How to Choose Between GPT-5.4 Mini and Nano for Coding Agents and High-Volume API Tasks

OpenAI’s new GPT-5.4 mini and GPT-5.4 nano give you two smaller GPT-5.4 options for coding agents, tool-using workflows, and high-volume API tasks. This guide shows how to choose between them for subagents, coding assistants, and background workers, using the March 17 release details from the official announcement and OpenAI’s current API pricing page.

The short version is simple. Choose GPT-5.4 mini when your agent needs strong coding performance, multimodal inputs, computer use, or tool-heavy workflows. Choose GPT-5.4 nano when throughput and low cost matter more than top-end reasoning, especially for classification, extraction, ranking, and narrow coding subtasks.

What changed in the March 17 release

OpenAI released GPT-5.4 mini and GPT-5.4 nano on March 17, 2026. According to the announcement, mini launched in the API, Codex, and ChatGPT, while nano launched API-only.

OpenAI positions mini as a fast model for responsive coding assistants and parallel subagents. The announcement says it is more than 2× faster than GPT-5 mini and improves on GPT-5 mini across coding, reasoning, multimodal understanding, and tool use.

That release context matters because these models fit a common agent architecture pattern. A larger model plans and reviews, while smaller workers execute scoped tasks in parallel. If you are building that pattern, this launch is directly relevant. For a broader design discussion, see Multi-Agent Systems Explained: When One Agent Isn’t Enough and AI Agent Frameworks Compared: LangChain vs CrewAI vs LlamaIndex.

The practical decision: mini vs nano

Start with the task, not the model name.

If your workload includes coding, screenshot interpretation, computer use, file search, web search, or tool calling with multiple steps, GPT-5.4 mini is the safer default. OpenAI explicitly lists those as target use cases and says mini supports text input, image input, tool use, function calling, web search, file search, computer use, skills, plus a 400k context window.

If your workload is a high-volume background task such as classification, extraction, ranking, or lightweight coding support, GPT-5.4 nano is the cost-first option. The launch article positions nano as the lowest-cost, speed-first model for simpler agentic subtasks.

Benchmark differences that matter for real workloads

OpenAI’s published numbers make the tradeoff clear.

Benchmark	GPT-5.4 mini	GPT-5.4 nano
SWE-Bench Pro (Public)	54.4%	52.4%
Terminal-Bench 2.0	60.0%	46.3%
Toolathlon	42.9%	35.5%
GPQA Diamond	88.0%	82.8%
OSWorld-Verified	72.1%	39.0%

Two rows matter most for agent builders.

Terminal-Bench 2.0 and OSWorld-Verified show a much larger gap than SWE-Bench Pro. That suggests mini is a better fit when your agent interacts with tools, terminals, or computer-use environments. Nano holds up better on simpler coding-style tasks than on computer-use benchmarks, but it drops sharply on OSWorld-Verified.

Use that pattern when routing traffic:

• Mini for code edits, debugging, terminal actions, and UI or screenshot reasoning
• Nano for triage, labeling, filtering, extraction, and parallel subtasks with tight budgets

Capability and availability differences

This is the most useful capability summary from the release materials.

Model	Availability	Best fit	Notable capabilities
GPT-5.4 mini	API, Codex, ChatGPT	Coding assistants, subagents, multimodal workflows, computer use	Text, image input, tool use, function calling, web search, file search, computer use, skills, 400k context
GPT-5.4 nano	API only	High-volume background API tasks	Lowest-cost option in the launch announcement

One rollout detail is especially relevant for teams using Codex. OpenAI says GPT-5.4 mini uses 30% of the GPT-5.4 quota in Codex, and it is available in the app, CLI, IDE extension, and web. That makes mini the practical delegated worker model for many coding tasks.

If your workflow depends on persistent tools and scoped capabilities, it is worth pairing this release with OpenAI’s agent features. See How to Build Stateful AI Agents with OpenAI’s Responses API Containers, Skills, and Shell and What Are Agent Skills and Why They Matter.

Pricing and cost planning

Pricing needs extra attention because OpenAI’s two official sources do not match on launch day.

The launch announcement lists:

Model	Input	Output
GPT-5.4 mini	$0.75 / 1M tokens	$4.50 / 1M tokens
GPT-5.4 nano	$0.20 / 1M tokens	$1.25 / 1M tokens

But OpenAI’s current pricing page shows a lower price for GPT-5.4 mini:

Model	Input	Cached input	Output	Notes
GPT-5.4 mini	$0.250 / 1M tokens	$0.025 / 1M tokens	$2.000 / 1M tokens	Standard processing for context lengths under 270K

The pricing page also says data residency / regional processing endpoints add 10% for all GPT-5.4 models.

The documentation retrieved for this post does not confirm a corresponding current pricing-page listing for GPT-5.4 nano, so the launch article is the verified source for nano pricing at release time.

For budgeting, use this rule:

• If you need a firm current number for mini, use the pricing page
• If you are evaluating nano, use the launch article price until OpenAI’s pricing page clearly lists it

Choosing the right model by workload

The easiest way to pick is to map model choice to failure cost.

Workload	Recommended model	Why
Code generation and code fixes	GPT-5.4 mini	Better coding and terminal benchmark performance
Tool-using agents	GPT-5.4 mini	Stronger Toolathlon score
Screenshot or UI interpretation	GPT-5.4 mini	OpenAI explicitly targets screenshot interpretation and multimodal reasoning
Computer-use workflows	GPT-5.4 mini	Large OSWorld-Verified advantage
Triage and routing	GPT-5.4 nano	Cost and speed matter more than deep reasoning
Classification and extraction	GPT-5.4 nano	Good fit for narrow, repeated subtasks
Ranking and filtering pipelines	GPT-5.4 nano	Lower-cost high-volume worker
Parallel subagents with strict task scopes	Mini or nano	Use mini for complex subtasks, nano for simple ones

A useful production pattern is tiered routing. Send everything to nano first when the task is simple and bounded. Escalate to mini when the request includes code changes, multiple tools, screenshots, or higher-value actions. That approach matches the broader idea behind Context Engineering: The Most Important AI Skill in 2026, where task framing and routing often matter as much as the base model.

How to structure your agent around mini and nano

The launch materials point to a clear architecture.

Use a stronger planner or reviewer model for decomposition and validation. Then delegate narrower execution tasks to GPT-5.4 mini or GPT-5.4 nano in parallel. OpenAI links this directly to Codex subagent orchestration, where subagent workflows are enabled by default in current Codex releases and specialized agents can run in parallel and merge results.

A practical split looks like this:

1. Planner decides what needs to happen
2. Nano workers handle repetitive extraction, ranking, and classification
3. Mini workers handle coding, tool calling, terminal actions, and multimodal tasks
4. Reviewer validates outputs before taking external actions

That structure also reduces wasted tokens. You reserve higher-capability calls for the steps that need them.

Configuration guidance

The release materials provide capability and pricing details, but they do not include a verified API request example for these new models in the sources available here. For implementation details, refer to the official announcement and OpenAI’s developer documentation.

You should still make a few configuration decisions up front:

• Route image input and computer-use tasks to mini
• Keep nano prompts tightly scoped and schema-oriented for extraction or ranking work
• Watch context length costs, especially because the pricing page note for mini applies to standard processing under 270K, even though mini’s context window is 400k
• If you rely on cached prompts, factor in cached input pricing for mini from the pricing page

For structured extraction, pair narrow prompts with explicit output formatting rules. If your system depends on predictable fields, Structured Output from LLMs: JSON Mode Explained is the right companion read.

Limitations and tradeoffs

Mini is the stronger general-purpose worker, but it is still a tradeoff against full GPT-5.4. OpenAI’s own benchmark table shows mini trailing the flagship on every reported task, even when it stays close on SWE-Bench Pro and OSWorld-Verified.

Nano is cheaper, but the benchmark drop is not uniform. It is relatively close to mini on SWE-Bench Pro, then much weaker on Terminal-Bench 2.0 and especially OSWorld-Verified. That means nano is better treated as a narrow worker than a general coding copilot.

There is also a safety-related operational note. OpenAI’s March 17 safety appendix says GPT-5.4 mini has lower chain-of-thought controllability than any previous model they reported controllability for. The appendix also reports that mini is not classified as “Bio High” under OpenAI’s current novice-uplift criterion. If you are deploying coding agents in sensitive environments, keep the controllability note in mind and add stronger review steps for risky tool actions. Related concerns around agent security are covered in OpenAI Details New ChatGPT Agent Defenses Against Prompt Injection.

When to standardize on one model

Pick GPT-5.4 mini as your default if your product is a coding assistant, IDE helper, terminal agent, or multimodal operator. The performance gap over nano is large enough on tool and computer-use tasks that a single-model standard can simplify routing.

Pick GPT-5.4 nano as your default if you are running a pipeline with large request volume and low per-task value, especially for extraction, routing, or ranking.

Use both if your system already separates planning from execution or if you are building multi-agent workflows. That is the highest-leverage setup for cost control.

Start by routing your repetitive background subtasks to nano and your coding or tool-using steps to mini. Then compare cost, latency, and task success over a week before deciding whether to widen nano’s scope or standardize on mini.