General Agents Benchmark

PinchBench

PinchBench - agentic benchmark.

Category

General Agents

Score type

percent

Scale

100%

higher is better

Models tested

6

What is PinchBench?

PinchBench is a benchmark for how well a model acts as a general-purpose agent. The official description says: "PinchBench - agentic benchmark". Below we describe in simple words what benchmarks of this type generally examine.

What does it test?

PinchBench 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: PinchBench - agentic benchmark.

Frequently asked questions

What is the PinchBench benchmark?

PinchBench is a benchmark for how well a model acts as a general-purpose agent. The official description says: 'PinchBench - agentic benchmark'. Below we describe in simple words what benchmarks of this type generally examine.

What does PinchBench test?

PinchBench 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 PinchBench 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 PinchBench data and scores?

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/pinchbench/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 6 models tested

Reset
# Model Score Date Source Verified
1 DeepSeek-V4-Flash
DeepSeek
91.3%
04.06.2026 Report Unverified
2 Kimi K2.6
Moonshot AI
90.2%
04.06.2026 Report Unverified
3 NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
NVIDIA
90%
04.06.2026 Report Verified
4 DeepSeek-V4-Pro
DeepSeek
88.6%
04.06.2026 Report Unverified
5 NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB
NVIDIA
87.82%
15.10.2026 Report Verified
6 LFM2.5-24B-A2B
LiquidAI
83.18%
13.10.2026 Report Verified

What now?

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