ES, MX, US: Calculadora Costos LLM Avanzada - Presupuestos API Gemini, OpenAI, Anthropic

Calculadora que proporciona un análisis avanzado de costos LLM para las API de Gemini, OpenAI y Anthropic, dirigida a desarrolladores en ES, MX, US. Esta Calculadora de Precios de API LLM está diseñada para programadores y arquitectos de software que trabajan con lenguajes como Python (Django, Flask), JavaScript (Express.js), Java (Spring Boot), C#, Go y Ruby on Rails. Ayuda en la planificación financiera meticulosa para el desarrollo de APPS móviles (multiplataforma o nativas), desarrollo WEB sofisticado (microservicios, SPAs) y desarrollo de software empresarial robusto. Los equipos de diseño UI/UX también pueden aprovecharla para comprender el costo de incorporar funciones de IA. Evalúa diferentes niveles de precios, descuentos por volumen y capacidades de los modelos (por ejemplo, GPT-4 vs Claude 3 Opus vs Gemini Pro) para alinearlos con los requisitos técnicos y las restricciones presupuestarias de tu proyecto. Toma decisiones estratégicas para tus operaciones en España, México y Estados Unidos, asegurando la eficiencia de costos y maximizando el valor derivado de los modelos de lenguaje grandes.

Comprehensive LLM API Pricing Calculator

Estimate your Large Language Model API usage costs across various providers and models.

1. Basic Configuration

Currency

API Provider

LLM Model (Text/Chat/Multimodal)

Calculation Basis i

Per Request

Total Monthly Usage

2. Text/Chat Model Usage Parameters

Number of Requests (Monthly) i

Average Input Tokens per Request i

Average Output Tokens per Request i

Input Token Price (per 1M tokens) i

Output Token Price (per 1M tokens) i

3. Image Input Usage Parameters (Multimodal)

Number of Input Images i

Price per Input Image i

4. Image Generation Usage Parameters

Number of Images to Generate i

Image Generation Model/Quality

Price per Image i

5. Embedding Model Usage Parameters

Embedding Model

Number of Embedding Chunks/Requests i

Average Tokens per Chunk/Request i

Embedding Price (per 1M tokens) i

6. Audio Model Usage Parameters (e.g., Speech-to-Text, Text-to-Speech)

Audio Model

Total Audio Duration (Input) / Characters (Output) i

Unit

Audio Price (per Minute/Second/Million Chars) i

7. Fine-Tuning Costs Parameters

Training Data Size (Millions of Tokens) i

Training Price (per 1M tokens / per hour / etc.) i

Fine-Tuned Model Usage (Input applicable if 'Monthly' selected for Calculation Basis):

Input Tokens for FT Model (Monthly, 1M) i

Output Tokens for FT Model (Monthly, 1M) i

FT Model Input Price (per 1M tokens) i

FT Model Output Price (per 1M tokens) i

Estimated Costs

API Provider

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.