What LLM should you use for AI coding?

2026-07-15 · Source: Refactoring · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

This article provides a framework for selecting Large Language Models (LLMs) for coding tasks, emphasizing that the choice involves a nuanced balance of accuracy, speed, and cost. It highlights that these three dimensions are not orthogonal, as higher accuracy might reduce overall token usage, and faster models, even if less accurate, can lead to lower total costs through quicker iteration. The analysis cautions against over-reliance on public benchmarks like those for Fable 5, GPT 5.6, GLM 5.2, Grok 4.5, and DeepSeek 4 Flash, noting that real-world team situations and codebase discipline significantly impact performance. It suggests that many open models can achieve 80% of frontier model accuracy at 1/10th the cost. The discussion also introduces concepts like model routing for varied task difficulty and strongly advises against self-hosting LLMs due to cloud-hosted models' superior cost-effectiveness.

Key takeaway

For AI Engineers evaluating LLMs for coding, you should prioritize a balanced assessment of accuracy, speed, and cost rather than relying solely on public benchmarks. Your team's specific codebase discipline and task requirements dictate optimal model choice. Consider implementing model routing to assign tasks to different LLMs based on their complexity, potentially leveraging cost-effective open models like GLM 5.2 or Grok 4.5 for 80% accuracy at 1/10th the price of frontier models. Avoid self-hosting, as cloud solutions generally offer better value.

Key insights

Effective LLM selection for coding balances accuracy, speed, and cost, recognizing public benchmarks are insufficient and real-world context is key.

Principles

• Optimize for fast feedback loops over perfect output.
• Accuracy and cost are not linearly related.
• Open models offer 80% accuracy at 1/10th cost.

In practice

• Consider GLM 5.2 or Grok 4.5 for coding.
• Route tasks to models based on difficulty.
• Prefer cloud-hosted LLMs over self-hosting.

Topics

• LLM Selection
• AI Coding
• Model Evaluation
• Cost Optimization
• Model Routing
• Open-weight Models

Code references

• antirez/ds4

Best for: AI Engineer, Machine Learning Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Refactoring.