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
- Why We Built Our Own Background Agentbuilders.ramp.com
- FrontierCode 1.1 | Cognitioncognition.com
- Benchmarking Coding Agents on Databricks’ Multi-Million Line Codebasedatabricks.com
- FrontierSWEfrontierswe.com
- Senior SWE-Benchsenior-swe-bench.snorkel.ai
- FrontierSmith: Synthesizing Open-Ended Coding Problems at Scale | alphaXivalphaxiv.org
- Quantifying infrastructure noise in agentic coding evals \ Anthropicanthropic.com
- Coding Models Are Doing Too Much | whnrehiew.github.io
- Coding vs thinking — Paradigm 3paradigm3.org
- FrontierSWEfrontierswe.com
- SWE-1.7: Frontier Intelligence at a Fraction of the Costcognition.com