With the rise of large models, the token-based pricing model has gradually been accepted. But why is this the chosen pricing method? This article will delve into the essence of tokens, their role in large models, and the rationale, advantages, and future trends of token-based pricing for reference.
If you have used large model products like ChatGPT or Wenxin Yiyan, you may have noticed their unique pricing method—charging based on the number of tokens. This is entirely different from traditional software pricing models based on usage frequency or time.
Why are large models so “calculative” about tokens? What exactly are tokens?
01
What Are Tokens?
In large language models, a token refers to a small portion of the input text, which could be a word, a character, or part of a word. Different models may define and process tokens differently, but the fundamental principle remains the same: the model segments text into smaller units for processing and understanding.
✅ Breaking Down Text Like LEGO Bricks
Tokens are not simply “characters” or “words”; they are the smallest units through which large models understand text.
- Chinese: 1 Chinese character ≈ 1.5-2 tokens (as word combinations need to be considered)
- English: 1 word ≈ 1-3 tokens (e.g., “ChatGPT” is split into “Chat” + “GPT”)
- Special symbols: Punctuation marks and spaces may each count as a separate token.
✅ Why Must Text Be Split into Tokens?
Humans read text as a whole, but AI can only process numbers. Tokens act as a “bridge” that converts text into numerical codes. Each token corresponds to a sequence of numbers (e.g., 你 = 1024, 好 = 2048), making it easier for AI to compute.
02
Why Charge Based on Tokens?
Running large models is highly computationally expensive. Token-based pricing allows for more precise control of resource usage, making it a more transparent and fair pricing method.
✅ Costs Scale with Tokens
- Computational power consumption: Processing a 100-token query is 10 times more complex than a 10-token one, requiring more GPU power.
- Memory usage: When generating responses, AI needs to remember previous tokens (similar to writing an essay while recalling previous sentences). More tokens mean greater memory load.
- Response time: The more tokens, the longer AI takes to “think,” increasing server queue times.
✅ Fair “Pay-as-You-Use” Model
Traditional subscription models (e.g., monthly fees) often result in light users subsidizing heavy users. Token-based pricing ensures that occasional users (who look up information occasionally) don’t have to cover the costs for power users (who generate large volumes of text daily).
✅ Sustainable Business Model
Training large models is incredibly expensive (GPT-4 reportedly cost around $100 million). Token-based pricing allows companies to adjust resources based on actual usage, avoiding financial losses and continuously optimizing models.
03
How Is Token-Based Pricing Different from Traditional API Pricing?
Although DeepSeek also operates via API calls, this is a technical mechanism rather than a pricing model. Previously, API calls were typically charged per request—each call had a fixed cost.
- Traditional API pricing: Like “selling rice noodles by the bowl,” where each bowl has a fixed price.
- Token-based pricing: Like “selling Wagyu beef by weight,” where you pay based on how much you consume.
04
Are There Other Pricing Models Besides API and Token-Based Pricing?
Yes, but each has its pros and cons:
✅ Subscription Model (Monthly/Annual Plans)
- Best for: High-frequency users
- Downside: Companies may lose money if users excessively exploit the service.
✅ Time-Based Pricing (e.g., $1 per minute)
- Pros: Simple and straightforward
- Cons: Unfair—processing 100 words and 1000 words takes different amounts of time.
✅ Tiered Pricing (Basic/Pro Plans)
- Pros: Suitable for well-defined use cases
- Cons: Cannot cover long-tail demand.
05
Why Does Token-Based Pricing Win?
It best reflects actual costs while allowing users to flexibly control their budgets (e.g., setting a monthly token limit).
06
Tokens: The “Hard Currency” of the AI World
- Nature of tokens: The “work unit” for text processing, directly tied to AI’s computational costs.
- Pricing logic: Pay for the resources you use, avoiding the inefficiencies of “all-you-can-eat” models.
- Future trends: As models improve, the per-token cost may decrease, but the pricing model is unlikely to change significantly.
Next time you use AI, pay attention to your input length—every penny you spend is paying for these “text particles”!
07
Questions and Answers
Q: Right now, we can use many large models for free. Who is actually paying for token-based pricing?
A: Although some large models are free to use, token-based pricing is mainly charged by the service providers. These providers are companies or institutions that develop, train, and deploy large models, offering them as a service for various applications such as text generation and natural language processing.
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