LLMs for Coding in 2024: Price, Performance, and the Battle for the Best

The rapidly evolving landscape of Large Language Models (LLMs) for coding presents developers with a wealth of choices. This analysis compares top LLMs accessible via public APIs, focusing on their coding prowess as measured by benchmarks like HumanEval and real-world Elo scores. Whether you're building personal projects or integrating AI into your workflow, understanding these models' strengths and weaknesses is crucial for informed decision-making.

The Challenges of LLM Comparison:

Direct comparison is difficult due to frequent model updates (even minor ones significantly impact performance), the inherent stochasticity of LLMs leading to inconsistent results, and potential biases in benchmark design and reporting. This analysis represents a best-effort comparison based on currently available data.

Evaluation Metrics: HumanEval and Elo Scores:

This analysis utilizes two key metrics:

• HumanEval: A benchmark assessing code correctness and functionality based on given requirements. It measures code completion and problem-solving abilities.
• Elo Scores (Chatbot Arena - coding only): Derived from head-to-head LLM comparisons judged by humans. Higher Elo scores indicate superior relative performance. A 100-point difference suggests a ~64% win rate for the higher-rated model.

Performance Overview:

OpenAI's models consistently top both HumanEval and Elo rankings, showcasing superior coding capabilities. The o1-mini model surprisingly outperforms the larger o1 model in both metrics. Other companies' best models show comparable performance, though trailing OpenAI.

Benchmark vs. Real-World Performance Discrepancies:

A significant mismatch exists between HumanEval and Elo scores. Some models, like Mistral's Mistral Large, perform better on HumanEval than in real-world usage (potential overfitting), while others, such as Google's Gemini 1.5 Pro, show the opposite trend (underestimation in benchmarks). This highlights the limitations of relying solely on benchmarks. Alibaba and Mistral models often overfit benchmarks, while Google's models appear underrated due to their emphasis on fair evaluation. Meta models demonstrate a consistent balance between benchmark and real-world performance.

Balancing Performance and Price:

The Pareto front (optimal balance of performance and price) primarily features OpenAI (high performance) and Google (value for money) models. Meta's open-source Llama models, priced based on cloud provider averages, also show competitive value.

Additional Insights:

LLMs consistently improve in performance and decrease in cost. Proprietary models maintain dominance, although open-source models are catching up. Even minor updates significantly affect performance and/or pricing.

Conclusion:

The coding LLM landscape is dynamic. Developers should regularly assess the latest models, considering both performance and cost. Understanding the limitations of benchmarks and prioritizing diverse evaluation metrics is crucial for making informed choices. This analysis provides a snapshot of the current state, and continuous monitoring is essential to stay ahead in this rapidly evolving field.

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