code benchmark
Surge AI Coding Quality is a blinded human evaluation of model coding outputs by professional annotators using a 1-5 point scale.
Updated Oct 23, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMA | Score3.74 points | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and 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.
Ranking basisThis surge ai coding quality 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
Mistral Large 4 currently leads Surge AI Coding Quality with 3.74 points. 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.
What Surge AI Coding Quality measures and how its scores work.
A code benchmark that evaluates model coding outputs through human ratings with model identities hidden.
It measures coding output quality using a Score in points on a 1-5 scale.
Scores are shown in points. 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 Surge AI Coding Quality.
Mistral Large 4 is currently ranked first with 3.74 points.
The current leaders are Mistral Large 4 (3.74 points).
Mistral Large 4 has the lowest matched official input price at $1.4 input / $4.2 output per 1M tokens.
No matched runtime record is currently available.
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures coding output quality using a Score in points on a 1-5 scale.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.