical-path workloads are risky. If your batch fails partway through, you're resubmitting from scratch. Teams needing sub-hour turnaround should pay full price for real-time GPT-5.2 instead.

Specifications

Provider: openai
Category: llm
Context length: 400,000 tokens
Max output: 128,000 tokens
Modalities: file, image, text
License: proprietary
Released: 2025-12-10

Pricing

Input: $0.88/Mtok
Output: $7.00/Mtok
Model ID: openai/gpt-5.2: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

$47.74

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

Label Customer Support Tickets

Classify this support ticket into one of these categories: [billing, technical, feature_request, bug_report]. Also assign urgency: [low, medium, high, critical]. Return JSON with 'category', 'urgency', and 'reasoning' fields.

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Generate Synthetic QA Pairs

Read this technical documentation and generate 50 diverse question-answer pairs that test understanding at different difficulty levels. Include edge cases and common misconceptions. Format as JSON array with 'question', 'answer', and 'difficulty' fields.

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Refactor Legacy Codebase

Review this codebase and identify opportunities to refactor for better maintainability. Focus on reducing duplication, improving naming, and modernizing patterns. Provide specific file paths, line numbers, and refactored code snippets.

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Summarize Research Papers

Summarize this research paper in 300 words. Include: main hypothesis, methodology, key findings, limitations, and implications for practitioners. Use clear section headers.

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Extract Structured Data from Invoices

Extract all line items from this invoice image. Return JSON with vendor name, invoice number, date, line items (description, quantity, unit price, total), subtotal, tax, and grand total. Handle missing fields gracefully.

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