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OpenAI: GPT-5.4 Nano (batch)

GPT-5.4 nano is the most lightweight and cost-efficient variant of the GPT-5.4 family, optimized for speed-critical and high-volume tasks. It supports text and image inputs and is designed for low-latency...

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

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Verdict

GPT-5.4 Nano (batch) delivers OpenAI's latest reasoning architecture at aggressive batch pricing — $0.10/Mtok input makes it viable for high-volume document processing and async workflows. The 400K context window handles book-length inputs comfortably. Trade-off: batch-only means no real-time responses, and at $0.63/Mtok output it's pricier than competitors for generation-heavy tasks. Reach for this when you're processing large corpora overnight and need GPT-5-class reasoning without the real-time tax.

Best for

Overnight batch processing of large document sets
Cost-sensitive long-context analysis tasks
Async workflows with 400K token inputs
High-volume content moderation pipelines
Research corpus summarization at scale

Strengths

The 400K context window puts entire codebases or multi-chapter documents in a single call, eliminating chunking overhead. Input pricing at $0.10/Mtok undercuts most GPT-4-class models by 5-10x, making it economical for large-scale ingestion. Batch mode forces you into async patterns that scale better than real-time queuing. The GPT-5 architecture brings improved reasoning over GPT-4 Turbo, particularly on multi-step logic and instruction-following in long contexts.

Trade-offs

Batch-only processing means latency measured in minutes or hours, not seconds — unusable for interactive applications. Output pricing at $0.63/Mtok climbs quickly on generation-heavy tasks; a 10K-token summary costs more here than with Claude Sonnet 4.5. No public benchmarks yet means you're flying blind on specific capability gaps versus Gemini 2.0 Flash or Llama 3.3 70B. Vision and file support are present but unproven in real-world batch scenarios.

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: $0.10/Mtok
Output: $0.63/Mtok
Model ID: openai/gpt-5.4-nano: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

$4.53

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

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Starter prompts

Multi-Document Synthesis

You have been provided with 23 research papers on transformer architecture efficiency. Synthesize the key findings into a structured report with: (1) consensus techniques, (2) conflicting results, (3) gaps in current research. Cite paper titles when referencing specific claims.

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Codebase Security Audit

Review this codebase for security vulnerabilities. Focus on: SQL injection risks, authentication bypasses, insecure data handling, and hardcoded secrets. For each finding, provide file path, line number, severity rating, and remediation code snippet.

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Legal Contract Comparison

Compare these five SaaS vendor contracts. Create a table showing: pricing structure, liability caps, data ownership terms, termination clauses, and auto-renewal policies. Flag any unusual or unfavorable terms for our organization.

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Customer Feedback Clustering

Analyze these 2,000 customer support tickets. Cluster feedback into themes, rank by frequency, and identify: (1) top 5 pain points, (2) feature requests mentioned 10+ times, (3) urgent bugs requiring immediate attention. Provide example ticket IDs for each theme.

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Historical Data Summarization

Summarize 18 months of engineering team meeting notes. Extract: (1) recurring blockers that were never resolved, (2) decisions that were later reversed, (3) technical debt acknowledged but not addressed. Organize chronologically with month labels.

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