LLMopenaiPlan: Pro and up

OpenAI: GPT-5 Nano (batch)

GPT-5-Nano is the smallest and fastest variant in the GPT-5 system, optimized for developer tools, rapid interactions, and ultra-low latency environments. While limited in reasoning depth compared to its larger...

Anyone in the Project can @-mention OpenAI: GPT-5 Nano (batch) with the team's shared context - pooled credits, one chat, one memory.

All models

Verdict

GPT-5 Nano (batch) delivers GPT-5 reasoning at batch-optimized pricing — $0.03/Mtok input makes it the cheapest way to access OpenAI's latest architecture for high-volume workloads. The 400K context window handles long documents and multi-turn conversations without truncation. Trade-off: batch mode means 24-hour turnaround, so this isn't for real-time use. Reach for this when you're processing thousands of documents overnight or running evals where latency doesn't matter.

Best for

Overnight document processing pipelines
Large-scale dataset annotation jobs
Cost-sensitive research and evaluation
Batch summarization of support tickets
Bulk content moderation workflows

Strengths

At $0.03/Mtok input, this is OpenAI's most economical GPT-5 access point — roughly 80% cheaper than real-time variants. The 400K context window accommodates full codebases, long transcripts, or multi-document analysis without chunking. Inherits GPT-5's reasoning architecture, so you get the same logical coherence and instruction-following as the flagship model. Vision and file handling included, so you can batch-process PDFs or screenshots alongside text.

Trade-offs

Batch processing means 24-hour turnaround — you submit jobs and retrieve results the next day, making this unusable for interactive applications or user-facing features. No public benchmarks yet, so performance relative to GPT-4o or Claude remains unverified in third-party evals. Output pricing at $0.20/Mtok is standard for GPT-5 tier, so savings only materialize if your workload is input-heavy. If you need answers in seconds rather than hours, pay up for the real-time API.

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: $0.03/Mtok
Output: $0.20/Mtok
Model ID: openai/gpt-5-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

$1.36

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

Batch Ticket Categorization

Categorize this support ticket into one of these departments: Billing, Technical, Account, or Sales. Also assign urgency: Low, Medium, High, or Critical. Return JSON with 'department', 'urgency', and a one-sentence 'reason'.

Open in a Project →

Multi-Document Synthesis

You have 30 research papers on climate adaptation strategies. Identify the top 5 recurring themes, cite which papers support each theme, and note any major disagreements between authors. Format as a structured report.

Open in a Project →

Codebase Documentation

This repository contains 50 Python files. For each public function and class, generate a docstring that explains purpose, parameters, return values, and usage examples. Output as markdown organized by module.

Open in a Project →

Bulk Image Captioning

Write a descriptive, SEO-friendly caption for this product image. Include visible features, colors, materials, and potential use cases. Keep it under 150 characters for meta descriptions.

Open in a Project →

Dataset Labeling for ML

Label this text snippet with sentiment (positive, negative, neutral), topic tags (up to 3), and whether it contains personally identifiable information. Return structured JSON.

Open in a Project →

Compare with

Compare OpenAI: GPT-5 Nano (batch) with…

More language models

See all language models →