OpenAI dropped GPT-5.4 mini and nano on March 17, and the question I keep seeing everywhere is simple: which one should I use?
Short answer: mini for almost everything, nano for subagent work and bulk processing. But “almost everything” is doing a lot of heavy lifting in that sentence, so let me break down what actually matters.
The Quick Decision
If you don’t want to read 1,800 words, here’s your cheat sheet:
| GPT-5.4 Mini | GPT-5.4 Nano | |
|---|---|---|
| Input price | $0.75 / 1M tokens | $0.20 / 1M tokens |
| Output price | $4.50 / 1M tokens | $1.25 / 1M tokens |
| Context window | 400K tokens | 400K tokens |
| SWE-Bench Pro | 54.4% | 52.4% |
| OSWorld (computer use) | 72.1% | 39.0% |
| Terminal-Bench 2.0 | — | 46.3% |
| tau2-bench (telecom) | 93.4% | — |
| MCP Atlas | 57.7% | 56.1% |
| Speed | ~273 tokens/sec | ~208 tokens/sec |
| Available in ChatGPT | Yes (Free + Go tiers) | No |
| Available in Codex | Yes | No |
| API access | Yes | Yes (API only) |
| Best for | General coding, reasoning, tool use | Classification, extraction, subagents |
The numbers tell an interesting story. Mini isn’t just “better”—it’s a completely different class of model for anything involving computer use or complex tool chains. But for simpler tasks? Nano holds its own surprisingly well.
What These Models Actually Are
Both landed less than two weeks after the full GPT-5.4 launched on March 5. That’s the fastest flagship-to-small-model pipeline OpenAI has ever shipped.
GPT-5.4 mini is the workhorse. OpenAI is positioning it as the new default for production apps—good enough to feel frontier, cheap enough to run at scale. It handles text, images, tool use, function calling, web search, file search, and computer use. And it’s 2x faster than the old GPT-5 mini.
GPT-5.4 nano is the intern. Not meant to run the show, but great at well-defined tasks: classify this, extract that, rank these, handle simple coding subtasks. OpenAI built it specifically for the subagent era—where a big model plans and coordinates while smaller models execute in parallel.
The way I think about it: mini can replace your flagship model for 80% of tasks. Nano can handle the other 20% that doesn’t need a reasoning engine.
Benchmarks That Actually Matter
I’m going to skip the MMLU scores nobody cares about and focus on the benchmarks that predict real-world usefulness.
Coding (SWE-Bench Pro)
This is the one developers watch. SWE-Bench Pro tests models on actual GitHub issues—real bugs, real codebases, real fixes.
- GPT-5.4 (flagship): 57.7%
- GPT-5.4 mini: 54.4%
- GPT-5.4 nano: 52.4%
- GPT-5 mini (previous gen): 45.7%
Mini trails the flagship by only 3.3 points. Nano trails by 5.3. Both absolutely crush the previous-gen GPT-5 mini. For reference, Claude Haiku 4.5 hits about 41% on coding benchmarks, and Gemini 3 Flash lands around 47.6%.
So even nano—the $0.20 model—is beating last year’s $20/month ChatGPT experience on real coding tasks. That’s wild.
Computer Use (OSWorld-Verified)
Here’s where things diverge dramatically:
- GPT-5.4 mini: 72.1% (human baseline: 72.4%)
- GPT-5.4 nano: 39.0%
Mini is essentially human-level at computer use tasks. Nano… isn’t. If you need your model to navigate UIs, click buttons, or fill out forms, nano is not your model. This is the single biggest gap between the two.
Tool Use and Agent Work
On tau2-bench (testing real-world tool orchestration for telecom workflows), mini hits 93.4%—up from 74.1% for the previous GPT-5 mini. On MCP Atlas, mini reaches 57.7% and nano isn’t far behind at 56.1%.
Both models are solid at following tool-calling patterns. But mini handles complex, multi-step tool chains much better.
The Cost Math
This is where nano starts looking really attractive.
Per-Request Cost
Assume an average request uses 1,000 input tokens and generates 500 output tokens:
- Mini: $0.00075 + $0.00225 = $0.003 per request
- Nano: $0.0002 + $0.000625 = $0.000825 per request
Nano is 3.6x cheaper per request.
At Scale
If you’re processing 1 million requests per day:
| GPT-5.4 Mini | GPT-5.4 Nano | |
|---|---|---|
| Daily cost | $3,000 | $825 |
| Monthly cost | $90,000 | $24,750 |
| Annual cost | $1,095,000 | $301,125 |
That’s a $794K annual difference. At that volume, the question isn’t “is nano good enough”—it’s “can I afford not to use nano for every task that doesn’t need mini’s reasoning?”
Batch API Discount
Both models get a 50% discount through OpenAI’s Batch API (24-hour turnaround). If latency isn’t critical, nano through the Batch API costs $0.10 per million input tokens. That’s approaching free.
Where Each Model Wins
Use Nano When…
- Running subagents in parallel. The whole point of nano. Let your flagship model plan, and dispatch nano instances to search codebases, parse files, extract structured data.
- Classification and routing. Deciding which tool to use, which category something belongs to, whether a request is spam. Nano handles this easily.
- Processing bulk data. Simon Willison calculated you could describe 76,000 photos for $52 with nano. That’s the kind of math that makes bulk processing viable.
- Batch API workloads. Anything that can wait 24 hours. Nano + Batch API is absurdly cheap.
- Data extraction and ranking. Pull structured data from unstructured text, rank results, score relevance. Nano’s accuracy is fine here.
Use Mini When…
- It’s your primary model. For most API developers, mini is the new default. Seriously. Mercor and Hebbia are already using it in production—contract reviews, financial modeling, the works.
- You need computer use. 72.1% vs 39.0%. Not close.
- Complex coding tasks. Both are good, but mini’s reasoning depth matters when the task requires understanding a whole codebase, not just editing a single file.
- ChatGPT or Codex access. Nano is API-only. If you want to use these models through ChatGPT’s interface (free tier included) or Codex, mini is your only option.
- Tool orchestration matters. Multi-step workflows, complex function calling chains, anything that requires the model to plan its own tool usage across multiple steps.
How They Stack Up Against Competitors
The small model space is crowded. Here’s how mini and nano compare to the models you’re probably already considering:
| Model | Input Price | Output Price | SWE-Bench | Best For |
|---|---|---|---|---|
| GPT-5.4 mini | $0.75/1M | $4.50/1M | 54.4% | General production use |
| GPT-5.4 nano | $0.20/1M | $1.25/1M | 52.4% | Subagents, bulk work |
| Claude Haiku 4.5 | $1.00/1M | $5.00/1M | ~41% | Writing quality, conversational flow |
| Gemini 3 Flash | $0.50/1M | $3.00/1M | ~47.6% | Multimodal, long context |
| Gemini 2.5 Flash-Lite | $0.10/1M | $0.40/1M | — | Ultra-cheap classification |
A few things jump out.
Mini is cheaper than Claude Haiku 4.5 and beats it on coding benchmarks by a wide margin. If you’re using Haiku purely for cost, mini is a straight upgrade.
Nano undercuts Gemini 3 Flash on price while outperforming it on coding. But Gemini Flash still wins on raw throughput (200+ tokens/sec) and has that massive context window for multimodal work.
And Gemini 2.5 Flash-Lite is still the cheapest option if you literally just need “good enough” classification at rock-bottom prices—but heads up, Gemini 2.0 Flash Lite is being shut down June 1, 2026, so plan accordingly.
The Subagent Architecture (Why This Actually Matters)
Here’s the bigger picture most comparisons miss.
OpenAI didn’t release nano to compete with mini. They released it to work with mini—and with the flagship GPT-5.4. The subagent architecture is the real product here.
In Codex, a GPT-5.4 task can automatically delegate to mini subagents that run in parallel. Mini uses only 30% of the GPT-5.4 quota, meaning you can run three mini tasks for the cost of one flagship task. And nano goes even further down the cost curve for tasks that need less reasoning.
This is the pattern every serious AI application will adopt: a smart coordinator dispatching cheap, fast workers. If you’re building agents, plan your architecture around this model hierarchy now.
Want to get better at working with these models? Our Prompt Engineering course covers how to write prompts that get consistent results from both large and small models—a skill that matters more as you start mixing model tiers.
My Recommendation
For most developers reading this, here’s the play:
- Switch your default API model to GPT-5.4 mini. It’s cheap, fast, and almost as good as the flagship on coding. Unless you need peak reasoning, this should be your workhorse.
- Use nano for anything you’re doing at scale. Batch classification, data extraction, routing decisions, simple code edits. The 3.6x cost savings add up fast.
- Keep the flagship GPT-5.4 for complex reasoning. Architecture decisions, novel problem-solving, tasks that require deep multi-step planning.
- Don’t sleep on the Batch API. Nano + Batch API for non-urgent bulk work is almost criminally cheap.
The small model war is heating up, and OpenAI just made a strong case that you don’t need to pick one model anymore. You need a team of them.
If you want a deeper look at how all the major AI subscriptions compare—not just the API tier—check out our full AI Pricing Comparison for 2026.
Keep Learning
Free courses to level up your AI game:
- Prompt Engineering — Write prompts that work consistently across large and small models
- AI Fundamentals — Understand how AI models actually work under the hood
- Advanced Prompts — Chain-of-thought, few-shot, and techniques that matter more with smaller models
- Multi-Agent AI Systems — Build the coordinator + subagent architecture that nano was designed for
- AI Testing and QA — Evaluate model outputs and catch regressions when switching models
Related posts:
- AI Pricing Comparison 2026 — Full breakdown of every major AI subscription and API tier
- ChatGPT vs Claude vs Gemini — How the flagship models compare for everyday use
Pricing and benchmark data sourced from OpenAI’s official announcement, Artificial Analysis, DataCamp’s benchmark review, and Simon Willison’s analysis. All prices reflect OpenAI’s API pricing as of March 21, 2026.
