How Much Does OpenAI API Cost? A Detailed Pricing Guide

How Much Does OpenAI API Cost? A Detailed Pricing Guide
how much does open ai api cost

In the rapidly evolving landscape of artificial intelligence, OpenAI has emerged as a powerhouse, providing developers and businesses with access to some of the most advanced large language models (LLMs) and generative AI capabilities on the planet. From crafting sophisticated chatbots and intelligent assistants to generating stunning images and transcribing audio, OpenAI's API offers a vast array of tools to innovate and automate. However, for many embarking on their AI journey, a critical question often arises: how much does OpenAI API cost? Understanding the intricate pricing structure is not merely a matter of budget allocation; it's a strategic imperative that influences architectural decisions, model selection, and ultimately, the profitability and scalability of AI-driven applications.

This comprehensive guide aims to demystify OpenAI's API pricing, breaking down the costs associated with various models, services, and usage patterns. We'll delve into the nuances of token-based pricing, explore the cost-effectiveness of different model tiers, and provide practical strategies for managing and optimizing your expenditures. By the end of this article, you'll have a clear understanding of the financial implications of integrating OpenAI's powerful AI into your projects, enabling you to build intelligent solutions with confidence and fiscal prudence.

Unpacking the OpenAI Pricing Model: A Token-Based Economy

At the heart of OpenAI's API pricing lies a token-based system. Unlike traditional software-as-a-service (SaaS) models that might charge per user or per API call, OpenAI's language models, and even some other services like embeddings, bill you based on the number of "tokens" processed. But what exactly is a token, and why is this understanding crucial?

A token can be thought of as a common sequence of characters found in text. For English text, a single token typically corresponds to roughly four characters or about three-quarters of a word. So, 100 tokens would be about 75 words. For other languages, the token-to-word ratio might vary. When you send a prompt to an OpenAI model, the input text is broken down into tokens. Similarly, the response generated by the model is also measured in tokens.

The critical distinction in pricing is often between input tokens (what you send to the model) and output tokens (what the model generates in response). Generally, output tokens are more expensive than input tokens, reflecting the computational effort involved in generating new content. This distinction underscores the importance of efficient prompt engineering, as verbose prompts or excessively long responses can quickly inflate costs.

Furthermore, each model has a specific context window, which defines the maximum number of tokens (input + output) it can handle in a single request. For example, a model with a 128k token context window can process 128,000 tokens in a single conversation turn. This large context allows for more detailed instructions, longer conversations, and the processing of extensive documents, but also means that even if you don't use the full context, the potential for high token usage is always there.

Understanding these fundamentals—tokens, input/output pricing, and context windows—forms the bedrock of managing your OpenAI API costs effectively.

The Diverse Palette of OpenAI Models: Pricing by Capability

OpenAI offers a spectrum of models, each designed for different tasks, offering varying levels of intelligence, speed, and, consequently, cost. The choice of model is arguably the most significant factor influencing your API expenditure. Let's break down the pricing for the main categories of models.

1. Generative Pre-trained Transformers (GPT) Models: The Brains of the Operation

The GPT series represents OpenAI's flagship large language models, renowned for their ability to understand, generate, and manipulate human language. These models are categorized by their power and efficiency, leading to a tiered pricing structure.

GPT-4o: The Newest Contender for Omnimodel Supremacy

OpenAI's latest and most advanced model, GPT-4o (where "o" stands for "omnimodel"), represents a significant leap forward. It's designed to be natively multimodal, meaning it can process and generate text, audio, and images from a single neural network. It boasts impressive speed and efficiency, often outperforming previous GPT-4 models while being considerably more cost-effective. This model is a game-changer for applications requiring real-time interaction and sophisticated multimodal understanding. Its o4-mini pricing makes advanced AI more accessible than ever.

Current GPT-4o Pricing (as of May 2024):

| Model Name | Input Price (per 1 Million Tokens) | Output Price (per 1 Million Tokens) | Context Window | Description | | :---------------- | :--------------------------------- | :---------------------------------- | :------------- | 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---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------GPT-4 Turbo (e.g., gpt-4-turbo-2024-04-09) * Input: $10.00 / 1M tokens * Output: $30.00 / 1M tokens

GPT-4 and GPT-3.5 Turbo: Workhorses of Language AI

The original GPT-4 models, while still incredibly powerful, are older generations. GPT-3.5 Turbo, on the other hand, is a family of highly cost-effective and fast models, ideal for tasks that don't require the absolute cutting edge of reasoning or comprehensive knowledge.

Current GPT-4 and GPT-3.5 Turbo Pricing:

| Model Name | Input Price (per 1 Million Tokens) | Output Price (per 1 Million Tokens) | Context Window | Description | | :---------------- | :--------------------------------- | :---------------------------------- | :------------- | 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GPT-4 Turbo with Vision (128k tokens context window) is generally used for vision capabilities, allowing your applications to analyze images alongside text prompts.

Standard GPT-4 Models: For the Most Demanding Tasks

The original GPT-4 models provided groundbreaking reasoning capabilities. While still available, for most new projects, GPT-4o or GPT-4 Turbo are often preferred due to their superior speed and lower cost for comparable or better performance.

GPT-4 Legacy Models Pricing:

| Model Name | Input Price (per 1 Million Tokens) | Output Price (per 1 Million Tokens) | Context Window | Description | | :---------------- | :--------------------------------- | :---------------------------------- | :------------- | 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GPT-4o, with its significantly reduced costs per token compared to previous GPT-4 models, presents an exceptionally attractive option for new AI applications and optimizing existing deployments.

Fine-tuning GPT Models: Customization at a Cost

OpenAI also offers the capability to fine-tune GPT-3.5 Turbo models on your specific datasets. Fine-tuning allows the model to learn your particular style, tone, and domain-specific knowledge, leading to more accurate and relevant responses. However, fine-tuning involves distinct costs:

  • Training Costs: Charged per 1,000 tokens processed during the training phase. This includes both the prompt and completion tokens in your training data.
  • Usage Costs: After a model is fine-tuned, its usage is also charged per 1,000 tokens, but often at a higher rate than the base model due to the specialized nature of the hosted model.
  • Hosting Costs: A daily charge applies for keeping your fine-tuned model deployed, regardless of usage.

Example Fine-tuning Pricing (for gpt-3.5-turbo-0125):

Category Price (per 1 Million Tokens) Hosting Cost (per day) Description
Training Input: $8.00
Output: $16.00
N/A Costs incurred while the model learns from your data.
Fine-tuned Usage Input: $3.00
Output: $6.00
$0.60 Higher usage rate for your specialized model, plus a daily hosting fee.

Fine-tuning is a powerful feature but requires careful consideration of its upfront training costs and ongoing usage/hosting fees. It's best suited for tasks where the base model consistently underperforms, and custom knowledge or style is paramount.

2. Embedding Models: Transforming Text into Vectors

Embedding models convert text into numerical representations (vectors) that capture semantic meaning. These embeddings are crucial for applications like semantic search, recommendation systems, classification, and Retrieval-Augmented Generation (RAG). OpenAI offers highly efficient and cost-effective embedding models.

Current Embedding Models Pricing:

Model Name Price (per 1 Million Tokens) Output Dimension Description
## The Evolving Landscape of OpenAI's Pricing: Key Factors and Recent Changes

OpenAI frequently updates its models and pricing, usually offering more efficient models at lower costs over time. The introduction of models like GPT-4o exemplifies this trend, providing higher performance at a fraction of the cost of its predecessors. This constant evolution means that staying informed about the latest pricing is crucial for cost-effective development.

Several key factors determine your ultimate OpenAI API costs:

  1. Model Choice: As evident from the tables above, the most impactful decision is selecting the right model for the job. Using GPT-4o or GPT-4 Turbo for simple classification or data extraction where GPT-3.5 Turbo might suffice is a surefire way to overspend. Conversely, using GPT-3.5 Turbo for highly complex reasoning tasks that demand GPT-4o's capabilities will result in suboptimal outcomes or require extensive prompt engineering that consumes more tokens anyway.
  2. Input vs. Output Tokens: Always remember that output tokens are generally more expensive. Optimize your prompts to be concise and steer the model towards shorter, more direct answers when possible. Avoid asking for verbose explanations if a simple list will do.
  3. Context Window Management: While larger context windows offer incredible flexibility, using them inefficiently can be costly. If you're processing large documents, consider strategies like summarization, semantic chunking, and using embedding search to retrieve only the most relevant sections before feeding them to a GPT model. Sending entire lengthy documents in every API call will quickly deplete your budget.
  4. Frequency and Volume of API Calls: High-volume applications will naturally incur higher costs. Monitoring your usage and understanding peak times can help in capacity planning and cost prediction.
  5. Specific Features Used: Services like DALL-E (image generation), Whisper (speech-to-text), and TTS (text-to-speech) have their own distinct pricing structures. If your application heavily relies on these, their costs must be factored in separately.
  6. Fine-tuning Investment: While fine-tuning can improve model performance for specific tasks, it comes with training and ongoing hosting costs. Weigh the benefits of improved accuracy against these additional expenses.

Real-World Cost Estimation: Putting Tokens into Perspective

Let's consider a few practical scenarios to illustrate how much OpenAI API cost can vary:

  • Scenario 1: Simple Chatbot (GPT-3.5 Turbo)
    • A user sends a 100-token query.
    • The chatbot responds with 150 tokens.
    • Cost per conversation: (100 input tokens * $0.50/1M) + (150 output tokens * $1.50/1M) = $0.00005 + $0.000225 = $0.000275.
    • 10,000 conversations/month: $2.75. Highly affordable for basic interactions.
  • Scenario 2: Content Generation (GPT-4o)
    • A content creator provides a 500-token prompt for an article.
    • GPT-4o generates a 2000-token article.
    • Cost per article: (500 input tokens * $5.00/1M) + (2000 output tokens * $15.00/1M) = $0.0025 + $0.03 = $0.0325.
    • Generating 100 articles/month: $3.25. Far more cost-effective than previous GPT-4 models.
  • Scenario 3: Document Analysis with RAG (GPT-4 Turbo with Embeddings)
    • Embeddings: A user uploads a 100,000-token document for embedding. Using text-embedding-3-small: 100,000 tokens * $0.02/1M = $0.002.
    • Retrieval: A user asks a question, triggering a search over the embeddings. Relevant chunks totaling 2,000 tokens are retrieved.
    • Generation: These 2,000 tokens (input) are sent to gpt-4-turbo-2024-04-09 along with a 100-token user query. The model generates a 300-token answer.
    • Cost per query: (2100 input tokens * $10.00/1M) + (300 output tokens * $30.00/1M) = $0.021 + $0.009 = $0.03. (Plus the initial embedding cost).
    • If 1,000 users query this document system daily: $30/day, or ~$900/month, plus embedding costs if documents are updated.

These examples highlight the sensitivity of costs to model choice and token efficiency.

XRoute is a cutting-edge unified API platform designed to streamline access to large language models (LLMs) for developers, businesses, and AI enthusiasts. By providing a single, OpenAI-compatible endpoint, XRoute.AI simplifies the integration of over 60 AI models from more than 20 active providers(including OpenAI, Anthropic, Mistral, Llama2, Google Gemini, and more), enabling seamless development of AI-driven applications, chatbots, and automated workflows.

Token Price Comparison and Optimization Strategies

The direct Token Price Comparison across models clearly shows that GPT-3.5 Turbo and embedding models offer significantly lower costs per token than the more advanced GPT-4 series, particularly prior to GPT-4o. This pricing disparity provides a clear roadmap for cost optimization.

1. Strategic Model Selection: The Right Tool for the Job

This is perhaps the most crucial optimization strategy. Don't use a sledgehammer to crack a nut.

  • GPT-3.5 Turbo for General Tasks: For common tasks like quick answers, simple text generation, basic summarization, translation, or chatbot interactions where extreme accuracy or complex reasoning isn't paramount, gpt-3.5-turbo is incredibly cost-effective. Its speed also makes it ideal for real-time applications where latency is a concern.
  • GPT-4o for Advanced Reasoning and Multimodal Tasks: When you absolutely need the best reasoning, creativity, or require multimodal capabilities (understanding images or audio), GPT-4o is the go-to. Its significantly lower price point compared to older GPT-4 models makes it highly accessible for these demanding tasks without breaking the bank. It excels in complex problem-solving, code generation, detailed content creation, and nuanced understanding of prompts.
  • GPT-4 Turbo for Specialized GPT-4 Tasks (Legacy): For applications built on specific previous GPT-4 Turbo versions or where 128k context is a hard requirement and GPT-4o's multimodal nature is not needed, GPT-4 Turbo still serves its purpose, though GPT-4o is often a superior choice now.
  • Embedding Models for Search and Retrieval: Never use a large language model to create embeddings or perform vector searches. Dedicated embedding models are orders of magnitude cheaper and more efficient for these tasks.
  • Fine-tuning for Niche Domains: If your application requires highly specific knowledge or adheres to a unique brand voice, fine-tuning a gpt-3.5-turbo model can offer a good balance of performance and cost, provided the training data is clean and substantial.

2. Prompt Engineering for Token Efficiency

Optimizing your prompts can dramatically reduce token usage.

  • Be Concise and Clear: Avoid unnecessary words in your prompts. Get straight to the point.
  • Use Few-Shot Learning: Instead of long, detailed instructions, provide a few examples of desired input/output pairs. This often guides the model more effectively with fewer tokens.
  • Instruct for Brevity: Explicitly tell the model to "be concise," "provide a brief summary," or "list key points" if short answers are desired.
  • Structured Output: Requesting structured outputs (e.g., JSON) can sometimes be more token-efficient than free-form text, as it reduces ambiguity and often leads to more direct responses.
  • Summarize Inputs: Before sending large chunks of text to a powerful (and expensive) model, consider using a cheaper model (like GPT-3.5 Turbo) or a local algorithm to summarize the input first.

3. Smart Context Management

  • Chunking and Retrieval: For applications dealing with large knowledge bases, implement Retrieval-Augmented Generation (RAG). Store your documents as embeddings, and when a query comes in, retrieve only the most relevant chunks to pass to the LLM. This drastically reduces the input token count.
  • Conversation Summarization: In long-running conversations, periodically summarize past turns and feed only the summary and the latest few turns to the LLM. This keeps the context window lean and prevents costs from spiraling.
  • Truncation: If the exact beginning or end of a lengthy input isn't critical, truncate the text to fit within a smaller context window.
  • Batch Processing: For non-real-time tasks, batching multiple smaller requests into one larger request (if feasible within the context window) can sometimes offer efficiencies, though direct API pricing is per token, so the main saving is on API call overheads.

4. Caching and Deduplication

  • Cache Common Responses: If your application frequently asks the same or very similar questions and expects consistent answers, cache the responses. This eliminates the need for repeated API calls.
  • Deduplicate Requests: Implement logic to detect identical requests within a short timeframe and serve the cached response instead of calling the API again.

5. Monitoring and Analytics

  • Set Usage Limits and Alerts: OpenAI provides tools in your dashboard to set hard and soft usage limits. Configure email alerts to notify you when you approach your budget.
  • Track Token Usage: Integrate logging into your application to track token usage per user, feature, or project. This data is invaluable for identifying cost sinks and optimizing.
  • Analyze API Logs: Regularly review your API logs to understand which models are being called most frequently, with what context sizes, and what kind of responses they generate.

6. Utilizing OpenAI's Evolving Pricing and Features

OpenAI is continuously releasing new, more efficient models and modifying pricing. For instance, the transition from older GPT-4 models to GPT-4 Turbo and now GPT-4o demonstrates a clear trend of lower costs for higher performance. Always check the latest pricing page on OpenAI's website and be prepared to migrate your applications to newer, more cost-effective models as they become available. Early adoption of these newer models, like optimizing for o4-mini pricing, can lead to substantial savings.

Beyond OpenAI: Exploring Unified API Platforms for Broader Access and Cost Efficiency

While OpenAI provides market-leading models, the broader AI ecosystem is rich with innovation. Developers and businesses are increasingly seeking solutions that offer flexibility, redundancy, and even greater cost-effectiveness by leveraging models from various providers. This is where the concept of a unified API platform becomes invaluable.

Imagine a scenario where your application needs to use the latest GPT-4o for complex reasoning, but a much cheaper open-source model like Llama 3 for simpler classification, and perhaps a specialized text-to-image model from a different vendor for design tasks. Managing multiple API keys, different SDKs, varying rate limits, and inconsistent data formats across these providers can quickly become an integration nightmare.

This challenge is precisely what platforms like XRoute.AI are built to solve. XRoute.AI is a cutting-edge unified API platform designed to streamline access to large language models (LLMs) for developers, businesses, and AI enthusiasts. By providing a single, OpenAI-compatible endpoint, XRoute.AI simplifies the integration of over 60 AI models from more than 20 active providers, enabling seamless development of AI-driven applications, chatbots, and automated workflows.

How XRoute.AI Enhances Cost-Effectiveness and Flexibility

XRoute.AI addresses several critical pain points, particularly concerning cost and operational complexity:

  • Simplified Integration: Instead of writing custom code for each provider, you interact with a single API endpoint. This dramatically reduces development time and maintenance overhead. Your existing OpenAI integrations can often be repurposed with minimal changes, simply by pointing to XRoute.AI's endpoint.
  • Low Latency AI & High Throughput: XRoute.AI optimizes routing and infrastructure to ensure low latency AI responses, crucial for real-time applications where every millisecond counts. Its architecture also supports high throughput, ensuring your applications can scale to meet demand without performance bottlenecks.
  • Cost-Effective AI Through Dynamic Routing: One of XRoute.AI's most compelling features is its ability to route requests dynamically. This means you can configure it to automatically select the most cost-effective model for a given task, or to failover to a different provider if one is experiencing issues. This capability allows for real-time Token Price Comparison across various providers, ensuring you always get the best value without manual intervention. For instance, if another provider offers a comparable model at a lower price for certain types of queries, XRoute.AI can intelligently switch to it.
  • Access to a Wider Model Ecosystem: Beyond OpenAI, XRoute.AI gives you access to models from Cohere, Anthropic, Google, Mistral, and many more. This expands your toolkit, allowing you to choose the absolute best model for each specific task, often leading to better performance and lower costs than being locked into a single provider.
  • Centralized Monitoring and Management: With a single platform, you get a unified view of your AI usage across all providers, simplifying cost tracking, performance monitoring, and API key management.
  • Flexible Pricing Model: XRoute.AI's flexible pricing model is designed to accommodate projects of all sizes, from startups experimenting with AI to enterprise-level applications with high demands. This ensures that you only pay for what you use, without hidden fees or complex tiered structures.

In essence, XRoute.AI empowers you to build intelligent solutions without the complexity of managing multiple API connections, offering a strategic advantage in terms of development velocity, cost optimization, and access to a broader, more competitive AI landscape. It's a pragmatic choice for anyone looking to maximize their AI investment and future-proof their applications against vendor lock-in.

Conclusion: Navigating the Costs of Intelligent Innovation

Understanding how much OpenAI API cost is foundational for any developer or business venturing into the world of AI. It's not a static number but a dynamic equation influenced by model selection, token efficiency, usage patterns, and the ever-evolving landscape of AI technologies. By diligently applying strategies for model choice, prompt engineering, context management, and continuous monitoring, you can significantly optimize your OpenAI API expenditures.

The recent advancements, particularly with models like GPT-4o, demonstrate OpenAI's commitment to delivering more powerful capabilities at increasingly accessible price points. This trend, coupled with the emergence of unified API platforms like XRoute.AI, ushers in an era where advanced AI is not only more capable but also more manageable and cost-effective. These platforms offer an intelligent layer that sits atop various AI providers, enabling developers to harness the best of breed models, perform real-time Token Price Comparison, and ensure low latency AI and cost-effective AI without the associated integration headaches.

As you continue to build and innovate with AI, remember that strategic planning and informed decision-making regarding your API usage will be as crucial as the intelligence of the models themselves. By staying informed and leveraging the tools available, you can unlock the full potential of AI without unintended financial surprises, ensuring your projects remain both cutting-edge and economically viable.


Frequently Asked Questions (FAQ)

Q1: What is a "token" in OpenAI API pricing, and how does it relate to cost?

A1: A token is a fundamental unit of text used by OpenAI models. Roughly, 1 token corresponds to 4 characters or 0.75 words in English. OpenAI charges you based on the number of tokens you send to the model (input tokens) and the number of tokens the model generates (output tokens). Output tokens are typically more expensive than input tokens. The more tokens you process, the higher your cost.

Q2: Which OpenAI model is the most cost-effective for general tasks, and which for advanced tasks?

A2: For general tasks like basic chatbots, simple text generation, or quick summaries, gpt-3.5-turbo is the most cost-effective. For advanced reasoning, complex problem-solving, and multimodal capabilities (processing text, images, and audio), gpt-4o offers the best balance of performance and cost-effectiveness among the most powerful models, with significantly lower o4-mini pricing compared to previous GPT-4 versions.

Q3: How can I reduce my OpenAI API costs for applications dealing with large documents or long conversations?

A3: To reduce costs for large documents or long conversations, focus on efficient context management. Strategies include: * Retrieval-Augmented Generation (RAG): Use embedding models to store and retrieve only relevant document chunks for the LLM. * Conversation Summarization: Periodically summarize past turns in a conversation to keep the context window small. * Prompt Engineering: Be concise with prompts and instruct the model to provide brief responses when possible.

Q4: Are there separate costs for services like DALL-E (image generation) or Whisper (speech-to-text)?

A4: Yes, DALL-E for image generation, Whisper for speech-to-text, and TTS (Text-to-Speech) models have their own distinct pricing structures. DALL-E is typically charged per image generated, while Whisper and TTS are usually charged per minute of audio or per character of text, respectively. These costs are separate from the token-based pricing of the GPT language models.

Q5: How can a unified API platform like XRoute.AI help with OpenAI API costs and flexibility?

A5: A unified API platform like XRoute.AI can significantly help by providing a single endpoint to access over 60 AI models from multiple providers, including OpenAI. This allows for: * Cost Optimization: Dynamic routing to automatically select the most cost-effective model across different providers for a given task, based on Token Price Comparison. * Flexibility & Redundancy: Access to a wider ecosystem of models reduces vendor lock-in and provides failover options. * Simplified Integration: Reduces development time by standardizing API interactions. * Performance: Achieves low latency AI and high throughput through optimized infrastructure.

🚀You can securely and efficiently connect to thousands of data sources with XRoute in just two steps:

Step 1: Create Your API Key

To start using XRoute.AI, the first step is to create an account and generate your XRoute API KEY. This key unlocks access to the platform’s unified API interface, allowing you to connect to a vast ecosystem of large language models with minimal setup.

Here’s how to do it: 1. Visit https://xroute.ai/ and sign up for a free account. 2. Upon registration, explore the platform. 3. Navigate to the user dashboard and generate your XRoute API KEY.

This process takes less than a minute, and your API key will serve as the gateway to XRoute.AI’s robust developer tools, enabling seamless integration with LLM APIs for your projects.


Step 2: Select a Model and Make API Calls

Once you have your XRoute API KEY, you can select from over 60 large language models available on XRoute.AI and start making API calls. The platform’s OpenAI-compatible endpoint ensures that you can easily integrate models into your applications using just a few lines of code.

Here’s a sample configuration to call an LLM:

curl --location 'https://api.xroute.ai/openai/v1/chat/completions' \
--header 'Authorization: Bearer $apikey' \
--header 'Content-Type: application/json' \
--data '{
    "model": "gpt-5",
    "messages": [
        {
            "content": "Your text prompt here",
            "role": "user"
        }
    ]
}'

With this setup, your application can instantly connect to XRoute.AI’s unified API platform, leveraging low latency AI and high throughput (handling 891.82K tokens per month globally). XRoute.AI manages provider routing, load balancing, and failover, ensuring reliable performance for real-time applications like chatbots, data analysis tools, or automated workflows. You can also purchase additional API credits to scale your usage as needed, making it a cost-effective AI solution for projects of all sizes.

Note: Explore the documentation on https://xroute.ai/ for model-specific details, SDKs, and open-source examples to accelerate your development.

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