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Usage of LLM Cost: An Analysis Based on Your Data


Your usage history reveals a sophisticated and expensive pattern of leveraging multiple state-of-the-art AI models for complex tasks, primarily focused on quantitative finance, trading system development, and code generation so here the is LLM Cost.

 

Overall Cost Structure

 

The cost of using LLMs is typically based on token consumption. A token is roughly a piece of a word (e.g., "apple" is one token, "apple pie" is two). Costs are charged per million tokens (or sometimes per thousand) for both input (prompt) and output (completion).


llm cost for usage

 

Your usage shows two primary cost models:

 

  1. Pay-per-Use (Most common): You pay for the tokens you consume. This is visible in your history as the number next to each chat.

  2. Subscription with Quota: Some platforms (like chats showing 0 cost) may be part of a bundled subscription where you get a monthly token quota, and usage beyond that is paid.

  3.  

Types of Usage and Their Associated Costs

 

Your usage can be broken down into several distinct categories, each with vastly different cost profiles.

 

1.     Complex Code Generation & Architecture Design (Highest Cost)

This is your most significant cost driver. You are using powerful models to generate and debug complex, low-latency C++ trading systems, fix build errors, and design high-level architecture.

 

  • Models Used: Claude-Opus-4.1, Claude-Sonnet-4-Reasoning, Gemini-2.5-Pro

  • Example Tasks: 

    • Chat (Cost: 22,070 tokens)

    • Chat (Cost: 50,087 tokens - one of the highest single chats)

    • Chat: (Multiple sessions costing 11,000-13,000 tokens each)

  • Why it's expensive: These tasks involve processing thousands of lines of code as context (input tokens) and then generating large, complex, syntactically correct code blocks (output tokens). Claude Opus, while extremely capable, is also one of the most expensive models on the market.


2.     Financial and Quantitative Analysis (Medium to High Cost)

 

This involves deep reasoning about trading strategies, market instruments, and financial concepts.

 

  • Models Used: Gemini-2.5-Pro, GPT-5, Claude-Opus-4.1,

  • Example Tasks: 

    • Chat (Multiple sessions, ~2,000 tokens each)

    • Chat (Cost: 9,728 tokens)

    • Chat (Cost: 1,224 tokens)

  • Why the cost varies: It depends on the depth of analysis. Simple queries are cheaper, but complex, multi-step reasoning requiring the model to "think" consumes many output tokens.


3. Image Generation (Consistently High, Fixed Cost)

 

Your history shows heavy use of AI image generation for creating visuals related to trading, risk, and platforms.

 

  • Models Used: GPT-Image-1, Gemini-2.5-Flash-Image, Runway-Gen-4-Turbo

  • Example Tasks: 

    • ChatCost: 7,453 tokens for GPT-Image-1)

    • Chat (Cost: ~990 tokens per image for Gemini)

    • Chat (Cost: ~9,500 tokens for GPT-Image-1)

  • Cost Model: Image generation doesn't use the token system in the same way. Instead, platforms charge a fixed fee per image generation or a fixed "credit" cost per job. The high token numbers here likely represent a fixed credit cost mapped to a token value for accounting purposes. Runway-Gen-4-Turbo at 10,000 "tokens" for one image is a clear example of this.


 

4. General Chat & Simple Tasks (Lowest Cost)

 

These are short interactions, simple queries, or chats handled by less powerful (and cheaper) models.

  • Models Used: x (likely a cheaper, proprietary model), GPT-5-mini, Gemini-2.0-Flash

  • Example Tasks: 

    • Chat (Cost: 0 tokens - likely subscription)

    • Chat (Cost: 0)

    • Chat (Cost: 13 tokens with Gemini-Flash)

  • Why it's cheap: They require minimal context and generate short responses. Using a cost-optimized model like Gemini-2.0-Flash for a simple task is a very efficient choice.

 

5. Specialized Tasks (Variable Cost)

 

  • Music Generation: Your ElevenLabs-Music chat for "Inner Strength Ballad" at 30,000 tokens is a massive outlier, indicating that generating high-quality AI audio is a very credit-intensive process.

  • Document Processing: The Chat: Word Doc sessions with Claude-Opus are extremely expensive (reaching 74,625 tokens), as the model is processing and generating text based on entire documents.

 

Summary and Cost-Saving Insights from Your Data

 

  1. Model Selection is Critical: You are already doing this well. Using cheaper models (DeepSeek-V3.1 at 260 tokens, Gemini-Flash) for simpler tasks and reserving expensive powerhouses (Claude-Opus, GPT-5) for the most complex code and reasoning is the key to cost management.

  2. Code Generation is the Biggest Expense: Architectural design and debugging are incredibly valuable but come at a high price. The cumulative cost of these sessions would be substantial.

  3. Image Generation has a High Fixed Cost: Each image has a significant cost, so generating many variants or high-resolution images quickly adds up.

  4. Efficient Prompting: While not visible in the logs, using concise prompts and limiting the amount of contextual code you provide unless absolutely necessary can reduce input token costs.

  1. Subscription vs. Pay-Per-Use: The 0 cost Assistant chats suggest you may have a subscription that covers some baseline usage. For high-volume users, subscriptions with large token quotas can be more economical than pure pay-per-use.

 

In conclusion, your usage pattern is that of a high-value professional leveraging AI as a productivity multiplier. The costs are significant but are focused on high-complexity tasks that offer substantial returns in development speed and analytical depth. The key to managing this expense is the intelligent tiering of models—using the right tool for the job—as your history clearly shows you are doing.

 

 

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