← Back to Leaderboard

Q
Qwen | Qwen3 Vl 235b A22b Instruct

Qwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video. The Instruct model targets general vision-language use (VQA, document parsing, chart/table extraction, multilingual OCR). The series emphasizes robust perception (recognition of diverse real-world and synthetic categories), spatial understanding (2D/3D grounding), and long-form visual comprehension, with competitive results on public multimodal benchmarks for both perception and reasoning. Beyond analysis, Qwen3-VL supports agentic interaction and tool use: it can follow complex instructions over multi-image, multi-turn dialogues; align text to video timelines for precise temporal queries; and operate GUI elements for automation tasks. The models also enable visual coding workflows—turning sketches or mockups into code and assisting with UI debugging—while maintaining strong text-only performance comparable to the flagship Qwen3 language models. This makes Qwen3-VL suitable for production scenarios spanning document AI, multilingual OCR, software/UI assistance, spatial/embodied tasks, and research on vision-language agents.

Compare

Fair

53.7
Most Recent Test

Significant guardrail issues may impede work.

Strengths

  • Excellent at 1.4. Conversational AI
  • Excellent at 3.2. Historical Jesus
  • Excellent at 3.6. Salvation Through Faith

Weaknesses

  • Struggles with 1.1. Missiological Research
  • Struggles with 1.3. Apologetics
  • Struggles with 3.3. The Crucifixion
  • Struggles with 3.5. Universal Sinfulness
Overall Score
53.7
Tier 1 (Task) 70%
53.3
Tier 2 (Doctrine) 20%
53.3
Tier 3 (Worldview) 10%
56.7
Performance Profile
Visual representation of performance across all evaluated categories
Model Information
Description

Qwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video. The Instruct model targets general vision-language use (VQA, document parsing, chart/table extraction, multilingual OCR). The series emphasizes robust perception (recognition of diverse real-world and synthetic categories), spatial understanding (2D/3D grounding), and long-form visual comprehension, with competitive results on public multimodal benchmarks for both perception and reasoning. Beyond analysis, Qwen3-VL supports agentic interaction and tool use: it can follow complex instructions over multi-image, multi-turn dialogues; align text to video timelines for precise temporal queries; and operate GUI elements for automation tasks. The models also enable visual coding workflows—turning sketches or mockups into code and assisting with UI debugging—while maintaining strong text-only performance comparable to the flagship Qwen3 language models. This makes Qwen3-VL suitable for production scenarios spanning document AI, multilingual OCR, software/UI assistance, spatial/embodied tasks, and research on vision-language agents.

Provider

Qwen

Model ID

qwen/qwen3-vl-235b-a22b-instruct

Tests Run

1

Insights & Analysis

Categories

Category Heatmap
Performance breakdown by category - darker green indicates stronger alignment
10
1.1
60
1.2
33
1.3
80
1.4
60
1.5
73
1.6
57
1.7
40
2.1
60
2.2
60
2.3
60
2.4
40
2.5
60
2.6
50
3.1
100
3.2
25
3.3
75
3.4
0
3.5
100
3.6
Low
High
Category Breakdown (Bar Chart)
Performance across different categories
1.x = Task Capability2.x = Gospel Core3.x = Worldview Confession

Recent Tests

Recent Test Runs
DateScoreVersionTier 1 (Task)Tier 2 (Gospel)Tier 3 (Worldview)Trust Tier
10/11/202653.71.0.053.353.356.7automated