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OpenAI: GPT-5.4 (batch)

GPT-5.4 is OpenAI’s latest frontier model, unifying the Codex and GPT lines into a single system. It features a 1M+ token context window (922K input, 128K output) with support for...

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

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Verdict

GPT-5.4 batch delivers OpenAI's latest reasoning at 50% off list price, trading real-time response for cost efficiency on large workloads. The 1M+ token context window handles book-length documents and massive codebases, while batch processing suits overnight analysis, bulk content generation, and scheduled data pipelines. Reach for this when you're processing hundreds of requests where 12-24 hour latency is acceptable and cost matters more than speed.

Best for

Overnight document analysis pipelines
Bulk content generation at scale
Cost-sensitive code review workflows
Scheduled data processing jobs
Large-batch synthetic data creation

Strengths

The 50% discount over synchronous GPT-5.4 makes this the most cost-effective access to OpenAI's current-generation reasoning. The 1.05M token context window swallows entire codebases or multi-chapter documents in a single call, eliminating chunking overhead. Multimodal support handles screenshots and PDFs natively. At $1.25/Mtok input, it undercuts Claude Sonnet 4.5 by 75% for batch workloads where overnight turnaround works.

Trade-offs

Batch processing introduces 12-24 hour latency, making this unsuitable for interactive applications or time-sensitive workflows. Without public benchmarks yet, performance relative to Claude Opus 4 or Gemini 2.0 Pro remains unverified in production scenarios. The $7.50/Mtok output cost climbs quickly on verbose generation tasks. Teams needing sub-second response times or real-time user-facing features should use the synchronous API despite the 2x cost premium.

Specifications

Provider: openai
Category: llm
Context length: 1,050,000 tokens
Max output: 128,000 tokens
Modalities: text, image, file
License: proprietary
Released: 2026-03-05

Pricing

Input: $1.25/Mtok
Output: $7.50/Mtok
Model ID: openai/gpt-5.4: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

$55.00

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

Codebase Security Audit

Review this codebase for security vulnerabilities. Focus on SQL injection risks, authentication bypasses, and exposed secrets. For each issue, cite the file path, line number, severity level, and recommended fix.

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Multi-Document Synthesis

Synthesize key findings from these research papers. Identify consensus views, contradictory claims, and gaps in the literature. Organize by theme and cite specific papers for each point.

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Bulk Product Descriptions

Write a 150-word product description for each item in this catalog. Include key features, benefits, and relevant keywords for search optimization. Maintain consistent tone across all entries.

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Contract Clause Extraction

Extract all liability, indemnification, and termination clauses from these contracts. For each clause, note the document name, section number, and any cross-references to other sections.

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Dataset Annotation Pipeline

Label each text sample with sentiment (positive/negative/neutral), topic category, and confidence score. Flag any ambiguous cases that need human review. Output as structured JSON.

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