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In-depth analysis of API pricing strategies from OpenAI, Anthropic, Google and other major LLM providers. Learn about Standard, Batch, Realtime API, and Prompt Caching to choose the optimal approach and control costs.
LLM API pricing has evolved from simple per-token billing to a multi-dimensional tiered pricing system. Understanding these modes is crucial for cost control and performance optimization. The main pricing modes include:
Real-time response, balanced latency and cost
Async processing, 50% cost reduction
Ultra-low latency, voice interaction
Reuse prompts, save up to 90%
Standard mode is the most basic way to call LLM APIs, where requests are processed in real-time and results are returned immediately. This is the default choice for most applications, providing a good balance between latency and cost. Input and output tokens are billed separately, with output tokens typically costing 2-4x more than input.
Batch API, introduced by OpenAI in April 2024, is an asynchronous processing mode that allows developers to submit multiple requests at once, with the system completing processing within 24 hours. This mode is ideal for large-scale data processing tasks that are not latency-sensitive.
Batch API offers a 50% price discount for asynchronous tasks like data analysis, content summarization, and bulk translation. Results are guaranteed within 24 hours.
Package all API requests into a single JSONL file
Upload via Files API, then create a Batch job
System processes using idle capacity within 24 hours
Download result file containing all responses
Realtime API is a dedicated interface designed for voice conversations and low-latency interactions. It supports end-to-end Speech-to-Speech processing without the need for STT/TTS conversion, significantly reducing latency. This is the preferred solution for building voice assistants and real-time translation applications.
Prompt caching is a cost optimization technique that caches reusable prompt prefixes. When subsequent requests reuse cached content, they only pay a minimal read fee. Both Anthropic Claude and OpenAI support this feature, which can save up to 90% on input costs and reduce latency by 85%.
Prompt caching works by saving the "attention states" that the model builds when processing a prompt, avoiding recalculation from scratch. For applications with extensive system instructions, documents, or code, this can bring significant cost and latency improvements.
First request processes full prompt and caches prefix, slightly higher than standard input cost
Subsequent cache hits reuse computed results, only 10% of standard cost
Anthropic defaults to 5 min (optional 1 hour), OpenAI auto-manages based on usage
| Type | Price Multiplier | Example (Claude 3.5 Sonnet) |
|---|---|---|
| Standard Input | 1.0x | $3.00 / 1M tokens |
| Cache Write (5min) | 1.25x | $3.75 / 1M tokens |
| Cache Write (1hour) | 2.0x | $6.00 / 1M tokens |
| Cache Read | 0.1x | $0.30 / 1M tokens |
| Pricing Mode | Latency | Cost | Best For |
|---|---|---|---|
| Standard | Medium | Standard | Chatbots, real-time generation |
| Batch | High (≤24h) | Low (-50%) | Data analysis, bulk translation |
| Realtime | Very Low | High | Voice assistants, real-time translation |
| Prompt Cache | Low | Very Low (-90%) | Repeated prompts, long documents |
Real-time interaction (chat, coding assistant): Choose Standard mode for best UX
Batch data processing (analysis, translation, summarization): Use Batch API, save 50%
Voice conversation apps: Use Realtime API for lowest latency
Repetitive prompts (fixed system prompts): Enable caching, reduce input costs by 90%
Cost-sensitive projects: Combine Batch API + Prompt Caching + appropriate model size