US, HK, CA: 進階 LLM 成本計算機 - Gemini, OpenAI, Anthropic API 預算
計算機，為 Gemini、OpenAI 和 Anthropic API 提供高級 LLM 成本分析，面向US, HK, CA的開發人員。此 LLM API 定價計算機專為使用 Python (Django, Flask)、JavaScript (Express.js)、Java (Spring Boot)、C#、Go 和 Ruby on Rails 等語言的程序員和軟件架構師而設。它有助於為移動APP 開發（跨平台或原生）、複雜的 WEB 開發（微服務、SPA）和強大的企業軟件開發進行細緻的財務規劃。UI/UX 設計團隊也可以利用它來了解嵌入 AI 功能的成本。評估不同的定價層級、批量折扣和模型能力（例如 GPT-4 vs Claude 3 Opus vs Gemini Pro），以符合您項目的技術要求和預算限制。為您在美國、香港、加拿大的業務運營做出戰略決策，確保成本效益並最大化從大型語言模型中獲得的價值。
Comprehensive LLM API Pricing Calculator
Estimate your Large Language Model API usage costs across various providers and models.
Estimated Costs
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API Provider
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LLM Model (Text/Chat)
$0.00000
Est. Cost per Request (Total)
$0.00
Text Input Cost
$0.00
Text Output Cost
$0.00
Total Text API Cost
$0.00
Image Input Cost
$0.00
Image Generation Cost
$0.00
Embedding Model Cost
$0.00
Audio Model Cost
$0.00
Fine-Tuning Training Cost
$0.00
Fine-Tuned Model Usage Cost
$0.00
Total Fine-Tuning Related Cost
$0.00
Estimated Grand Total Cost
Key Factors Influencing LLM API Costs
- Model Choice: More powerful models are generally more expensive. Specialized models (embedding, audio, image) have their own pricing structures.
- Token Volume: Costs are directly tied to the number of input and output tokens for text and embedding models.
- Context Window: Models supporting larger context windows may have different pricing tiers or higher costs for utilizing the full window.
- Modalities: Generating images, processing image inputs, or transcribing/synthesizing audio incurs separate costs, often per image, per minute/second of audio, or per character for TTS.
- Fine-Tuning: Involves training costs (data processing, instance hours) and often different (sometimes higher) per-token usage rates for the custom model.
- Provider & Region: Pricing can vary between providers and sometimes by datacenter region.
- Usage Tiers, Commitments & Free Tiers: Discounts for high-volume usage, committed spend, or limited free tiers are common but not covered here.
- Rate Limits & Throughput: Exceeding rate limits might lead to throttling or require higher-tier plans with different pricing.
- Specific Features: Advanced features like function calling, RAG optimization, or higher resolutions for images can influence costs.
Understanding Tokens
Tokens are the basic units of text that LLMs process. For English text:
- 1 token is approximately 4 characters.
- 1 token is approximately ¾ of a word.
- 100 tokens are about 75 words.
Different models use different tokenization methods. Use provider-specific tools (like OpenAI's Tiktokenizer) to count tokens accurately for a particular model.
Cost Optimization Tips
- Choose the Right Model: Use the least expensive model that meets your performance requirements for each specific task.
- Optimize Prompts & Queries: Keep prompts concise. For embeddings, process only necessary text.
- Limit Output Length: Instruct models to generate shorter responses where appropriate.
- Batch Requests: Batch multiple queries into fewer API calls if supported efficiently by the provider.
- Implement Caching: Cache responses for common queries to avoid redundant API calls.
- Monitor Usage Regularly: Use provider dashboards to track spending and identify unexpected costs.
- Review Pricing Updates: LLM pricing can change frequently.
- Compress Data: For audio, use efficient formats and sampling rates. For text, be concise.
- Consider Asynchronous Processing: For non-real-time tasks, asynchronous APIs might be cheaper or handle larger loads better.
Disclaimer:
This calculator provides estimates based on publicly available pricing data (primarily referencing data up to May 2025 from various sources, subject to frequent changes) and user inputs. Actual LLM API costs can vary significantly. This tool is for guidance and planning purposes only and does not guarantee specific results. Always refer to the official LLM provider websites for the most current and accurate pricing information. All trademarks are the property of their respective owners.