Artificial Analysis Coding Agent Index

Artificial Analysis Coding Agent Index is a benchmark for an aggregated index computed from several underlying benchmarks. The official description says: "Aggregator index (Artificial Analysis) for autonomous agentic coding performance; combines scores across coding-agent evals and reports tasks completed (%)". Below we describe in simple words what benchmarks of this type generally examine.

Artificial Analysis Coding Agent Index measures an aggregated index computed from several underlying benchmarks. In detail, it checks:

• a weighted synthesis of many individual evaluations
• coverage (how many different skills the model performed on)
• recency (favouring newly released models)
• none of the weaknesses that a single narrow benchmark would hide

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.

Official methodology note: Aggregator index (Artificial Analysis) for autonomous agentic coding performance; combines scores across coding-agent evals and reports tasks completed (%). Chart on the ML4 card: Mistral Large 4 48%, GLM-5.3 49%, DeepSeek V4.1 Flash 1017 46%, Kimi K3 41%, Muse Spark Open 26%, Beam 1.0 20%.

What is the Artificial Analysis Coding Agent Index benchmark?

Artificial Analysis Coding Agent Index is a benchmark for an aggregated index computed from several underlying benchmarks. The official description says: 'Aggregator index (Artificial Analysis) for autonomous agentic coding performance; combines scores across coding-agent evals and reports tasks completed (%)'. Below we describe in simple words what benchmarks of this type generally examine.

What does Artificial Analysis Coding Agent Index test?

Artificial Analysis Coding Agent Index mainly measures an aggregated index computed from several underlying benchmarks. Concretely, it checks: a weighted synthesis of many individual evaluations; coverage (how many different skills the model performed on); recency (favouring newly released models).

How is the Artificial Analysis Coding Agent Index score calculated?

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.

Where do I get Artificial Analysis Coding Agent Index data and scores?

The official source is https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B/blob/main/images/coding_index.png. The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/aa-coding-agent-index/scores) or pull it directly from the Open LLM Radar API with any HTTP client.