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GPT 5 Nano

The smallest and fastest variant in the GPT-5 series, optimized for developer tools, rapid interactions, and ultra-low latency environments. Although it has limitations in inference depth compared to larger models, it retains key instruction-following and security features. It is the successor to GPT-4.1-nano, offering a lightweight option for cost-sensitive or real-time applications.
Intelligence(Medium)
Speed(Fast)
Input Supported Modalities
Yes
Is Reasoning Model
400,000
Context Window
128,000
Maximum Output Tokens
2024-10-31
Knowledge Cutoff

Pricing

¥0.36 /M tokens
Input
¥2.88 /M tokens
Output
¥1.08 /M tokens
Blended Price

Quick Simple Comparison

Input

Output

GPT-5‑pro
gpt‑oss‑120b
GPT-4.1 mini
¥0.4

Basic Parameters

GPT-5-NanoTechnical Parameters
Parameter Count
Not Announced
Context Length
400.00k tokens
Training Data Cutoff
2024-10-31
Open Source Category
Proprietary
Multimodal Support
Text, Image
Throughput
387
Release Date
2025-08-07
Response Speed
387 tokens/s

Benchmark Scores

Below is the performance of GPT-5-Nano in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
47
Large Language Model Intelligence Level
Coding Index
88
Indicator of AI model performance on coding tasks
Math Index
87
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
82
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
88.4
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
88
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
88
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
88
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
87.2
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
87
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
94.6
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
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