Artificial Analysis launches its FrontierCode 1.1 coding leaderboard
Artificial Analysis launched its independent FrontierCode 1.1 leaderboard on October 12, 2026, adding a way to compare whether AI coding models produce changes that satisfy repository maintainers’ requirements.
Cognition introduced FrontierCode on June 8 and released version 1.1 on July 7. October brings Artificial Analysis’s separate evaluation.
What the leaderboard measures
The evaluation page describes 100 private tasks from the benchmark’s Main set. Artificial Analysis runs each task three times in its Stirrup agent harness, with a limit of 250 turns per attempt. Keeping tasks private is intended to reduce benchmark contamination.
When checked on October 13, Claude Opus 5.5 with maximum reasoning and default fallback led with a 52.2% score. OpenAI’s Sol model, version 6.1, at maximum reasoning followed at 49.1%, alongside Claude Fable 5.1 with maximum reasoning and default fallback at 49.0%.
A score measures more than a pass
Artificial Analysis’s scoring methodology gives an attempt zero if it fails any blocking requirement or is flagged for looking up a solution. An attempt that clears every blocking requirement receives between 70% and 100%, depending on the remaining quality criteria. Scores are averaged across attempts and then across equally weighted tasks.
The launch report put the leading Opus configuration’s pass rate at 58%. That measures attempts clearing all blocking checks. Its rounded 52% score also reflects quality criteria.
For a development team, the distinction matters. A model can satisfy the conditions needed to accept a change while still leaving room for better style or maintainability. Treating the score as a simple success percentage loses that information.
Code quality includes restraint
Cognition’s benchmark design considers correctness, regressions, build and style checks, test quality, scope and readability. Maintainers from 36 repositories helped define those expectations. One check runs the agent’s new tests against the original code to see whether they actually detect the problem. Other checks restrict unnecessary edits.
The July revision also addressed internet use. Documentation lookup remains allowed, while retrieving an existing solution can invalidate a run. Cognition says the revision relaxed 75 overly strict blocking criteria after reviewing more than 1,000. That history is a reason to match benchmark versions before comparing scores.
Price and setup belong beside the score
At launch, Artificial Analysis reported average task costs in U.S. dollars of $0.81 for Sol at maximum reasoning and $10.15 for the leading Opus configuration. Those figures accompanied rounded scores of 49% and 52%, respectively. Three extra points cost more than twelve times as much.
The cost charts use token pricing, including input, output and caching categories where available. Their time estimates cover decoding and exclude overhead and the wait for the first token. Budget separately for review and integration work when applying these comparisons to a project.
The published methodology also uses an AI judge for rubric review. Results therefore reflect a particular harness, grading process and model configuration. A team evaluating a coding assistant should compare its own tasks under the settings and tools it intends to use.
A useful shortlist still needs local review
ByteForward’s takeaway is to use the ranking to choose candidates for a practical trial. Check whether proposed changes stay within scope, whether tests catch the original problem and how much review the patch requires. Track the total effort needed to reach an accepted change.
This report uses published findings from Artificial Analysis and Cognition. ByteForward did not run the benchmark. The live leaderboard can change as evaluations are added.
Illustrative coding photograph by SACHINRAJ CP. CC0 via the WordPress Photo Directory.
