FrontierCode 1.1 Main
Cognition's 100-task software-engineering benchmark for whether coding agents produce mergeable, production-quality pull requests, scored for correctness, tests, scope, style, and maintainability through maintainer-authored rubrics.
How we show FrontierCode 1.1
The snapshot mirrors Cognition's current FrontierCode 1.1 Main table captured on September 4, 2026 snapshot. Main contains 100 of the 150 private tasks. Cognition reports each model at its best-performing published reasoning effort.
We keep FrontierCode display-only. Each result combines a model with an agent harness, and the private tasks cannot be independently rerun from the public artifact, so these scores do not enter the weighted model rankings.
Snapshot
Main score on FrontierCode 1.1 Main — September 4, 2026 snapshot
We mirror the published main score view for FrontierCode 1.1 Main. Claude Fable 5 leads the public snapshot at 53.5%, followed by Claude Opus 4.8 (46.5%) and GPT-5.5 (43.0%). We do not use these results to rank models overall.
Claude Fable 5
Anthropic
claude-code
Claude Opus 4.8
Anthropic
claude-code
GPT-5.5
OpenAI
codex
8 modelsCodingCurrentDisplay onlyUpdated September 4, 2026 snapshot
Main score table (8 models)
ScoreThe published FrontierCode 1.1 Main snapshot places Claude Fable 5 first at 53.5%. The third row is 10.5 points behind. The broader top-10 range is 29.2 points, so the table still separates the published systems.
8 models have been evaluated on FrontierCode 1.1 Main. The benchmark falls in the Coding category. This category carries a 20% weight in BenchLM.ai's overall scoring system. FrontierCode 1.1 Main is currently displayed for reference but excluded from the scoring formula, so it does not directly affect overall rankings.
About FrontierCode 1.1 Main
Year
2026
Tasks
100 private Main tasks (150 in Extended)
Format
Repository task completion with maintainer rubrics
Difficulty
Frontier coding-agent quality
FrontierCode 1.1 Main uses 100 of the benchmark's 150 private software-engineering tasks. The leaderboard reports the best-performing published reasoning effort for each model-agent row. We keep the results display-only because each row combines a model with an agent harness and the private tasks cannot be independently rerun from the public artifact.
BenchLM freshness & provenance
Version
FrontierCode 1.1 Main
Refresh cadence
Rolling
Staleness state
Current
Question availability
Private tasks with public aggregate results
BenchLM uses freshness metadata to decide whether a benchmark should still be treated as a strong differentiator, a benchmark to watch, or a display-only reference. For the full scoring policy, see the BenchLM methodology page.
FAQ
What does FrontierCode 1.1 Main measure?
Cognition's 100-task software-engineering benchmark for whether coding agents produce mergeable, production-quality pull requests, scored for correctness, tests, scope, style, and maintainability through maintainer-authored rubrics.
Which model leads the published FrontierCode 1.1 Main snapshot?
Claude Fable 5 currently leads the published FrontierCode 1.1 Main snapshot with 53.5% main score. BenchLM shows this benchmark for display only and does not use it in overall rankings.
How many models are evaluated on FrontierCode 1.1 Main?
The September 4, 2026 snapshot snapshot contains 8 AI models.
Know when it’s worth switching models
The model to choose, the cheaper alternative, and the release we would wait on.
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