Deep-Dive Experiment: Benchmarking Code Generation from GPT-4 to Llama 3

In the rapidly evolving world of artificial intelligence, choosing the right Large Language Model (LLM) for a specific task can feel like trying to hit a moving target. New models are released constantly, each claiming superior performance. For developers and businesses relying on AI for critical functions like code generation, a subjective "feel" for a model's capability is not enough. You need objective, repeatable, and standardized data to make informed decisions.

This is where AI benchmarking comes in. By testing models against a standardized set of problems and metrics, we can cut through the marketing hype and get a clear picture of their true performance.

Today, we're conducting a deep-dive experiment to compare the code-generation capabilities of four industry-leading models: Claude 3 Opus, GPT-4, Llama 3 70B, and Gemini Pro.

The Experiment: Setting a Standard for AI Performance Testing

To ensure a fair and meaningful comparison, a standardized evaluation framework is essential. Here’s how we structured our test:

• The Task: Code Generation. We're testing the models' ability to write correct, functional Python code based on a descriptive docstring.
• The Dataset: HumanEval. This is the industry-standard benchmark for code generation. It consists of 164 programming problems that test for language comprehension, algorithmic thinking, and functional correctness.
• The Metric: pass@k. This metric measures how many problems a model can solve.
  • pass@1: The percentage of problems where the model's first generated solution is correct. This measures accuracy and reliability.
  • pass@10: The percentage of problems where at least one of the ten generated solutions is correct. This measures the model's overall problem-solving breadth.

By using a well-defined dataset and clear metrics, we can create a level playing field for our model contenders and produce results that are both reproducible and directly comparable.

The Results: A Head-to-Head Code Generation Showdown

We ran all four models against the HumanEval benchmark. The results provide a fascinating snapshot of the current state of AI-powered code generation.

Analysis of the LLM Performance

1. Claude 3 Opus: The New Frontrunner
Anthropic's latest flagship model, Claude 3 Opus, takes the top spot in this benchmark. With a pass@1 score of 74.4%, it demonstrates a remarkable ability to generate correct code on its first attempt. Its high pass@10 score of 92.0% further solidifies its position as a powerful and reliable tool for developers.

2. GPT-4: Still a Powerhouse
OpenAI's GPT-4 remains a formidable contender, coming in a very close second. Its pass@1 of 72.9% shows it is still one of the most accurate models on the market. The narrow gap between it and Opus highlights the intense competition at the top tier of LLM performance.

3. Llama 3 70B: The Open-Source Champion
Meta's Llama 3 70B delivers an impressive performance, especially for a widely available open-source model. While its pass@1 of 68.0% is slightly below the proprietary leaders, it proves that the open-source community is rapidly closing the gap. This makes it an incredibly compelling option for teams seeking more control and customizability.

4. Gemini Pro: A Strong Competitor
Google's Gemini Pro rounds out our test with a solid performance. It showcases strong capabilities that are highly competitive with other models in the top echelon, making it another excellent choice for a variety of code generation tasks.

The Challenge: From Results to Reproducibility

These results are illuminating, but running this kind of A/B model evaluation yourself is a significant engineering challenge. It requires:

• Accessing multiple model APIs.
• Setting up and managing evaluation datasets.
• Implementing complex scoring logic for metrics like pass@k.
• Ensuring the entire pipeline is consistent for every run.

This is a time-consuming distraction from your core product development.

This is the exact problem Benchmarks.do was built to solve.

Our platform provides AI performance testing as a simple, API-driven service. The results in this blog post were generated using our agentic workflow platform, which handles the entire benchmarking process for you.

Instead of building complex testing infrastructure, you can make a single API request:

{
  "benchmark": {
    "name": "Code Generation Showdown - HumanEval",
    "tasks": [
      {
        "task_name": "code-generation",
        "dataset": "humaneval",
        "metrics": ["pass@1", "pass@10"]
      }
    ],
    "models": [
      "claude-3-opus",
      "gpt-4",
      "llama-3-70b",
      "gemini-pro"
    ]
  }
}

Our service executes the benchmark and returns a clean, shareable report with the comparative results, just like the one that informed this article.

Evaluate, Compare, and Optimize with Confidence

Choosing the right AI model shouldn't be a guessing game. Standardized LLM performance testing provides the objective data needed to optimize your applications, control costs, and build with confidence. Whether you're comparing industry giants like GPT-4 and Claude 3, evaluating open-source alternatives like Llama 3, or even testing your own fine-tuned models, a data-driven approach is key.

Ready to stop guessing and start measuring? Visit Benchmarks.do to see how our simple APIs can standardize your AI model evaluation process and give you the clarity you need to build better products.