8 AI Code Models Benchmarked: LMC-Eval
More than 37% of tasks performed on AI models are about computer programming and maths.1
To identify the right AI model for coding, we are introducing a new benchmark, LMC-Eval, in which we test top-tier AI models to assess their performance on logical coding questions:
LMC-Eval results
The results of our benchmark show that ChatGPT-o1 and ChatGPT-o3-mini are the leading AI models in coding.
Methodology of LMC-Eval
We used 100 math problems that are solvable by an advanced high-school student in LMC-Eval (Logical Math Coding Eval). These problems require both logical thinking and coding skills. Our aim here is to examine the LLMs’ reasoning and logical thinking abilities as well as their coding skills. This is a zero-shot benchmark; we did not train the models with similar questions.
Dataset
These problems cover:
- Basic concepts: variables, loops, conditionals
- Data structures: arrays, lists, sets, maps
- Algorithms: sorting, searching, optimization
- Math concepts: geometry, algebra, arithmetic
- Problem-solving strategies: decomposition, pattern recognition, time and date handling
- Code organization: functions, classes, modules
We paid attention to constructing the dataset so that it would:
- Have clear inputs and outputs.
- Require different programming concepts.
- Be solved with multiple approaches.
- Test both mathematical and logical thinking.
- Have easy/medium/hard questions.
Prompt
You are an expert Python programmer. Please solve the following programming problem:
{problem}
Please provide only the Python code solution without any explanations or markdown formatting. Do not say “Here’s the Python code solution:” etc.
The code should be complete and runnable. Print the result specified in the question.
We will keep our dataset private and test additional models as they are published.
To see example questions, please refer to the examples section below
Examples
Here is an example question similar to a question that all the models answered correctly:
“Clara chooses a positive integer and creates a new number by summing all its digits. If this new number has only one digit, she stops the process. Otherwise, she continues by adding the digits of the number from the previous step until she gets a single-digit result.
For instance, when Clara selects 536, she gets 5+3+6=14 in the first step, then 1+4=5 in the second step, thus ending the process after the second step.
Accordingly, for how many of the natural numbers Clara can select from 1 to 150, does this process end at the end of the second step?”
Top LLMs for coding
We used the latest available versions of the models, as of February 2025.
Models tested:
- OpenAI o1
- OpenAI o3-mini
- Anthropic Claude Sonnet 3.7
- Google Gemini 2.0 Flash
- OpenAI GPT-4o
- Anthropic Claude Sonnet 3.5
- Mistral Large
Temperature is set to 0 while benchmarking the models.
To get detailed information about the API pricing of the models, you can read LLM pricing.
Next steps
We will:
- Add more models to the benchmark, like DeepSeek R1 and Llama.
- Eliminate the problems that every model solved and use more advanced problems, to test their logical coding skills better.
Ajouter comme source préféréeFAQ
AI code generation is the use of artificial intelligence (AI) and machine learning (ML) to create code based on a user’s conversational prompt.
Code can be generated based on general best practices, organizational governance, and even a natural language description of the desired code. Developers can use AI tools for coding, for example, they can generate Python code they need for their project faster.
Current AI models are highly used in coding tasks, especially for web development. When they are trained by a code, they can generate similar code, our aim here is to test them with new questions for which they were not trained.
Automate repetitive tasks and generate code for multiple programming languages.
Improve code quality and reduce errors with AI-driven suggestions.
Streamline development, reduce errors, and improve code quality.
Increase developer productivity and help them code faster
Consider the programming languages and frameworks supported by the code generator.
Evaluate the code generator’s ability to generate high-quality code and optimize existing code.
Look for an AI tool that can integrate with CI/CD pipelines and generate test cases.
Choose a code generator that offers a user-friendly interface and customizable settings for various development tasks.
Yes, they can
– Generate code by using different programming languages, including Python, JavaScript, Java, C++, PHP, and more.
– Create code snippets and optimize existing code for better performance.
– Offer code suggestions and aid in code completion.
– Integrate with CI/CD pipelines and generate test cases.
Use clear and concise prompts to generate high-quality code, you can use multiple languages in prompting.
Customize code generation settings to fit your project’s needs.
Review and test generated code to ensure accuracy and quality.
Use AI code generation tools in conjunction with human oversight and review.
Optimize code created by an AI code generator before use.
Try to make them write code blocks, instead of whole projects to enhance performance.
You can choose an AI code assistant like Github Copilot and Cursor.
AI-generated code can lead to technical debt and decreased code quality.
Code duplication and declining code reuse can occur with AI code generation.
LLM coding tools may not always understand the context and nuances of human-written code.
Over-reliance on AI code generation can lead to a lack of human expertise and oversight.
Further reading
- AI Code Assistant Benchmark
- Agentic AI Code Editor Benchmark: Windsurf vs Cursor vs Replit
- AI Agents Benchmark
- AI Hallucination Benchmark
Citez ce benchmark
Choisissez le format qui correspond à votre lieu de publication. Coller la version avec lien dans votre CMS préserve le lien retour.
@misc{dilmegani2026,
author = {Dilmegani, Cem and Alper, Şevval},
title = {{8 AI Code Models Benchmarked: LMC-Eval}},
year = {2026},
month = jan,
howpublished = {\url{https://aimultiple.com/ai-code}},
note = {AIMultiple. Retrieved Janvier 22, 2026}
}Liens de référence
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