Claude Code Leads AI Coding Benchmark, Open Models Show Strong Gains

January 2026

In the rapidly evolving landscape of AI-powered software engineering, a new benchmark snapshot reveals a highly competitive field where proprietary models maintain a narrow lead, while open-source alternatives are closing the gap at a remarkable pace. According to data from Nebius AI's SWE-rebench leaderboard for January 2026, Anthropic's Claude Code (based on Opus 4.6) currently leads the pack, but the margins at the top are razor-thin, signaling an intensifying race for coding supremacy.

The Benchmark: Real-World GitHub Tasks

The evaluation, conducted by researchers at Nebius, tested 48 fresh GitHub pull request tasks—all created in December 2025—using the standard SWE-bench methodology. This approach requires AI models to read actual PR issues, edit codebases accordingly, run tests, and ensure the full test suite passes. Unlike synthetic coding challenges, this benchmark reflects real-world software maintenance and development scenarios, making it a particularly rigorous test of practical coding ability.

According to Anton from Nebius, who shared the results on the LocalLLaMA subreddit, "The setup is standard SWE-bench: models read real PR issues, edit code, run tests, and must make the full suite pass." This methodology has become an industry standard for evaluating coding assistants beyond simple code completion, testing their ability to understand context, implement fixes, and navigate complex codebases.

The Frontier: A Three-Way Tie at the Top

The January results reveal an exceptionally tight competition among the leading proprietary models. Claude Code (Opus 4.6) achieved a 52.9% resolved rate—the highest single score—and also posted the best pass@5 rate of 70.8%, indicating strong performance when allowed multiple attempts.

However, the race remains extremely close. Both the standard Claude Opus 4.6 and OpenAI's gpt-5.2-xhigh configuration followed immediately behind with identical 51.7% resolved rates. Perhaps more surprisingly, gpt-5.2-medium performed remarkably close to these frontier models at 51.0%, suggesting that OpenAI's mid-tier offering provides exceptional value for performance.

"The top tier is extremely tight," noted the benchmark report, highlighting how incremental improvements have become the norm in this mature phase of AI coding assistant development.

The Open-Source Challenge

Perhaps the most significant trend emerging from the data is the strengthening performance of open and openly-licensed models. Kimi K2 Thinking led this category with a 43.8% resolved rate, followed closely by GLM-5 at 42.1% and Qwen3-Coder-Next at 40.0%.

These results demonstrate that open models are now performing at approximately 80-85% of the capability level of the leading proprietary systems—a substantial narrowing of the gap compared to benchmarks from just a year ago. The performance suggests that organizations prioritizing data privacy, customization, or cost control now have increasingly viable alternatives to closed API-based services.

Cost-Performance Tradeoffs and Variant Analysis

The benchmark also revealed interesting dynamics within model families. MiniMax's M2.5 model continued to demonstrate strong performance at 39.6% while maintaining its position as "one of the cheapest options," according to the report. This highlights the growing importance of cost-efficiency considerations in enterprise adoption.

Notably, the data showed a "clear gap" between different Kimi variants, with K2 Thinking (43.8%) significantly outperforming K2.5 (37.9%). This variance suggests that architectural choices and training approaches within the same model family can yield substantially different outcomes on complex coding tasks.

Meanwhile, newer smaller and "flash" variants designed for efficiency—such as GLM-4.7 Flash and gpt-5-mini-medium—occupied the 25–31% performance range. These models appear to trade approximately 40-50% of the top models' capability for significantly reduced computational requirements and cost, creating distinct market segments for different use cases.

Industry Implications and Future Outlook

The January 2026 snapshot arrives at a critical juncture for AI-assisted software development. With leading models now successfully resolving over half of real GitHub PR tasks—and achieving 70%+ success rates with multiple attempts—the technology is transitioning from experimental assistant to production-ready tool.

The compressed performance gap between proprietary and open models suggests increased competition that may drive faster innovation and more favorable pricing. Additionally, the emergence of distinct performance tiers allows organizations to match models to specific needs: frontier models for complex development tasks, open models for privacy-sensitive environments, and efficient variants for high-volume, lower-complexity work.

As the field advances, benchmarks like SWE-rebench will continue to play a crucial role in providing transparent, reproducible evaluations of AI coding capabilities. The January results not only document current state-of-the-art but also hint at an increasingly diversified and competitive marketplace for AI software engineering tools in the year ahead.

Benchmark data sourced from Nebius AI's SWE-rebench leaderboard for January 2026, as reported on the LocalLLaMA subreddit. Results reflect performance on 48 fresh GitHub PR tasks using standard SWE-bench methodology.