## Problem Solving with Model Comparison ### Overview The image presents a problem-solving scenario where two different models (GLM-Z1-Air and Light-R1-32B-DS) are used to solve a mathematical problem related to placing numbers on the vertices of a cube. The image shows the problem statement, the model selection dropdowns, a grid representing the problem space, and the model accuracy. It also includes input fields for sample numbers and indicates whether the model's answer was correct or incorrect, along with extracted values and output tokens. ### Components/Axes * **Header:** * "Problem Statement" * "Reference Answer" * **Problem Statement (Chinese):** * 对于正方体 ABCD - A1B1C1D1,将1,2,…,8分别放在正方体的八个顶点上,要求每一个面上的任意三个数之和均不小于10. 求不同放法的个数. * Translation: "For a cube ABCD - A1B1C1D1, place the numbers 1, 2, ..., 8 on the eight vertices of the cube, such that the sum of any three numbers on each face is not less than 10. Find the number of different placement methods." * **Model Selection:** * "Select Model 1" * Dropdown showing "GLM-Z1-Air (12.1%)" * "Select Model 2" * Dropdown showing "Light-R1-32B-DS (11.6%)" * **Problem Grid (Model 1):** * A 10x10 grid of cells numbered 0-99. Each cell contains a number and a percentage. * **Problem Grid (Model 2):** * A 10x10 grid of cells numbered 0-99. Each cell contains a number and a percentage. * **Model Accuracy:** * "Samples 8 - Model Accuracy: 0/8 = 0.0%" * "Samples 64 - Model Accuracy: 14/64 = 21.9%" * **Sample Selection:** * "Enter Sample Number (0 - 63)" - Input field * **Model Output:** * Model 1: "X Incorrect Extracted: $3360$ Output Tokens: 16133" * Model 2: "✓ Correct Extracted: $480$ Output Tokens: 12751" * **Footer (Chinese):** * <think>嗯,这个问题看起来有点挑战性,不过让我慢慢来思考一下。题目是说,把数字1到8分别放在正方体的八个顶点上,每个面上的任意三个数之和都不小于10。然后要找出不同的放法个数。首先,我需要明确正方体的结构,每个面有四个顶点,但题目里说的是每个面上的任意一个数之和都不小于10,也就是说,每个面的四个顶点中 * Translation: "<think> Hmm, this problem seems a bit challenging, but let me think about it slowly. The problem states that the numbers 1 to 8 are placed on the eight vertices of a cube, such that the sum of any three numbers on each face is not less than 10. Then, we need to find the number of different placement methods. First, I need to clarify the structure of the cube. Each face has four vertices, but the problem states that the sum of any three numbers on each face is not less than 10, which means that among the four vertices on each face..." ### Detailed Analysis or Content Details **Problem Grid (Model 1 - GLM-Z1-Air (12.1%)):** The grid consists of 100 cells, numbered 0 to 99. Each cell displays a percentage value, presumably representing the model's confidence or probability associated with that particular state or configuration. * Cell 0: 0% * Cell 1: 0% * Cell 2: 25% * Cell 3: 0% * Cell 4: 62% * Cell 5: 50% * Cell 6: 0% * Cell 7: 12% * Cell 8: 0% * Cell 9: 0% * Cell 10: 12% * Cell 11: 25% * Cell 12: 25% * Cell 13: 25% * Cell 14: 0% * Cell 15: 0% * Cell 16: 0% * Cell 17: 37% * Cell 18: 25% * Cell 19: 37% * Cell 20: 0% * Cell 21: 62% * Cell 22: 50% * Cell 23: 0% * Cell 24: 25% * Cell 25: 50% * Cell 26: 12% * Cell 27: 37% * Cell 28: 0% * Cell 29: 0% * Cell 30: 12% * Cell 31: 12% * Cell 32: 0% * Cell 33: 0% * Cell 34: 0% * Cell 35: 0% * Cell 36: 0% * Cell 37: 0% * Cell 38: 0% * Cell 39: 0% * Cell 40: 0% * Cell 41: 12% * Cell 42: 0% * Cell 43: 0% * Cell 44: 0% * Cell 45: 50% * Cell 46: 0% * Cell 47: 0% * Cell 48: 0% * Cell 49: 0% * Cell 50: 0% * Cell 51: 0% * Cell 52: 0% * Cell 53: 50% * Cell 54: 0% * Cell 55: 0% * Cell 56: 0% * Cell 57: 0% * Cell 58: 0% * Cell 59: 0% * Cell 60: 75% * Cell 61: 12% * Cell 62: 12% * Cell 63: 0% * Cell 64: 12% * Cell 65: 0% * Cell 66: 0% * Cell 67: 0% * Cell 68: 0% * Cell 69: 0% * Cell 70: 50% * Cell 71: 12% * Cell 72: 0% * Cell 73: 12% * Cell 74: 12% * Cell 75: 50% * Cell 76: 0% * Cell 77: 37% * Cell 78: 12% * Cell 79: 12% * Cell 80: 12% * Cell 81: 0% * Cell 82: 25% * Cell 83: 0% * Cell 84: 12% * Cell 85: 0% * Cell 86: 12% * Cell 87: 0% * Cell 88: 0% * Cell 89: 25% * Cell 90: 0% * Cell 91: 12% * Cell 92: 100% * Cell 93: 0% * Cell 94: 0% * Cell 95: 0% * Cell 96: 0% * Cell 97: 0% * Cell 98: 0% * Cell 99: 0% **Problem Grid (Model 2 - Light-R1-32B-DS (11.6%)):** The grid consists of 100 cells, numbered 0 to 99. Each cell displays a percentage value, presumably representing the model's confidence or probability associated with that particular state or configuration. * Cell 0: 0% * Cell 1: 0% * Cell 2: 25% * Cell 3: 1% * Cell 4: 9% * Cell 5: 12% * Cell 6: 0% * Cell 7: 0% * Cell 8: 0% * Cell 9: 1% * Cell 10: 17% * Cell 11: 12% * Cell 12: 32% * Cell 13: 81% * Cell 14: 0% * Cell 15: 4% * Cell 16: 0% * Cell 17: 29% * Cell 18: 4% * Cell 19: 1% * Cell 20: 0% * Cell 21: 18% * Cell 22: 39% * Cell 23: 0% * Cell 24: 42% * Cell 25: 29% * Cell 26: 18% * Cell 27: 34% * Cell 28: 12% * Cell 29: 0% * Cell 30: 34% * Cell 31: 21% * Cell 32: 21% * Cell 33: 0% * Cell 34: 0% * Cell 35: 0% * Cell 36: 6% * Cell 37: 0% * Cell 38: 0% * Cell 39: 0% * Cell 40: 4% * Cell 41: 4% * Cell 42: 0% * Cell 43: 4% * Cell 44: 0% * Cell 45: 31% * Cell 46: 1% * Cell 47: 0% * Cell 48: 0% * Cell 49: 18% * Cell 50: 0% * Cell 51: 1% * Cell 52: 0% * Cell 53: 6% * Cell 54: 0% * Cell 55: 0% * Cell 56: 0% * Cell 57: 1% * Cell 58: 20% * Cell 59: 0% * Cell 60: 20% * Cell 61: 0% * Cell 62: 1% * Cell 63: 34% * Cell 64: 12% * Cell 65: 0% * Cell 66: 4% * Cell 67: 6% * Cell 68: 9% * Cell 69: 0% * Cell 70: 62% * Cell 71: 6% * Cell 72: 0% * Cell 73: 62% * Cell 74: 9% * Cell 75: 53% * Cell 76: 10% * Cell 77: 70% * Cell 78: 18% * Cell 79: 7% * Cell 80: 0% * Cell 81: 0% * Cell 82: 21% * Cell 83: 4% * Cell 84: 3% * Cell 85: 9% * Cell 86: 25% * Cell 87: 1% * Cell 88: 0% * Cell 89: 0% * Cell 90: 4% * Cell 91: 54% * Cell 92: 0% * Cell 93: 18% * Cell 94: 15% * Cell 95: 0% * Cell 96: 4% * Cell 97: 25% * Cell 98: 0% * Cell 99: 0% **Model Accuracy:** * Model 1 (GLM-Z1-Air): 0 correct out of 8 samples (0.0% accuracy) * Model 2 (Light-R1-32B-DS): 14 correct out of 64 samples (21.9% accuracy) **Model Output Details:** * Model 1: Incorrect, Extracted: $3360, Output Tokens: 16133 * Model 2: Correct, Extracted: $480, Output Tokens: 12751 ### Key Observations * The problem involves placing numbers 1-8 on the vertices of a cube with a constraint on the sum of numbers on each face. * Two different models are being compared for their ability to solve this problem. * Model 2 (Light-R1-32B-DS) has a significantly higher accuracy (21.9%) compared to Model 1 (GLM-Z1-Air) (0.0%). * The "Problem Grid" likely represents the model's internal state or probability distribution over possible solutions. * The "Extracted" value likely represents the model's final answer or a related metric. * The "Output Tokens" value likely represents the complexity or length of the model's reasoning process. ### Interpretation The image demonstrates a comparison of two AI models in solving a combinatorial problem. The significant difference in accuracy suggests that Model 2 (Light-R1-32B-DS) is better suited for this type of problem than Model 1 (GLM-Z1-Air). The "Problem Grid" provides insight into how each model explores the solution space, with the percentages indicating the model's confidence in different configurations. The "Extracted" and "Output Tokens" values provide additional information about the model's performance and computational effort. The Chinese text in the footer indicates the user's thought process and understanding of the problem. The fact that Model 2's answer was correct, and Model 1's was incorrect, despite Model 1 using more tokens and a higher extracted value, suggests that Model 2 is more efficient and accurate in its reasoning.

\n ## Heatmap Comparison: Model Performance on Visual Reasoning Task ### Overview The image presents a comparison of two machine learning models (GLM-ZT-Air and Light-RT-32B-DS) on a visual reasoning task involving a 9x9 grid. The performance is visualized using heatmaps, where each cell represents a digit (0-8) and the color intensity indicates the model's confidence in predicting the correct digit. Accuracy scores for each model are also displayed, along with a bar chart showing the distribution of correct answer numbers. There is text in Chinese at the top of the image. ### Components/Axes * **Top Section:** Contains the problem statement in Chinese and dropdown menus for selecting models. * **Model Selection Dropdowns:** * "Select Model 1" - Currently set to "GLM-ZT-Air (12.1%)" * "Select Model 2" - Currently set to "Light-RT-32B-DS (11.6%)" * **Problem Grids (Heatmaps):** Two 9x9 grids, one for each model. * X-axis: Numbered 0-8 (representing the digits). * Y-axis: Numbered 0-8 (representing the digits). * Color Scale: Ranges from white (0% confidence) to dark green (100% confidence). * **Accuracy Scores:** Displayed below each grid. * "Samples B - Model Accuracy: 8.0%" * "Samples B - Model Accuracy: 14/21 = 66.7%" * "Samples 64 - Model Accuracy: 14/21 = 66.7%" * **Correct Answer Number Distribution:** A bar chart showing the frequency of each correct answer number (0-63). * X-axis: Correct Answer Number (0-63) * Y-axis: Frequency (0-7) * **Footer:** Contains text in Chinese, including a copyright notice and model information. ### Detailed Analysis or Content Details **Chinese Text (Top):** “对于正方形ABCD-A,B(C,D)。将1,2,...,8分别放在正方形的八个顶点上。要求每一个顶上 的任意三个数之和均不小于10. 求不同放置的个数.” *Translation:* "For the square ABCD - A, B (C, D). Place 1, 2, ..., 8 respectively at the eight vertices of the square. It is required that the sum of any three numbers at each vertex is not less than 10. Find the number of different arrangements." **Model 1: GLM-ZT-Air (12.1%)** * **Grid Analysis:** The heatmap shows varying confidence levels across the grid. The highest confidence (darkest green) appears concentrated around cells (20, 21), (21, 22), (40, 41), (41, 42), (60, 61), (61, 62). Many cells have very low confidence (white). * **Specific Values (Approximate):** * (0,0): 0% * (0,1): 25% * (0,2): 0% * (0,3): 52% * (0,4): 62% * (0,5): 12% * (0,6): 0% * (0,7): 37% * (0,8): 0% * (20,20): 82% * (20,21): 82% * (20,22): 50% * (20,23): 37% * (20,24): 0% * (20,25): 12% * (20,26): 0% * (20,27): 0% * (20,28): 0% * **Accuracy:** 8.0% and 14/21 = 66.7% **Model 2: Light-RT-32B-DS (11.6%)** * **Grid Analysis:** Similar to Model 1, this heatmap also shows a sparse distribution of high confidence. The highest confidence appears around cells (20, 21), (21, 22), (40, 41), (41, 42), (60, 61), (61, 62). * **Specific Values (Approximate):** * (0,0): 0% * (0,1): 25% * (0,2): 0% * (0,3): 52% * (0,4): 62% * (0,5): 12% * (0,6): 0% * (0,7): 37% * (0,8): 0% * (20,20): 87% * (20,21): 87% * (20,22): 42% * (20,23): 34% * (20,24): 12% * (20,25): 0% * (20,26): 0% * (20,27): 0% * (20,28): 0% * **Accuracy:** 14/21 = 66.7% **Correct Answer Number Distribution:** * The bar chart shows the frequency of each correct answer number. The highest frequency is around answer number 7, with a frequency of approximately 6. The frequencies for other answer numbers are generally lower, ranging from 0 to 3. **Footer Text (Chinese):** “版权所有 © 33986 实验班级名称：GLM-ZT-Air, Light-RT-32B-DS. 本文仅用于研究目的，任何商业用途均需获得授权。本实验基于对正方形ABCD-A,B(C,D)的顶点放置问题进行分析，旨在评估模型的视觉推理能力。请谨慎使用实验结果，并注意潜在的风险。” *Translation:* "Copyright © 33986. Experimental class name: GLM-ZT-Air, Light-RT-32B-DS. This article is for research purposes only, and any commercial use requires authorization. This experiment is based on the analysis of vertex placement problems in square ABCD - A, B (C, D), aiming to evaluate the visual reasoning ability of the model. Please use the experimental results with caution and pay attention to potential risks." ### Key Observations * Both models exhibit similar performance patterns on the grid, with high confidence concentrated in the same areas. * The accuracy scores are relatively low for both models (8.0% and 14/21 = 66.7%). * The distribution of correct answer numbers is uneven, suggesting some answers are easier to predict than others. * The problem statement describes a combinatorial problem involving placing numbers on the vertices of a square with a specific constraint. ### Interpretation The image demonstrates a comparison of two models' ability to solve a visual reasoning problem. The heatmaps visualize the models' confidence in predicting the correct digit for each cell in the grid. The low accuracy scores suggest that the task is challenging for both models. The similar performance patterns indicate that both models are struggling with the same aspects of the problem. The uneven distribution of correct answer numbers suggests that the problem has inherent biases or that certain configurations are easier to solve than others. The Chinese text provides the problem definition, indicating a combinatorial reasoning task. The footer emphasizes the research-only nature of the experiment and cautions against commercial use. The models are likely being evaluated on their ability to understand spatial relationships and apply constraints to solve the problem. The fact that the confidence is concentrated in specific areas suggests the models are identifying some patterns, but are not consistently accurate.

## Technical Document Extraction: AI Model Evaluation Interface ### Overview The image displays a web-based interface for evaluating and comparing the performance of two AI models on a specific mathematical problem. The interface includes the problem statement, model selection dropdowns, visual performance grids ("Problem Grids"), sample accuracy summaries, and model output snippets. The primary language is Chinese, with some English UI elements. ### Components/Axes **1. Header Section:** * **Tabs:** "Problem Statement" (selected, blue underline) and "Reference Answer". * **Problem Statement (Chinese):** "对于正方体 $ABCD - A_1B_1C_1D_1$, 将 $1, 2, \cdots, 8$ 分别放在正方体的八个顶点上, 要求每一个面上的任意三个数之和均不小于 $10$. 求不同放法的个数." * **English Translation:** "For a cube $ABCD - A_1B_1C_1D_1$, place the numbers $1, 2, \cdots, 8$ on the eight vertices of the cube, with the requirement that the sum of any three numbers on each face is not less than $10$. Find the number of different placement methods." **2. Model Selection:** * **Left Panel:** "Select Model 1" dropdown showing "GLM-Z1-Air (12.1%)". * **Right Panel:** "Select Model 2" dropdown showing "Light-R1-32B-DS (11.6%)". * The percentages in parentheses likely represent an overall accuracy or performance metric for each model on a broader benchmark. **3. Problem Grids (Two Identical Structures):** * **Title:** "Problem Grid" (appears above both left and right grids). * **Structure:** A 10x10 grid of cells, numbered 0 to 99 (reading left-to-right, top-to-bottom). * **Cell Content:** Each cell contains a number (the index, 0-99) and a percentage value below it. * **Color Coding:** Cells are colored based on the percentage value, creating a heatmap. * **Dark Red / Maroon:** Very low percentages (e.g., 0%, 1%, 4%). * **Brown / Tan:** Low to mid-range percentages (e.g., 12%, 18%, 25%, 37%). * **Olive / Yellow-Green:** Mid to high percentages (e.g., 50%, 62%, 70%). * **Dark Green:** Very high percentages (e.g., 81%, 100%). * **Spatial Layout:** The grids are placed side-by-side for direct visual comparison. The left grid corresponds to Model 1, and the right grid to Model 2. **4. Sample Accuracy & Output Sections:** * **Left (Model 1):** * **Title:** "Samples 8 – Model Accuracy: 0/8 = 0.0%" * **Sample Bar:** A horizontal bar with 8 segments, all colored dark red, labeled 0 through 7. * **Input Field:** "Enter Sample Number (0 – 63)" with a text box containing "0". * **Output Status:** "✗ Incorrect" (in red). * **Output Details:** "Extracted: $3360$", "Output Tokens: 16133". * **Model Output Snippet (Chinese):** "<think> 嗯，这个问题看起来有点挑战性，不过让我慢慢来思考一下。题目是说，把数字1到8分别放在正方体的八个顶点上，每个面上的任意三个数之和都不小于10。然后要找出不同的放法个数。首先，我需要明确正方体的结构。每个面有四个顶点，但题目里说的是每个面上的任意三个数之和都不小于10。也就是说，每个面的四个顶点中..." * **Partial English Translation:** "<think> Hmm, this problem looks a bit challenging, but let me think about it slowly. The problem states that we place the numbers 1 to 8 on the eight vertices of a cube, and the sum of any three numbers on each face is not less than 10. Then we need to find the number of different placement methods. First, I need to clarify the structure of a cube. Each face has four vertices, but the problem says the sum of any three numbers on each face is not less than 10. That is to say, among the four vertices of each face..." * **Right (Model 2):** * **Title:** "Samples 64 – Model Accuracy: 14/64 = 21.9%" * **Sample Bar:** A horizontal bar with 64 segments. Segments are colored either dark red (incorrect) or dark green (correct). The green segments are at indices: 0, 10, 13, 15, 21, 24, 40, 46, 47, 57, 59, 60, 61, 62. * **Input Field:** "Enter Sample Number (0 – 63)" with a text box containing "0". * **Output Status:** "✓ Correct" (in green). * **Output Details:** "Extracted: $480$", "Output Tokens: 12751". ### Detailed Analysis **Problem Grid Data (Key Points):** * **Left Grid (GLM-Z1-Air):** Shows a scattered pattern of performance. Notable high-performing cells (green/olive) include: * Cell 4: 62% * Cell 5: 50% * Cell 21: 62% * Cell 22: 50% * Cell 60: 75% * Cell 92: 100% (the only perfect score). * The majority of cells are dark red (0%) or brown (12-37%). * **Right Grid (Light-R1-32B-DS):** Shows a different performance distribution. Notable high-performing cells include: * Cell 13: 81% * Cell 24: 42% * Cell 30: 34% * Cell 70: 62% * Cell 73: 62% * Cell 77: 70% * Cell 92: 54% * This grid has fewer 0% cells but also fewer extremely high (75%+) cells compared to the left grid. **Model Accuracy Comparison:** * **Model 1 (GLM-Z1-Air):** Evaluated on 8 samples. Achieved 0 correct answers (0.0% accuracy). The extracted answer for sample 0 was "$3360$", which was marked incorrect. * **Model 2 (Light-R1-32B-DS):** Evaluated on 64 samples. Achieved 14 correct answers (21.9% accuracy). The extracted answer for sample 0 was "$480$", which was marked correct. ### Key Observations 1. **Performance Discrepancy:** There is a stark contrast in accuracy between the two models on this specific problem (0% vs. 21.9%), despite their similar overall benchmark scores shown in the dropdowns (12.1% vs. 11.6%). 2. **Answer Divergence:** For the same sample input (Sample 0), the models produced vastly different numerical answers ($3360$ vs. $480$), with only the latter being correct. 3. **Grid Pattern Differences:** The heatmap grids reveal that the models have different strengths and weaknesses across the 100 problem variants or test cases. Model 1 has a few very high peaks (including a 100% score) but many valleys (0%). Model 2's performance is more distributed, with fewer perfect scores but a higher baseline of non-zero results. 4. **Sample Size Inequality:** The models were evaluated on different sample sizes (8 vs. 64), which makes a direct comparison of the "Model Accuracy" percentage somewhat misleading without considering the confidence interval. ### Interpretation This interface is a diagnostic tool for analyzing AI model reasoning on a complex combinatorial geometry problem. The data suggests: * **Problem Difficulty:** The mathematical problem is non-trivial, as evidenced by the low accuracy rates (0% and 21.9%) even for specialized models. The correct answer appears to be 480. * **Model Capability:** Model 2 (Light-R1-32B-DS) demonstrates a significantly better grasp of this specific problem type than Model 1 (GLM-Z1-Air). Its higher accuracy and the correctness of its answer for the displayed sample indicate more robust reasoning for this constraint-satisfaction task. * **Diagnostic Value of the Grid:** The "Problem Grid" is not showing accuracy on 100 different problems, but likely the model's confidence or success rate on 100 different *sub-problems, reasoning steps, or perturbed versions* of the main problem. The color-coded heatmap allows researchers to quickly identify which specific aspects of the problem (represented by cell indices) are challenging for each model. For instance, both models scored 0% on cell 0, but Model 2 scored 81% on cell 13 where Model 1 scored 0%. * **Token Efficiency:** Model 2 achieved a correct answer using fewer output tokens (12751 vs. 16133) for the same sample, suggesting potentially more efficient reasoning. In summary, the image captures a moment of comparative analysis where one model clearly outperforms another on a challenging mathematical reasoning task, with visual tools provided to drill down into the granular performance differences.

# Technical Document Extraction ## Overview The image contains two comparative models (Model 1 and Model 2) with problem grids, sample data, and accuracy metrics. Text is primarily in Chinese with English annotations. Key components include heatmaps, sample number inputs, and financial/token metrics. --- ## Model 1 ### Problem Grid - **Structure**: 10x10 matrix (0-99) with percentage values - **Color Legend**: - Green: 0-20% - Yellow: 21-40% - Orange: 41-60% - Red: 61-80% - Dark Red: 81-100% - **Sample Numbers**: 8 samples (0-63) - **Accuracy**: 0/8 = 0.0% - **Extracted Data**: - Financial: $3360 - Tokens: 16133 ### Chinese Text Translation > "For quadrilateral ABCD - A₁B₁C₁D₁, divide 1,2,...,8 into eight parts on the quadrilateral's vertices. Each face requires three numbers, and the average should not be less than 10. Find different numbers for each." --- ## Model 2 ### Problem Grid - **Structure**: 10x10 matrix (0-99) with percentage values - **Color Legend**: - Green: 0-20% - Yellow: 21-40% - Orange: 41-60% - Red: 61-80% - Dark Red: 81-100% - **Sample Numbers**: 64 samples (0-63) - **Accuracy**: 14/64 = 21.9% - **Extracted Data**: - Financial: $480 - Tokens: 12751 ### Chinese Text Translation > "Each face has four vertices, but the problem requires three numbers per face. After selecting different numbers, the average should not be less than 10." --- ## Spatial Analysis 1. **Legend Placement**: - Model 1: Top-left corner - Model 2: Top-right corner 2. **Color Consistency**: - Verified all grid cells match legend color ranges - Example: Model 1 cell 0 (0%) = Green (0-20%) --- ## Trend Verification - **Model 1 Grid Trends**: - Highest values (81-100%) concentrated in lower rows (80-99) - Lower values (0-20%) in upper rows (0-20) - **Model 2 Grid Trends**: - More distributed values with 21.9% accuracy indicating moderate performance --- ## Component Isolation 1. **Header**: - "Problem Statement" (blue) and "Reference Answer" tabs 2. **Main Charts**: - Two heatmaps with percentage distributions 3. **Footer**: - Sample number inputs and extracted metrics --- ## Data Table Reconstruction ### Model 1 Sample 8 | Sample | Value | Color | Accuracy | |--------|-------|--------|----------| | 0 | 0 | Green | 0% | | 1 | 1 | Red | 100% | | ... | ... | ... | ... | | 7 | 7 | Red | 100% | ### Model 2 Sample 64 | Sample | Value | Color | Accuracy | |--------|-------|--------|----------| | 0 | 0 | Green | 100% | | 1 | 1 | Red | 100% | | ... | ... | ... | ... | | 63 | 63 | Red | 100% | --- ## Language Notes - **Primary Language**: Chinese (Simplified) - **Secondary Language**: English (annotations) - **Translated Text**: Provided for critical problem statements --- ## Conclusion The image compares two models with distinct performance metrics. Model 2 shows significantly better accuracy (21.9% vs 0.0%) despite similar grid structures. Financial and token metrics suggest different computational costs between models.