## Horizontal Bar Chart: Normalized Total GPU Compute Time for 100 Questions
### Overview
The chart compares GPU compute times (in GPU-hours) for 100 questions across various AI models. It uses a horizontal bar format with color-coded categories: green for models under the exam time threshold (2.5 GPU-hours), red for models exceeding it, and gray for non-multimodal models. A red dashed line at 2.5 GPU-hours marks the exam time benchmark.
### Components/Axes
- **X-axis**: "Normalized Total GPU Compute Time for 100 Questions" (0–20.8 GPU-hours)
- **Y-axis**: List of AI models (e.g., HyperCLOVAX-SEED-Text-Instruct-1.5B, EXAONE-4.0-1.2B, Qwen3 variants, Llama3, InternVL, Phi, A.X, VARCO-VISION, c4ai-command, gpt-oss, command-a-reasoning)
- **Legend**:
- Green: GPU-hours ≤ Exam Time (150 minutes ≈ 2.5 GPU-hours)
- Red: GPU-hours > Exam Time
- Gray: Non-multimodal
- **Red Dashed Line**: Exam time threshold (2.5 GPU-hours)
### Detailed Analysis
- **Green Bars** (≤ 2.5 GPU-hours):
- Most models fall into this category, including early versions of Qwen3, Llama3, InternVL, and Phi.
- Example: HyperCLOVAX-SEED-Text-Instruct-1.5B (0.00 GPU-hours), EXAONE-4.0-1.2B (0.00 GPU-hours), Qwen3-30B-A3B-Instruct-2507 (0.00 GPU-hours).
- **Red Bars** (> 2.5 GPU-hours):
- High-complexity models like Qwen3-VL-32B-Thinking (3.8 GPU-hours), Qwen3-VL-235B-A22B-Thinking (3.1 GPU-hours), and Qwen3-32B (2.4 GPU-hours).
- **Gray Bars** (Non-multimodal):
- All non-multimodal models (e.g., command-a-reasoning-08-2025) are under the exam time threshold, with the longest at 1.5 GPU-hours (Phi-4-mini-reasoning).
### Key Observations
1. **Efficiency Dominance**: 85% of models (green bars) complete 100 questions within the exam time threshold.
2. **Outliers**:
- Qwen3-VL-32B-Thinking (3.8 GPU-hours) and Qwen3-VL-235B-A22B-Thinking (3.1 GPU-hours) exceed the threshold by 52% and 24%, respectively.
- Non-multimodal command-a-reasoning-08-2025 (1.5 GPU-hours) is the longest gray bar but still under the threshold.
3. **Threshold Significance**: The red dashed line at 2.5 GPU-hours acts as a critical benchmark, separating efficient models from resource-intensive ones.
### Interpretation
The chart reveals that most AI models are optimized to handle 100 questions within the exam time constraint (2.5 GPU-hours). However, high-complexity multimodal models like Qwen3-VL variants require significantly more compute time, suggesting trade-offs between capability and efficiency. The non-multimodal command-a-reasoning model, while slower than some multimodal peers, remains under the threshold, indicating that non-multimodal architectures may offer a balance between performance and resource usage. The extreme outlier (Qwen3-VL-32B-Thinking) highlights the computational demands of advanced reasoning tasks, raising questions about scalability for real-time applications.
