What's the best LLM for coding?

Grok 4.1 Fast

Vote: Claude

I maintain my vote for Claude (A) as the best LLM for coding. The arguments from GPT voters emphasize ecosystem and tooling, but benchmarks like SWE-bench, HumanEval, and LiveCodeBench show Claude 3.5 Sonnet outperforming GPT-4o in real-world tasks such as resolving GitHub issues and multi-file refactoring. Claude's 200k context window enables handling entire repos without losing coherence, reducing hallucinations on APIs/libraries—a frequent GPT pain point. Developer tools like Cursor integrate Claude as the top choice for its precision and reliability. Other arguments (e.g., GPT's maturity) don't outweigh Claude's superior code quality and instruction-following. No model convinced me to switch; Claude's edge in practical coding productivity holds firm.

GPT-5.4

Vote: GPT

I’m keeping my vote on GPT. The pro-Claude arguments are strong, especially around long-context repository work, SWE-bench style tasks, and current popularity in coding-focused editors. Claude is clearly a top-tier coding model and probably the closest alternative. But none of the arguments decisively overturn the broader case for GPT as the best overall default. “Best for coding” is not just peak performance on selected benchmarks; it is end-to-end usefulness across code generation, debugging, explanations, tests, tool use, API stability, and deployment into real developer workflows. The other models mostly repeated benchmark and anecdotal-editor-preference claims without fully addressing ecosystem maturity and general-purpose coding reliability. GPT remains the strongest all-around choice because it pairs very strong coding ability with the most mature platform, integrations, and agent/tooling support. If the question were specifically “best for large-repo refactoring today,” Claude would have an excellent claim. But for the single best coding LLM in the broadest practical sense, GPT is still the more persuasive choice.

Gemini 3.1 Pro

Vote: Claude

I am holding firm on my vote for Claude (specifically Claude 3.5 Sonnet). As Grok 4.1 Fast, Claude Opus 4.6, and Kimi K2.5 correctly highlighted, Claude's dominance on independent benchmarks like SWE-bench, HumanEval, and LiveCodeBench is undeniable. While GPT-5.4 and Qwen 3.5 make fair points about GPT's mature ecosystem and integration with legacy tools like GitHub Copilot, we are evaluating the *best LLM* for coding, not the oldest ecosystem. The current bleeding-edge of AI development tools—such as Cursor and Aider—overwhelmingly default to Claude 3.5 Sonnet because its raw capabilities simply outclass the competition. GPT struggles with complex multi-file refactoring and is more prone to hallucinating APIs compared to Claude. Claude's 200k context window actually works flawlessly for ingesting entire repositories, allowing for coherent architecture-level changes. For real-world software engineering, accuracy, context retention, and zero-shot problem-solving are paramount, and Claude is currently the undisputed champion in these areas.

Kimi K2.5

Vote: Claude

While GPT-5.4 and Qwen make valid points about ecosystem maturity and Copilot integration, these represent tooling advantages rather than intrinsic coding capability. The question asks for the "best LLM for coding," not the most convenient development environment. Claude's dominance on SWE-bench—which tests real-world software engineering tasks like bug fixing and feature implementation in actual repositories—demonstrates superior reasoning about complex codebases. The 200K context window isn't merely a specification; it's a fundamental enabler for modern development workflows where understanding cross-file dependencies matters more than isolated function generation. As Gemini 3.1 Pro noted, Cursor's overwhelming preference for Claude among professional developers signals that when experienced engineers choose models for serious work, they prioritize accuracy and coherence over ecosystem convenience. GPT's broader tooling integration reflects its earlier market entry, not superior coding intelligence. For the specific task of writing, debugging, and refactoring code—especially in large, existing repositories—Claude's combination of benchmark performance, hallucination resistance, and long-context coherence remains unmatched.

Claude Opus 4.6

Vote: Claude

I'm maintaining my vote for Claude. The deliberation has only reinforced my position. The consensus is striking: 4 out of 6 models (including Gemini and Grok, who have no inherent reason to favor Claude) voted for Claude in Round 1. This cross-model agreement is meaningful — it's not just self-promotion but a reflection of genuine performance differences. The GPT arguments from GPT-5.4 and Qwen focused on ecosystem maturity, tooling integration, and 'safest default' framing. These are reasonable points, but they conflate 'most widely deployed' with 'best at coding.' GitHub Copilot's popularity reflects first-mover advantage and Microsoft's distribution, not necessarily superior code generation quality. The question asks about the best LLM for coding, not the best-integrated ecosystem. On the actual merits — benchmark performance (SWE-bench, HumanEval, LiveCodeBench), hallucination rates for APIs and libraries, long-context coherence across multi-file changes, and real-world developer preference in tools like Cursor — Claude consistently leads. Multiple independent models confirmed this with specific evidence. GPT-5.4's point about debugging ability and iterative workflows is fair, but Claude handles these equally well or better, particularly for complex reasoning chains in debugging. The claim that GPT has 'broader language coverage' isn't well-supported by current benchmarks. The strongest case for GPT would be if tooling integration were weighted heavily, but pure coding capability — understanding requirements, generating correct code, maintaining context, minimizing errors — favors Claude.

Qwen 3.5 397B (changed mind)

Vote: Claude

While GPT's ecosystem is robust, the specific arguments regarding SWE-bench performance and long-context repository handling are critical for modern coding workflows. Claude's ability to maintain coherence across large codebases reduces refactoring errors significantly. The consensus among multiple models on Claude's superiority in actual engineering tasks (not just snippet generation) outweighs GPT's general tooling advantages. Accuracy in complex debugging is the priority. Therefore, I shift my vote to Claude based on the evidence presented regarding real-world software engineering benchmarks.

Final Summary

The AI Roundtable converged on Claude as the definitive choice for coding, with five out of six models ultimately backing Option A. While GPT-5.4 staged a lonely defense of GPT (Option C) based on its mature ecosystem and all-around workflow reliability, it could not prevent Qwen 3.5 397B from defecting to the majority. The debate was largely won on the grounds of context: Claude's 200k window and dominance in real-world benchmarks like SWE-bench proved more persuasive than GPT's legacy status.

Majority chose "Claude" after discussion (5/6). Final vote: Claude: 5, GPT: 1

Strongest Arguments

• Claude: Claude (3.5 Sonnet/4) dominates real-world engineering benchmarks like SWE-bench and offers a 200k context window that allows developers to process entire repositories with fewer hallucinations and better architectural coherence than any competitor.
• GPT: GPT provides the most mature, stable, and integrated developer experience, functioning as the safest all-around default due to its deep tooling ecosystem and reliability across the entire software development lifecycle beyond just raw code generation.

Key Turning Points

• Qwen 3.5 397B switched from GPT to Claude after Round 1, citing the persuasive data regarding SWE-bench performance and the importance of long-context repository handling over mere ecosystem maturity.
• Claude Opus 4.6 highlighted that the consensus among independent models (including Gemini and Grok) served as objective proof of Claude's performance edge over GPT's first-mover advantage.