Introducing FrontierCode | Cognition
Today’s coding benchmarks have established that models can write correct code, but the question we should really be asking is: can models actually write good code?
Raising the bar from correctness to quality # Today’s coding benchmarks have established that models can write correct code. But as AI-generated code becomes the dominant path to production, correctness is now table stakes. The question that we should be asking is: can models actually write good code? We’re excited to introduce FrontierCode, a benchmark that measures how well models can truly meet the standards of high-quality production codebases. What sets us apart: Would the maintainer actually merge this PR? We’re the first benchmark to measure code mergeability. Our criteria assess end-to
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related reading
- Introducing FrontierCode | Cognitioncognition.com
- The open source AI coding agentopencode.ai
- FrontierCode 1.1 | Cognitioncognition.com
- Coding Models Are Doing Too Much | whnrehiew.github.io
- Composer2.pdfcursor.com
- Best practices for Claude Code - Claude Code Docsanthropic.com
- Benchmarking Coding Agents on Databricks’ Multi-Million Line Codebasedatabricks.com
- FrontierSWEfrontierswe.com
- [2203.07814] Competition-Level Code Generation with AlphaCodearxiv.org
- [2501.01257] CodeElo: Benchmarking Competition-level Code Generation of LLMs with Human-comparable Elo Ratingsarxiv.org
- Coding vs thinking — Paradigm 3paradigm3.org
- The Verification Horizon: No Silver Bullet for Coding Agent Rewardsarxiv.org