General Agents Benchmark

VITA-Bench

VITA-Bench - agentic benchmark. Avg subdomain scores; using claude-4-sonnet as judger.

Category

General Agents

Score type

percent

Scale

100%

higher is better

Models tested

14

What is VITA-Bench?

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

What does it test?

VITA-Bench 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: VITA-Bench - agentic benchmark. Avg subdomain scores; using claude-4-sonnet as judger.

Frequently asked questions

What is the VITA-Bench benchmark?

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

What does VITA-Bench test?

VITA-Bench 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 VITA-Bench 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 VITA-Bench 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/vita-bench/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 14 models tested

Reset
# Model Score Date Source Verified
1 Gemma4-31B-it
Google
43%
16.04.2026 Qwen Team (official model card - comparison) Unverified
2 Qwen3.5-27B
Qwen Team (Alibaba Cloud)
41.8%
16.04.2026 Qwen Team (official model card - comparison) Unverified
3 Gemma4-26B-A4B
Google
36.9%
16.04.2026 Qwen Team (official model card - comparison) Unverified
4 Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
35.6%
16.04.2026 Qwen Team (official model card) Verified
5 Qwen3.5-122B-A10B
Qwen Team (Alibaba Cloud)
33.6%
01.02.2026 Report Verified
6 Qwen3-235B-A22B
Qwen Team (Alibaba Cloud)
31.6%
01.02.2026 Report Unverified
7 Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
29.1%
16.04.2026 Qwen Team (official model card - comparison) Unverified
8 Spark-X2.5-4B
SparkLLM (XHToken)
25.2%
— Report Verified
9 Qwen3.5-4B
Qwen
22%
01.02.2026 Report Verified
10 Gemma4-12B
Google
12.4%
— Report Verified
11 Spark-X2.5-1.7B
SparkLLM (XHToken)
8.3%
— Report Verified
12 Qwen3.5-2B
Qwen Team (Alibaba Cloud)
5.2%
— Report Verified
13 Gemma4-E4B
Google
4.8%
— Report Verified
14 Gemma4-E2B
Google
4.4%
— Report Verified

What now?

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