I Ran 10 Coding AIs Through Real Client Work — Here's the Bill

说明与用途

一名自由职业者对10种LLM进行真实客户任务基准测试，发现DeepSeek V4 Flash和Qwen3‑Coder‑30B在常规工作中提供最佳性价比，优质模型仅用于安全审查。

问题

Freelance developers need to choose cost‑effective LLMs for client work, balancing quality and token cost.

结果

Using DeepSeek V4 Flash and Qwen3‑Coder‑30B for routine coding tasks delivers the best quality‑per‑dollar, while premium models are reserved for security reviews, improving profitability and billing accuracy.

工作流

• 1. Identify 10 available, affordable models and list their per‑million output token cost.
• 2. Select five representative coding tasks that match typical client work.
• 3. Prompt each model with the same task and record output token count and cost.
• 4. Score each output on correctness, quality, documentation, and edge‑case handling (1–10).
• 5. Calculate value as score divided by cost per million tokens.
• 6. Rank models by value to identify the most cost‑effective options.
• 7. Update workflow: use low‑cost models for routine tasks, premium models for security reviews or detailed explanations.
• 8. Track token usage per day to forecast monthly costs and adjust model usage accordingly.

证据

DeepSeek V4 Flash scored 8.7 with an output cost of $0.25/M, yielding a value of 34.8.

来源

Qwen3‑Coder‑30B achieved a score of 8.8 for $0.35/M, value 25.1.

来源

DeepSeek‑R1 found 9 security issues versus 6 for cheaper models.

来源