reasoning benchmark
FrontierCode 1.1 evaluates whether coding-agent changes are mergeable using unit tests, maintainer-defined rubrics, and verifiers.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAN | Score53.50% | Percentile100.00% | Participants17 | EvidenceB | Evaluated |
| Rank02 | ModelAN | Score53.40% | Percentile93.75% | Participants17 | EvidenceB | Evaluated |
| Rank03 | ModelOP | Score53.30% | Percentile87.50% | Participants17 | EvidenceC | Evaluated |
| Rank04 | ModelOP | Score47.50% | Percentile81.25% | Participants17 | EvidenceB | Evaluated |
| Rank05 | ModelAN | Score46.50% | Percentile75.00% | Participants17 | EvidenceB | Evaluated |
| Rank06 | ModelGO | Score43.60% | Percentile68.75% | Participants17 | EvidenceC | Evaluated |
| Rank07 | ModelOP | Score43.00% | Percentile62.50% | Participants17 | EvidenceB | Evaluated |
| Rank08 | ModelAN | Score42.70% | Percentile56.25% | Participants17 | EvidenceB | Evaluated |
| Rank09 | ModelXA | Score42.40% | Percentile50.00% | Participants17 | EvidenceB | Evaluated |
| Rank10 | ModelOP | Score41.30% | Percentile43.75% | Participants17 | EvidenceB | Evaluated |
| Rank11 | ModelOP | Score39.80% | Percentile37.50% | Participants17 | EvidenceB | Evaluated |
| Rank12 | ModelAN | Score38.50% | Percentile31.25% | Participants17 | EvidenceB | Evaluated |
| Rank13 | ModelMA | Score30.10% | Percentile25.00% | Participants17 | EvidenceB | Evaluated |
| Rank14 | ModelZA | Score24.50% | Percentile18.75% | Participants17 | EvidenceB | Evaluated |
| Rank15 | ModelDE | Score17.60% | Percentile12.50% | Participants17 | EvidenceB | Evaluated |
| Rank16 | ModelMI | Score14.70% | Percentile6.25% | Participants17 | EvidenceB | Evaluated |
| Rank17 | ModelAC | Score10.20% | Percentile0.00% | Participants17 | EvidenceB | Evaluated |
The leading models and scores on this benchmark.
A closer view of the leading scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
What FrontierCode 1.1 measures and how its scores work.
FrontierCode 1.1 is a reasoning benchmark for evaluating coding-agent changes.
It measures a Score reported as a ratio; runs flagged for unfair internet use receive a zero score.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about FrontierCode 1.1.
Claude Fable 5 is currently ranked first with 53.50%.
The current leaders are Claude Fable 5 (53.50%), Claude Opus 5 (53.40%), and GPT-6 Astra (53.30%).
GPT-5.6 Luna has the lowest matched official input price at $0.20 input / $1.2 output per 1M tokens.
The fastest matched records are GPT-5.5 (134.94 tok/s via OpenAI), Gemini 3.7 Flash (117.86 tok/s via Google), and GPT-5.6 Terra (99.35 tok/s via OpenAI).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures a Score reported as a ratio; runs flagged for unfair internet use receive a zero score.
Yes. Higher values rank better for this benchmark.
17 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.
Ranking basisThis frontiercode 1.1 AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Claude Fable 5 currently leads FrontierCode 1.1 with 53.50%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.