LLM API Token Cost: Claude vs GPT vs DeepSeek
Benchmarking LLM API input/output rates, Prompt Caching discounts, tokenizer efficiency, and simulating monthly expenditures.
As Large Language Models (LLMs) power production software applications, controlling API token costs has become a top priority for engineering teams and indie builders.
AI model providers (Anthropic, OpenAI, Google, DeepSeek) structure pricing across multiple variables: Input Token Rates, Output Token Rates, Prompt Caching Discounts, and Context Window Boundaries. This article breaks down current LLM pricing dynamics and introduces how to simulate API expenses using the interactive PocketKit LLM Pricing Comparator.
1. 2026 LLM API Pricing Matrix Overview
Below is a cost snapshot across major model APIs (rates in USD per 1 Million Tokens):
| Model Name | Input Price / 1M | Output Price / 1M | Cached Input / 1M | Context Window |
|---|---|---|---|---|
| DeepSeek V3 | $0.14 | $0.28 | $0.014 | 64,000 |
| Claude 3.5 Sonnet | $3.00 | $15.00 | $0.30 | 200,000 |
| GPT-4o | $2.50 | $10.00 | $1.25 | 128,000 |
| Gemini 1.5 Pro | $1.25 | $5.00 | $0.31 | 2,097,152 |
| GPT-4o mini | $0.15 | $0.60 | $0.075 | 128,000 |
2. Three Key Strategies to Reduce Token Costs
2.1 Leverage Prompt Caching
For agent workflows with large system prompts or lengthy context documents, enabling Prompt Caching reduces repeated input costs by 75% to 90%. For instance, Claude 3.5 Sonnet drops from $3.00/1M down to $0.30/1M for cached prefix tokens.
2.2 Understand Tokenizer Efficiency (cl100k vs o200k)
Different tokenizers encode the same prompt into varying token lengths:
- OpenAI's o200k_base tokenizer improves multilingual token compression, producing 20% to 30% fewer tokens on non-English texts compared to older cl100k_base tokenizers.
- Verify prompt token counts prior to deployment using the free PocketKit Token Counter.
2.3 Implement Tiered Model Routing
Split application logic into simple vs complex task pipelines:
- Route simple classification and formatting tasks to DeepSeek V3 or GPT-4o mini.
- Reserve Claude 3.5 Sonnet or GPT-4o for intricate code synthesis and multi-step reasoning.
3. Simulating Monthly API Costs on PocketKit
Use the interactive PocketKit LLM Pricing Comparator to model daily call volumes and average input/output prompt lengths. The calculator forecasts total monthly expenditures across providers with visual comparisons.