Large Model API Call Optimization Guide

Before optimization, we must first identify the two primary scenarios driving token consumption: redundant descriptions at the input stage and ineffective content at the output stage. For example, when a user asks, "I want to understand AI applications in healthcare—specifically those that assist doctors in diagnosis and data analysis. Could you elaborate?" the phrase "specifically the kind that assists doctors in diagnosis and data analysis" constitutes redundant repetition, consuming an extra 15-20 tokens. If the model defaults to generating a 500-word detailed explanation when the user only needs 200 words of core points, the excess 300 words (approximately 450 tokens) represent ineffective output.

Based on OpenAI GPT-4 Turbo pricing (input: $0.01/1K tokens, output: $0.03/1K tokens), reducing input and output token consumption by 500 tokens per call would yield monthly savings for 1,000 calls: (500 × 0.01 + 500 × 0.03) ÷ 1000 × 1000 = $20. This demonstrates that optimization fundamentally involves "precisely matching requirements and eliminating redundant information."

"I'm currently conducting market research on new energy vehicles and need global sales data for 2024, along with market shares of major brands like Tesla and BYD. Could you also briefly mention the reasons for their growth?"

"2024 global new energy vehicle sales figures, Tesla/BYD market share, and growth drivers (core data only)"

• Use "noun + verb" to clearly state requirements (e.g., "sales figures," "market share"), omitting introductory phrases (e.g., "I'm currently researching," "such as these");

• Use parentheses to specify constraints (e.g., "limited to core data," "within 200 words") to prevent model overinterpretation.

When requirements involve multidimensional information, replace natural language connectors with symbols like "numbering, semicolons," etc., to reduce the model's semantic parsing token consumption.

"I need to write a short article about coffee, covering its origin, main varieties, brewing methods, and ensuring the language is easy to understand for beginners."

"Coffee essay requirements: 1. Origins; 2. Primary varieties; 3. Brewing methods; Style: Easy to understand (beginner-friendly)"

In multi-turn dialogues, avoid repeating foundational context each time by simplifying input through "Anaphora + Supplementation."

User's first query: "Analyze the reasons for the decline in China's smartphone shipments in 2024". For a follow-up query adding "the impact of offline store closures," there's no need to repeat "China's smartphone shipments in 2024."

"Add: Include the impact of offline store closures on shipments", saving 50% tokens compared to a full restatement.

When calling the API, directly restrict the number of output tokens via the max_tokens parameter to prevent models from "over-expanding."

A user requires a 100-word (approx. 150 tokens) product description. Without parameter settings, the model might generate 300 words (approx. 450 tokens).

Reserve 10-20% extra tokens (e.g., set to 170 for 150 tokens needed) to prevent critical information from being truncated due to word count limits.

Clearly define the output format (e.g., list, table, bullet points) based on requirements to help the model focus on core information and minimize introductory text.

"Summarize the core conclusions of this report" (Model may generate paragraph-style responses, consuming 20-30 extra tokens)

"Summarize the report's core conclusions (limit to 3 points, presented as a numbered list)" (Model directly outputs "1.XXX; 2.XXX; 3.XXX", saving 30% tokens)

If a request involves multiple steps (e.g., "Write proposal + Revise details + Summarize"), avoid asking the model to generate the entire content at once. Instead, call it in phases, focusing on a single objective per stage.

Writing a "Campus Book Club Activity Proposal." If asked to "Write proposal including theme, process, and budget" all at once, the model might generate 800 words (approx. 1200 tokens);

1) First Round: "Campus Book Club Theme Suggestions (3 options, 10 characters max per suggestion)" (approx. 25 tokens);

2) Second Round: "Design a 3-step activity process based on Theme X (20 characters max per step)" (approx. 40 tokens);

3) Final round: "Create a budget under 500 yuan following the process (list 2 core expenditures)" (approx. 35 tokens);

Total consumption: only 100 tokens, saving 90% compared to a single call.

Before inputting prompts, use online tools or code libraries to pre-calculate token counts and avoid exceeding budgets.

• Online Tools: OpenAI Token Calculator, Tiktokenizer—enter text to instantly display token count and estimated costs;

• Code Tools: Python's tiktoken library for batch-calculating token consumption across multiple prompt segments.

For structured requests like data queries or formula calculations, replace natural language responses with the API's "function call" feature to significantly reduce output tokens.

Calculating "Total New Energy Vehicle Sales from January to March 2024" would consume 50-80 tokens if the model described the process verbally;

Function call optimization: Directly invoke the "data summation function," returning only the result (e.g., "12 million units") at 5-10 tokens.

Periodically export API call logs to identify high-token-consumption scenarios (e.g., "long text summarization," "multiple redundant dialogue rounds"), then implement targeted optimizations.

• Peak tokens per call (identify abnormal spikes);

• High-frequency request types (e.g., whether "market research"-type requests can be templatized);

• Proportion of ineffective outputs (e.g., whether "background introductions" dominate model responses).

If the prompt only states "New Energy Vehicle Analysis," the model will generate generic content, requiring a secondary call to supplement requirements and consuming extra tokens.

Different models have distinct segmentation rules (e.g., Claude segments longer texts more coarsely). Applying GPT optimization logic to Claude may cause token count discrepancies.

Setting max_tokens far below requirements (e.g., needing 200 tokens but setting to 100) causes the model to truncate critical information, necessitating re-calls.

The core of reducing token consumption lies not in "making the model output less," but in "ensuring every token serves the requirement." Through the four-step optimization approach of "streamlining input, controlling output, leveraging tools, and adapting to scenarios," you can maintain model output quality while reducing costs by 30%-70%. For enterprise users, combining batch call discounts and dedicated API key permissions can further amplify optimization results. As large model technology evolves, smarter "dynamic token allocation" features may emerge in the future. However, at this stage, manual optimization remains the key to cost control.