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GPT-5 Nano

It supports extensive context processing and key NLP tasks such as summarization and classification, making it ideal for developers and enterprises needing fast, affordable, and versatile AI across text-to-text and image-to-text workflows.

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Text to Speech

Javascript

Python

                                        const { OpenAI } = require('openai');

const api = new OpenAI({
  baseURL: 'https://api.ai.cc/v1',
  apiKey: '',
});

const main = async () => {
  const result = await api.chat.completions.create({
    model: 'openai/gpt-5-nano-2025-08-07',
    messages: [
      {
        role: 'system',
        content: 'You are an AI assistant who knows everything.',
      },
      {
        role: 'user',
        content: 'Tell me, why is the sky blue?'
      }
    ],
  });

  const message = result.choices[0].message.content;
  console.log(`Assistant: ${message}`);
};

main();

                                        import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.ai.cc/v1",
    api_key="",    
)

response = client.chat.completions.create(
    model="openai/gpt-5-nano-2025-08-07",
    messages=[
        {
            "role": "system",
            "content": "You are an AI assistant who knows everything.",
        },
        {
            "role": "user",
            "content": "Tell me, why is the sky blue?"
        },
    ],
)

message = response.choices[0].message.content

print(f"Assistant: {message}")

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GPT-5 Nano

Product Detail

GPT-5 nano stands as a streamlined variant of OpenAI's GPT-5 model, meticulously engineered to deliver advanced multimodal reasoning and contextual understanding while significantly minimizing computational overhead. It presents itself as an efficient and cost-effective solution for developers and enterprises prioritizing rapid inference, all without compromising on the core capabilities of the comprehensive GPT-5 system.

Technical Specifications

Context Window and Token Capacity

GPT-5 nano boasts an impressive input context size of up to 400K tokens, directly mirroring the full-scale GPT-5. This robust capacity enables it to efficiently process extensive documents and diverse multimodal inputs, including sophisticated text-to-text and image-to-text tasks.

Performance Benchmarks

🚀 Speed & Latency: Optimized for low-latency inference, prioritizing faster response times, with thoughtful trade-offs compared to the deepest reasoning layers of the full GPT-5.
✅ Accuracy: Retains strong few-shot learning capabilities, profound multimodal understanding, and factual correctness, though designed for slightly less complexity handling than GPT-5 and GPT-5 mini.
🌐 Multilingual Support: Offers comprehensive language support, leveraging the expanded language capabilities inherent in the GPT-5 framework.

Architecture Highlights

Inheriting the advanced transformer framework of GPT-5, GPT-5 nano incorporates optimized attention mechanisms and efficient utilization of sparsity and mixture-of-experts layers, all meticulously tuned for lightweight operation. This architecture masterfully balances architectural scale to achieve high throughput and reduced compute costs, with a sharp focus on core reasoning and multimodal processing capabilities.

API Pricing

• Input tokens: $0.0525 per million tokens
• Output tokens: $0.42 per million tokens
• Cached input tokens: $0.00525 per million tokens

Core Features & Capabilities

✨ Model Scale: Features a smaller parameter count than GPT-5 and mini, purpose-built for speed and resource efficiency without substantial sacrifices in contextual understanding or multimodal tasks.
🖼️ Multimodality: Supports text-to-text and vision (image-to-text) input modalities through its API. Future expansions within the unified GPT-5 framework are targeted for audio, video, and code input functionalities.
🧠 Reasoning: Capable of stepwise logical reasoning and complex problem-solving, albeit optimized for faster execution over the most compute-intensive scenarios.
⚙️ Fine-Tuning & Adaptability: Provides flexible customization options for domain-specific tasks and diverse enterprise requirements.
🛡️ Bias & Safety: Integrates advanced alignment, bias mitigation, and safety features, consistent with the high standards of GPT-5.

Code Sample

<snippet data-name="open-ai.chat-completion" data-model="openai/gpt-5-nano-2025-08-07"></snippet>

Use Cases & Applications

💡 Rapid multimodal content understanding and generation, particularly valuable in cost-sensitive environments.
💡 Scalable deployment for lightweight software engineering support, encompassing code suggestions and debugging.
💡 Real-time, large-scale document analysis seamlessly integrated with image context.
💡 Educational tools and research assistants that require concise and accurate multi-step instruction processing.

Comparison with Other Models

VS GPT-5 mini: GPT-5 nano excels in the fastest execution and lowest cost, offering fundamental multimodal support. In contrast, GPT-5 mini strikes a balance between speed and reasoning depth, accommodating some expanded workflows at a slightly higher price point.

VS GPT-4o: GPT-5 nano demonstrates significant superiority over GPT-4o in reasoning accuracy, multimodal capabilities, and hallucination reduction. It also maintains considerably lower latency and cost compared to GPT-4o’s heavier, yet simpler, model design.

VS OpenAI o3: GPT-5 nano delivers more reliable fact-based answers and advanced reasoning than o3, enhanced by specialized alignment and safety mechanisms. It provides highly cost-efficient multimodal AI, ideally suited for real-time applications.

Frequently Asked Questions (FAQs)

❓ What extreme distillation techniques enable GPT-5 Nano's sub-100M parameter intelligence?

GPT-5 Nano employs revolutionary neural architecture search and progressive knowledge distillation that compresses GPT-5's capabilities into an astonishingly compact 87-million parameter model. The architecture features ultra-efficient attention mechanisms with factorized computations, shared expert networks that maximize parameter utilization, and dynamic width scaling that adapts model capacity based on task demands.

❓ How does the model maintain meaningful capabilities at such extreme compression ratios?

GPT-5 Nano implements capability-preserving compression through prioritized knowledge retention that focuses on essential reasoning patterns, common-sense understanding, and frequently used domains. The architecture employs multi-objective optimization that balances size constraints with performance retention and sophisticated parameter sharing.

❓ What deployment scenarios become possible with GPT-5 Nano's minimal footprint?

The model enables AI deployment in previously impossible scenarios including always-on wearable devices, embedded systems in consumer electronics, resource-constrained IoT devices, and applications requiring extreme privacy with no cloud dependency.

❓ How does GPT-5 Nano handle the fundamental trade-offs of extreme model compression?

The architecture makes intelligent compromises by prioritizing robust performance on common tasks, focusing on efficient information retrieval rather than deep creative generation, and optimizing for reliable operation within known domains rather than broad general knowledge.

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