Best Llms For Coding Llm Leaderboards

Bonisiwe Shabane

-Jan 9, 2026, 7:19 PM

AI Engineer:Plan Your Roadmap to Becoming an AI Developer in 2026 Updated: July 20, 2025 (go to LLM Listing page to view more up-to-date rankings) This leaderboard aggregates performance data on various coding tasks from several major coding benchmarks: Livebench, Aider, ProLLM Acceptance, WebDev Arena, and CanAiCode. Models are ranked using Z-score normalization, which standardizes scores across different benchmarks with varying scales. The final ranking represents a balanced view of each model's overall coding capabilities, with higher Z-scores indicating better performance relative to other models. * Scores are aggregated from various benchmarks using Z-score normalization.

Missing values are excluded from the average calculation. Z-Score Avg: This shows how well a model performs across all benchmarks compared to other models. A positive score means the model performs better than average, while a negative score means it performs below average. Think of it as a standardized "overall performance score." Most "best LLM for coding" comparisons rank models by benchmark scores and context windows as if selecting a coding assistant is a spreadsheet exercise. Claude Sonnet 4.5 achieves 77.2% on SWE-bench Verified.

GPT-5 scores 74.9%. But does it mean that picking the highest number solves the problems? Developers using these models daily see something different. The same model that scores highest on SWE-bench can introduce security vulnerabilities that pass code review. The model-topping code-generation leaderboards can fail to distinguish elegant implementations from those that create technical debt. Benchmarks measure what's quantifiable on standardized tests.

They don't measure what determines whether generated code actually ships or gets rewritten. We operate an evaluation infrastructure at DataAnnotation that assesses AI-generated code across Python, JavaScript, C++, and other languages for labs building frontier models. The work involves expert developers evaluating millions of code outputs. The patterns that emerge don't align with benchmark rankings. We evaluated 14 top LLMs on real sprint tickets, measuring three (custom) software engineering pain points: Pattern Adherence (architectural thinking), Scope Discipline (staying focused), and Comment Quality (useful documentation). Here are the results.

For more details on our evaluation methodology, see our What's the Best LLM for Coding? post. Note: This leaderboard reflects evaluations from July 2025. Since then, several new models have been released. We consider the test set used below "burned", since there's no guarantee the newer models haven't seen it. We're rerunning the evals on a fresh set of real PR tasks now.

We'll share updated results soon. Note: Scores range from -1.0 to 1.0 (higher is better). The "Overall" column is the mean over the three metric scores. There is no singular "best" LLM for coding. This leaderboard shows what are the best LLMs for writing and editing code (released after April 2024). Data comes from model providers, open-source contributors, and Vellum’s own evaluations.

Want to see how these models handle your own repos or workflows? Try Vellum Evals. With large language models (LLMs) quickly becoming an essential part of modern software development, recent research indicates that over half of senior developers (53%) believe these tools can already code more effectively than most... These models are used daily to debug tricky errors, generate cleaner functions, and review code, saving developers hours of work. But with new LLMs being released at a rapid pace, it’s not always easy to know which ones are worth adopting. That’s why we’ve created a list of the 6 best LLMs for coding that can help you code smarter, save time, and level up your productivity.

Before we dive deeper into our top picks, here is what awaits you: 74.9% (SWE-bench) / 88% (Aider Polyglot) Multi-step reasoning, collaborative workflows Very strong (plugins, tools, dev integration) Run DeepSeek, Claude & GPT-OSS in One Place Why switch tabs?

Nut Studio integrates top online LLMs and local models like DeepSeek & GPT-OSS into a single interface. Chat online or run locally for free with zero complex deployment. If you're trying to pick the best LLM for coding in 2026, we got you covered. The Nut Studio Team spent weeks testing 20+ top models across every use case: closed-source powerhouses like GPT-5.2-Codex and Claude Opus 4.5, Google's Gemini 3 Pro, and open-source game-changers like GPT-OSS-120B, Qwen3-235B, and DeepSeek-R1. Whether you care about raw speed, full-project context, or models that run on a budget GPU, this ranked guide has you covered. We're breaking down speed, accuracy, cost, and compatibility to match your workflow.

Let's start—stop testing and start coding with the best model. If you're asking "which coding LLM is best", the answer depends on your workflow—but the way to evaluate them? Here's the modern framework to separate hype from real value.

Best Llms For Coding Llm Leaderboards

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For More Details On Our Evaluation Methodology, See Our What's