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.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Benchmark dataset
The question/task data itself — download and evaluate locally
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 14 models tested
| # | 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 |