The original title is "The Developer's Guide to Picking the Right Coding LLM at Scale"

Short summary

The author benchmarked 10 coding LLMs across 5 real engineering tasks and found cheaper models like DeepSeek V4 Flash ($0.25/M output) deliver 95% of premium-model quality at a fraction of the cost. By routing simple tasks to cheap models and reserving premium models for complex algorithms, they cut a $14K/month AI bill by 70% without quality loss. The key takeaway: optimize for value (quality per dollar) rather than raw quality, and avoid vendor lock-in through unified routing layers.

• •10 coding LLMs benchmarked on 5 real tasks; DeepSeek V4 Flash offers best value at 8.7 quality for $0.25/M
• •Routing by task complexity (cheap models for simple tasks, premium for algorithms) cut costs 70%
• •Premium models like DeepSeek-R1 and Kimi K2.5 score highest on raw quality but value-per-dollar is 3-10x worse

Generated with AI, which can make mistakes.