GPT-5.4 Mini vs Nano: Pricing, Benchmarks, and Best Use Cases

OpenAI released GPT-5.4 mini and GPT-5.4 nano on March 17, 2026. The short version is simple: gpt-5.4-mini is the stronger small model for coding, computer use, and subagents, while gpt-5.4-nano is the cheaper high-throughput option for classification, ranking, extraction, and simpler supporting work.

If you are trying to choose between them for a real product, the decision is usually not about which model is "best" in the abstract. It is about which model gives you the best combination of latency, tool support, and cost for the exact job in front of you.

Quick Start

• Choose gpt-5.4-mini if the task involves coding, screenshot understanding, tool orchestration, or agent workflows that still need strong reasoning.

• Choose gpt-5.4-nano if the task is narrow, repeatable, and sensitive to throughput or API cost.

• As of March 20, 2026, official API pricing is $0.75 input and $4.50 output per 1M tokens for Mini, versus $0.20 input and $1.25 output for Nano.

• Both models support text and image input, have a 400,000 token context window, and allow up to 128,000 output tokens.

• If you are unsure, start with Mini for production-critical flows, then move narrowly scoped tasks down to Nano after you measure failure rate and rework cost.

What Actually Changed On March 17, 2026

OpenAI positions both releases as fast, efficient GPT-5.4-family models for high-volume product work, not as stripped-down curiosities.

The most important official takeaways are:

• OpenAI says gpt-5.4-mini improves over GPT-5 mini across coding, reasoning, multimodal understanding, and tool use while running more than 2x faster.

• OpenAI describes gpt-5.4-nano as the smallest and cheapest GPT-5.4 variant, and explicitly recommends it for classification, data extraction, ranking, and simpler coding subagents.

• OpenAI's latest model guide says to start with gpt-5.4-mini or gpt-5.4-nano when you want smaller, faster GPT-5.4-family variants instead of the full gpt-5.4 model.

That matters because a lot of teams still think "small model" means "toy model." These two are closer to routing components inside a real agent stack.

Official Benchmark Context

From the launch page, here are the headline comparisons at the highest tested reasoning settings:

• SWE-Bench Pro (Public): Mini 54.4%, Nano 52.4%

• Terminal-Bench 2.0: Mini 60.0%, Nano 46.3%

• Toolathlon: Mini 42.9%, Nano 35.5%

• GPQA Diamond: Mini 88.0%, Nano 82.8%

• OSWorld-Verified: Mini 72.1%, Nano 39.0%

The pattern is pretty clear:

• Mini keeps a meaningful lead on coding, tool use, and computer-use-style evaluation.

• Nano is still capable, but it drops off more sharply when the workflow becomes tool-heavy or visually grounded.

• If your product depends on screenshot interpretation or UI-level task execution, Mini is the safer default.

Mini vs Nano At A Glance

Both model pages currently list the same core envelope:

• 400k context window

• 128k max output

• Aug 31, 2025 knowledge cutoff

• text input and output

• image input only

The real difference is how much capability and control surface you get per token.

GPT-5.4 Mini

Use Mini when you need:

• coding workflows with stronger reasoning

• computer use

• tool search

• subagents that must search a codebase, review large files, or work through ambiguous supporting tasks

The official model page lists support for:

• web search

• file search

• image generation

• code interpreter

• hosted shell

• apply patch

• skills

• MCP

• tool search

• computer use

GPT-5.4 Nano

Use Nano when you need:

• cheap classification

• extraction and tagging

• ranking and scoring

• simpler coding subagents

• high-volume background tasks where retries are acceptable

The official Nano model page still supports a surprisingly broad tool set:

• web search

• file search

• image generation

• code interpreter

• hosted shell

• apply patch

• skills

• MCP

But Nano does not currently support:

• computer use

• tool search

That is one of the most practical routing boundaries in the whole decision.

Pricing: The Difference Is Large Enough To Change Architecture

On paper, both models are "small." In production budgeting, they are not interchangeable.

• gpt-5.4-mini: $0.75 input, $0.075 cached input, $4.50 output

• gpt-5.4-nano: $0.20 input, $0.02 cached input, $1.25 output

Mini is still affordable compared with the full gpt-5.4 model, but Nano is the one you reach for when you want massive throughput on tightly constrained tasks.

A simple rule of thumb:

• If a bad answer creates extra engineer time, route upward to Mini.

• If the task is easy to validate automatically, route downward to Nano.

Where GPT-5.4 Mini Wins In Real Product Work

Mini is the better choice when the task feels like "small agentic work" rather than "cheap inference."

That usually includes:

• repository search plus targeted edits

• debugging loops that need tools

• screenshot interpretation

• browser or UI automation via computer use

• multi-step subtasks delegated by a stronger planning model

OpenAI's launch post even calls out a concrete pattern in Codex: a larger model can plan and review, while Mini subagents handle narrower tasks in parallel. That is exactly the sort of architecture where Mini makes sense.

If you are also comparing higher-end coding models, our guide on GPT-5.4 vs GPT-5.3-Codex is a useful companion read.

Where GPT-5.4 Nano Wins

Nano wins when the work is predictable enough that cost and latency matter more than richer reasoning.

Good examples:

• normalizing unstructured text into a fixed schema

• scoring or ranking candidate outputs

• filtering tool results before a stronger model sees them

• generating small code transforms with clear acceptance tests

• handling background support tasks that can be retried or audited cheaply

This is why OpenAI's own positioning for Nano centers on classification, extraction, ranking, and simpler coding subagents.

A Practical Routing Blueprint

One useful production pattern is:

1. Start every request with a lightweight router.

2. Send narrow, low-risk, high-volume work to Nano.

3. Escalate to Mini when the task becomes ambiguous, tool-heavy, or visually grounded.

4. Optionally let a larger model make the final planning or judgment call for the highest-stakes flows.

This is often better than forcing one model to do everything. It keeps the expensive reasoning where it matters and preserves fast response times for supporting work.

Prompt Blueprint

If you want a copy-paste starter prompt for a router or supervisor agent, this is a good baseline:

You are a task router for GPT-5.4 family models.

Route to gpt-5.4-nano when the task is:
- well-scoped
- easy to validate
- classification, ranking, extraction, tagging, or a small code transform
- not dependent on computer use or tool search

Route to gpt-5.4-mini when the task is:
- coding with multiple steps
- likely to require stronger reasoning
- dependent on screenshot interpretation, computer use, or tool search
- a subagent task where failure would create meaningful review cost

Return:
1. selected_model
2. short_reason
3. fallback_model_if_retry_needed

You can then add your own logging around retry rate, correction rate, and cost per successful task.

Common Mistakes And Fixes

Mistake 1: Using Nano Just Because It Is Cheaper

Nano is cheaper per token, but it becomes expensive if your team has to re-run, repair, or manually review too many outputs.

Fix:

• keep Nano on fixed-shape tasks

• promote noisy or ambiguous tasks to Mini by default

Mistake 2: Using Mini For Every Background Job

Mini is the better model, but not every task needs that level of reasoning or tool reach.

Fix:

• carve out extraction, ranking, and schema-fill work for Nano

• keep Mini for the tasks with meaningful failure cost

Mistake 3: Forgetting Tool Support Boundaries

Mini and Nano do not expose the exact same tool surface.

Fix:

• do not design a Nano flow that depends on computer use

• do not assume tool search is available on Nano

FAQ

Is GPT-5.4 Mini just a renamed GPT-5 mini?

No. OpenAI launched GPT-5.4 mini on March 17, 2026 as a newer GPT-5.4-family model and says it improves over GPT-5 mini across coding, reasoning, multimodal understanding, and tool use while running more than 2x faster.

Is GPT-5.4 Nano good for coding?

Yes, but mainly for simpler supporting coding work. The official launch page positions Nano for simpler coding subagents rather than heavier coding tasks.

Do both models support images?

Yes. Both model pages list image input support, but not image output as a core modality.

Does GPT-5.4 Nano support computer use?

No. As of the current model page on March 20, 2026, Nano does not support computer use.

What is the safest default for production teams?

If you do not yet have routing data, Mini is usually the safer starting point for product-critical workflows. Then you can gradually move stable, narrow tasks to Nano.

Final Action

If your team is mapping out a broader AI stack, browse our AI tools directory for adjacent workflow tools, or explore more tactical reads in the blog archive.

References

• OpenAI, Introducing GPT-5.4 mini and nano — published March 17, 2026

• OpenAI API, GPT-5.4 mini model page — accessed March 20, 2026

• OpenAI API, GPT-5.4 nano model page — accessed March 20, 2026

• OpenAI API, Latest model guide — accessed March 20, 2026

• Tom's Guide, You probably won't use OpenAI's newest model — but it's about to change every AI tool you use — published March 18, 2026

• TechRadar, I tested the new ChatGPT-5.4 mini and nano models — and I didn't expect them to be this powerful — published March 18, 2026