General Agents Benchmark

Harvey Legal Agent

1,251 public tasks with Artificial Analysis's Stirrup harness; score is the all-pass rate over rubric criteria.

Category

General Agents

Score type

percent

Scale

100%

higher is better

Models tested

2

What is Harvey Legal Agent?

Harvey Legal Agent is a benchmark for how well a model acts as a general-purpose agent. The official description says: "1,251 public tasks with Artificial Analysis's Stirrup harness; score is the all-pass rate over rubric criteria". Below we describe in simple words what benchmarks of this type generally examine.

What does it test?

Harvey Legal Agent measures how well a model acts as a general-purpose agent. In detail, it checks:

  • solving long, multi-step tasks with many intermediate decisions
  • using tools correctly (browsers, terminals, APIs, calendars, other apps)
  • adapting when something unexpected happens mid-task
  • staying on goal over long task horizons instead of wandering off

How to read the score

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: 1,251 public tasks with Artificial Analysis's Stirrup harness; score is the all-pass rate over rubric criteria.

Frequently asked questions

What is the Harvey Legal Agent benchmark?

Harvey Legal Agent is a benchmark for how well a model acts as a general-purpose agent. The official description says: '1,251 public tasks with Artificial Analysis's Stirrup harness; score is the all-pass rate over rubric criteria'. Below we describe in simple words what benchmarks of this type generally examine.

What does Harvey Legal Agent test?

Harvey Legal Agent mainly measures how well a model acts as a general-purpose agent. Concretely, it checks: solving long, multi-step tasks with many intermediate decisions; using tools correctly (browsers, terminals, APIs, calendars, other apps); adapting when something unexpected happens mid-task.

How is the Harvey Legal Agent 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 Harvey Legal Agent data and scores?

The official source is https://huggingface.co/moonshotai/Kimi-K3.1-Pro. 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/harvey-legal-agent/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 2 models tested

Reset
# Model Score Date Source Verified
1 Kimi K3.1 Pro
Moonshot AI
23.7%
07.11.2026 Report Verified
2 DeepSeek-V4.1-Flash-1017
DeepSeek
21%
07.11.2026 Kimi K3.1 Pro model card comparison column (max effort) https://huggingface.co/moonshotai/Kimi-K3.1-Pro Unverified

What now?

You've seen how models score on Harvey Legal Agent — here's what to do next.