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OpenAI: GPT-5.4 Mini (batch)

GPT-5.4 mini brings the core capabilities of GPT-5.4 to a faster, more efficient model optimized for high-throughput workloads. It supports text and image inputs with strong performance across reasoning, coding,...

Anyone in the Project can @-mention OpenAI: GPT-5.4 Mini (batch) with the team's shared context - pooled credits, one chat, one memory.

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Verdict

GPT-5.4 Mini (batch) offers OpenAI's latest small model architecture with a massive 400K token context window at batch-optimized pricing ($0.38/$2.25 per Mtok). The batch API trades real-time response for 50% cost savings, making this ideal for overnight document processing, bulk classification, or scheduled report generation. Reach for this when you need GPT-5 series quality on large inputs without interactive latency requirements.

Best for

Overnight batch document processing
Bulk content classification jobs
Scheduled report generation workflows
Cost-sensitive long-context analysis
Non-interactive vision + text tasks

Strengths

The 400K context window handles entire codebases, long PDFs, or multi-document analysis in a single call. Batch pricing cuts costs in half versus real-time GPT-5.4 Mini while maintaining identical model quality. Multi-modal support (text, image, file) means you can process mixed-format datasets without preprocessing. The GPT-5 series architecture brings improved reasoning over GPT-4 generation models, particularly on multi-step tasks.

Trade-offs

Batch jobs complete within 24 hours but lack real-time response — unsuitable for interactive chat or user-facing features. No public benchmarks yet make it hard to compare reasoning quality against Claude 3.7 Sonnet or Gemini 2.0 Flash. Output pricing at $2.25/Mtok runs higher than some competitors for generation-heavy workloads. You're locked into OpenAI's infrastructure with no self-hosting option.

Specifications

Provider: openai
Category: llm
Context length: 400,000 tokens
Max output: 128,000 tokens
Modalities: file, image, text
License: proprietary
Released: 2026-03-17

Pricing

Input: $0.38/Mtok
Output: $2.25/Mtok
Model ID: openai/gpt-5.4-mini: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

$16.50

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

Quarterly Report Synthesis

Review these Q1-Q4 earnings reports and produce a 2-page executive summary highlighting revenue trends, margin changes, and forward guidance. Focus on year-over-year comparisons and flag any unusual variances.

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Codebase Documentation Audit

Scan this codebase and list all public functions lacking docstrings or inline comments. For each, suggest a one-sentence description based on the implementation. Prioritize user-facing APIs.

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Multi-Document Contract Review

Compare these five vendor contracts and create a table showing payment terms, liability caps, termination clauses, and renewal conditions. Highlight any non-standard provisions.

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Screenshot UI Feedback Batch

Review these 20 app screenshots and identify accessibility issues: missing alt text, low contrast ratios, touch targets under 44px, and unclear navigation patterns. Output a CSV with screen ID and issue list.

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Customer Feedback Categorization

Categorize these 500 support tickets into: Bug Report, Feature Request, Billing Question, How-To, or Other. For each, extract the core issue in 10 words or less. Output JSON with ticket ID, category, and summary.

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