GPT-5.4 Mini vs Nano: Which Small Model to Use?
GPT-5.4 Mini vs Nano Overview
OpenAI’s GPT-5.4 family includes two small models targeting different segments of the AI workload spectrum. GPT-5.4 mini delivers near-flagship performance for complex tasks, while GPT-5.4 nano is the smallest and fastest option for high-volume, simple operations.
Both models launched as part of the GPT-5.4 release in March 2026, giving developers a clear choice between capability and cost efficiency.
Benchmark Comparison
| Benchmark | GPT-5.4 Mini | GPT-5.4 Nano | Winner |
|---|---|---|---|
| SWE-bench Pro | 54.38% | 52.39% | Mini |
| Terminal-Bench 2.0 | 60.0% | 46.3% | Mini |
| GPQA Diamond | 88.01% | — | Mini |
| OSWorld-Verified | 72.13% | — | Mini |
| Input Price | $0.15/M | Lower | Nano |
| Output Price | $0.60/M | Lower | Nano |
| Speed | >2x faster than GPT-5 mini | Fastest in family | Nano |
GPT-5.4 mini wins every benchmark head-to-head. The gap is especially wide on Terminal-Bench 2.0 (60% vs 46.3%), which measures real-world terminal and coding agent performance. On SWE-bench Pro, the difference narrows to just 2 percentage points.
When to Use GPT-5.4 Mini
GPT-5.4 mini is the sweet spot for developers who need strong reasoning without flagship pricing. Key use cases include:
- Coding assistants — With 54.38% SWE-bench Pro and 60% Terminal-Bench 2.0, it handles complex code generation and debugging effectively.
- Subagent workloads — Fast enough to serve as a worker agent in multi-agent architectures, with enough capability for nuanced subtasks.
- Computer use — Its 72.13% OSWorld-Verified score makes it viable for GUI automation and computer interaction tasks.
- Multimodal applications — Near-flagship GPQA Diamond score (88.01% vs GPT-5.4’s 93%) at a fraction of the cost.
Companies like Hebbia and Notion have already adopted GPT-5.4 mini for production workloads where cost-performance balance matters.
When to Use GPT-5.4 Nano
GPT-5.4 nano targets a different problem entirely: high-volume, latency-sensitive tasks where “good enough” beats “best possible.” Ideal scenarios include:
- Classification — Sorting tickets, categorizing content, or routing requests where speed matters more than nuance.
- Data extraction — Pulling structured data from documents at scale.
- Ranking and scoring — Relevance scoring for search or recommendation systems.
- Edge deployment — Where model size and inference speed are primary constraints.
Nano’s 52.39% SWE-bench Pro shows it can still handle code tasks, but the 46.3% Terminal-Bench 2.0 score suggests it struggles with complex, multi-step coding workflows.
Which Should You Choose?
Choose GPT-5.4 mini if your workload involves coding, reasoning, or agentic tasks where quality directly impacts outcomes. The pricing at $0.15/$0.60 per million tokens is already aggressive for the performance level.
Choose GPT-5.4 nano if you’re running millions of simple requests where every millisecond and fraction of a cent counts. Classification pipelines, extraction jobs, and high-throughput ranking systems are nano’s territory.
For many production architectures, the answer is both: mini for complex subtasks, nano for simple ones.