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.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Model scores 6 models tested
| # | 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 |