## Bar Chart: Model Performance on Various Benchmarks ### Overview The image is a bar chart comparing the performance of four different language models on four different benchmarks: AIME 2024, MATH-500, GPQA Diamond, and LiveCodeBench. The y-axis represents Accuracy/Percentile (%), ranging from 30 to 100. The x-axis represents the benchmarks. The chart uses different colored bars to represent each model. ### Components/Axes * **Y-axis:** Accuracy / Percentile (%) * Scale: 30 to 100, with gridlines at intervals of 10. * **X-axis:** Benchmarks (AIME 2024, MATH-500, GPQA Diamond, LiveCodeBench), with "(Pass@1)" below each benchmark name. * **Legend:** Located at the top-right of the chart. * Light Green (with diagonal lines): AM-Distill-Qwen-32B * Light Red (with diagonal lines): DeepSeek-R1-Distill-Qwen-32B * Light Green (with diagonal lines): AM-Distill-Qwen-72B * Light Orange (with diagonal lines): DeepSeek-R1-Distill-Llama-70B ### Detailed Analysis Here's a breakdown of the performance of each model on each benchmark: * **AIME 2024 (Pass@1):** * AM-Distill-Qwen-32B (Light Green): 72.7 * DeepSeek-R1-Distill-Qwen-32B (Light Red): 72.6 * AM-Distill-Qwen-72B (Light Green): 76.5 * DeepSeek-R1-Distill-Llama-70B (Light Orange): 70.0 * **MATH-500 (Pass@1)::** * AM-Distill-Qwen-32B (Light Green): 96.2 * DeepSeek-R1-Distill-Qwen-32B (Light Red): 94.3 * AM-Distill-Qwen-72B (Light Green): 97.0 * DeepSeek-R1-Distill-Llama-70B (Light Orange): 94.5 * **GPQA Diamond (Pass@1):** * AM-Distill-Qwen-32B (Light Green): 64.3 * DeepSeek-R1-Distill-Qwen-32B (Light Red): 62.1 * AM-Distill-Qwen-72B (Light Green): 65.9 * DeepSeek-R1-Distill-Llama-70B (Light Orange): 65.2 * **LiveCodeBench (Pass@1):** * AM-Distill-Qwen-32B (Light Green): 59.1 * DeepSeek-R1-Distill-Qwen-32B (Light Red): 57.2 * AM-Distill-Qwen-72B (Light Green): 59.7 * DeepSeek-R1-Distill-Llama-70B (Light Orange): 57.5 ### Key Observations * The AM-Distill-Qwen-72B model generally performs the best across all benchmarks, achieving the highest scores in AIME 2024, MATH-500, and GPQA Diamond. * The MATH-500 benchmark has the highest scores for all models, indicating it might be an easier task compared to the others. * The LiveCodeBench benchmark has the lowest scores for all models, suggesting it is the most challenging task. * The performance difference between the models is most pronounced in the AIME 2024 benchmark. ### Interpretation The bar chart provides a comparative analysis of the performance of four language models on different benchmarks. The AM-Distill-Qwen-72B model consistently outperforms the other models, especially on the MATH-500 benchmark. The LiveCodeBench benchmark appears to be the most difficult for all models. The data suggests that the choice of model can significantly impact performance, and the difficulty of the benchmark also plays a crucial role. The "Pass@1" likely refers to the evaluation metric, indicating the accuracy of generating the correct answer on the first attempt.

\n ## Bar Chart: Model Performance on Benchmarks ### Overview This bar chart compares the performance of four different language models – AM-Distill-Qwen-32B, DeepSeek-R1-Distill-Qwen-32B, AM-Distill-Qwen-72B, and DeepSeek-R1-Distill-Llama-70B – across four benchmarks: AIME 2024, MATH-500, GPQA Diamond, and LiveCodeBench. The performance metric is Accuracy/Percentile (Pass@1). ### Components/Axes * **X-axis:** Benchmark Name (AIME 2024, MATH-500, GPQA Diamond, LiveCodeBench) with the Pass@1 metric specified below each name. * **Y-axis:** Accuracy / Percentile (%) ranging from 30 to 100, with increments of 10. * **Legend:** Located in the top-right corner, identifying the four models using both name and parameter size (e.g., AM-Distill-Qwen-32B). The legend uses color-coding to match the bars in the chart: * AM-Distill-Qwen-32B: Light Red/Pink (hashed pattern) * DeepSeek-R1-Distill-Qwen-32B: Medium Red/Pink (hashed pattern) * AM-Distill-Qwen-72B: Light Green (hashed pattern) * DeepSeek-R1-Distill-Llama-70B: Medium Green (hashed pattern) ### Detailed Analysis The chart consists of four groups of bars, one for each benchmark. Within each group, there are four bars representing the performance of each model. **AIME 2024 (Pass@1):** * AM-Distill-Qwen-32B: Approximately 72.7% * DeepSeek-R1-Distill-Qwen-32B: Approximately 72.6% * AM-Distill-Qwen-72B: Approximately 76.5% * DeepSeek-R1-Distill-Llama-70B: Approximately 70.0% **MATH-500 (Pass@1):** * AM-Distill-Qwen-32B: Approximately 96.2% * DeepSeek-R1-Distill-Qwen-32B: Approximately 94.3% * AM-Distill-Qwen-72B: Approximately 97.0% * DeepSeek-R1-Distill-Llama-70B: Approximately 94.5% **GPQA Diamond (Pass@1):** * AM-Distill-Qwen-32B: Approximately 64.3% * DeepSeek-R1-Distill-Qwen-32B: Approximately 62.1% * AM-Distill-Qwen-72B: Approximately 65.9% * DeepSeek-R1-Distill-Llama-70B: Approximately 65.2% **LiveCodeBench (Pass@1):** * AM-Distill-Qwen-32B: Approximately 59.1% * DeepSeek-R1-Distill-Qwen-32B: Approximately 57.2% * AM-Distill-Qwen-72B: Approximately 59.7% * DeepSeek-R1-Distill-Llama-70B: Approximately 57.5% ### Key Observations * **MATH-500 consistently shows the highest accuracy** across all models, with values generally above 94%. * **LiveCodeBench consistently shows the lowest accuracy** across all models, with values generally below 60%. * **AM-Distill-Qwen-72B generally outperforms AM-Distill-Qwen-32B** across all benchmarks. * **DeepSeek-R1-Distill-Llama-70B generally performs similarly to DeepSeek-R1-Distill-Qwen-32B**, with slight variations depending on the benchmark. * The differences between the models are more pronounced on some benchmarks (e.g., AIME 2024) than others (e.g., MATH-500). ### Interpretation The chart demonstrates the performance of different language models on a variety of benchmarks designed to test different capabilities. The consistent high performance on MATH-500 suggests these models are strong at mathematical reasoning. The lower performance on LiveCodeBench indicates a relative weakness in code generation or understanding. The fact that the 72B parameter model (AM-Distill-Qwen-72B) consistently outperforms the 32B parameter model suggests that increasing model size generally leads to improved performance, although the gains are not always substantial. The comparison between the Qwen and Llama based models provides insight into the architectural differences and their impact on performance across different tasks. The Pass@1 metric indicates the percentage of times the model provides the correct answer as the *first* prediction, which is a stringent measure of performance. The use of a hashed pattern within the bars suggests a visual emphasis on the discrete nature of the data, rather than a continuous trend.

## Grouped Bar Chart: Model Performance Comparison Across Benchmarks ### Overview This image is a grouped bar chart comparing the performance of four different AI models across four distinct benchmarks. The chart measures "Accuracy / Percentile (%)" on the y-axis against four benchmark categories on the x-axis. The legend is positioned in the top-right corner of the chart area. ### Components/Axes * **Y-Axis:** Labeled "Accuracy / Percentile (%)". The scale runs from 30 to 100, with major gridlines at intervals of 10 (30, 40, 50, 60, 70, 80, 90, 100). * **X-Axis:** Lists four benchmark categories. Each category has a primary label and a secondary label in parentheses. 1. **AIME 2024** (Pass@1) 2. **MATH-500** (Pass@1) 3. **GPQA Diamond** (Pass@1) 4. **LiveCodeBench** (Pass@1) * **Legend (Top-Right):** Identifies four models, each associated with a unique color and pattern. 1. **AM-Distill-Qwen-32B:** Teal color with diagonal stripes (\\). 2. **DeepSeek-R1-Distill-Qwen-32B:** Solid light salmon/pink color. 3. **AM-Distill-Qwen-72B:** Light mint green color with diagonal stripes (//). 4. **DeepSeek-R1-Distill-Llama-70B:** Solid light peach/beige color. ### Detailed Analysis The chart presents the following numerical data for each model on each benchmark. The values are read directly from the labels atop each bar. **1. AIME 2024 (Pass@1)** * AM-Distill-Qwen-32B: 72.7% * DeepSeek-R1-Distill-Qwen-32B: 72.6% * AM-Distill-Qwen-72B: 76.5% * DeepSeek-R1-Distill-Llama-70B: 70.0% **2. MATH-500 (Pass@1)** * AM-Distill-Qwen-32B: 96.2% * DeepSeek-R1-Distill-Qwen-32B: 94.3% * AM-Distill-Qwen-72B: 97.0% * DeepSeek-R1-Distill-Llama-70B: 94.5% **3. GPQA Diamond (Pass@1)** * AM-Distill-Qwen-32B: 64.3% * DeepSeek-R1-Distill-Qwen-32B: 62.1% * AM-Distill-Qwen-72B: 65.9% * DeepSeek-R1-Distill-Llama-70B: 65.2% **4. LiveCodeBench (Pass@1)** * AM-Distill-Qwen-32B: 59.1% * DeepSeek-R1-Distill-Qwen-32B: 57.2% * AM-Distill-Qwen-72B: 59.7% * DeepSeek-R1-Distill-Llama-70B: 57.5% ### Key Observations * **Highest Overall Performance:** The **MATH-500** benchmark yielded the highest scores for all models, with all values above 94%. * **Model Ranking Consistency:** The **AM-Distill-Qwen-72B** model (light green, striped) achieves the highest score in three out of four benchmarks (AIME 2024, MATH-500, GPQA Diamond). It is narrowly beaten by its smaller counterpart, AM-Distill-Qwen-32B, on LiveCodeBench (59.7% vs. 59.1%). * **Performance Gap:** The performance gap between the AM-Distill and DeepSeek-R1-Distill variants of the Qwen-32B model is smallest on AIME 2024 (0.1%) and largest on GPQA Diamond (2.2%). * **Lowest Scores:** The **LiveCodeBench** benchmark appears to be the most challenging, with all models scoring below 60%. * **Architecture Comparison:** On the Qwen-32B base, the AM-Distill variant consistently outperforms the DeepSeek-R1-Distill variant. The larger AM-Distill-Qwen-72B generally outperforms the similarly sized DeepSeek-R1-Distill-Llama-70B. ### Interpretation This chart provides a comparative performance analysis of distilled language models on reasoning-heavy benchmarks. The data suggests that the "AM-Distill" method, when applied to the Qwen architecture, yields models that are highly competitive and often superior to the "DeepSeek-R1-Distill" method on these specific tasks. The consistently high scores on MATH-500 indicate that all evaluated models have strong mathematical reasoning capabilities. Conversely, the lower scores on LiveCodeBench suggest that coding generation and execution in a live environment remains a more difficult challenge for these models relative to the other tested domains (math competitions, graduate-level QA). The chart effectively communicates that model scale (72B/70B vs. 32B) and distillation technique (AM vs. DeepSeek-R1) are both significant factors in performance, with the AM-Distill approach showing a slight but consistent advantage in this comparison. The visualization allows for quick cross-benchmark and cross-model comparisons, highlighting strengths and relative weaknesses.

## Bar Chart: Model Accuracy Comparison Across Datasets ### Overview The chart compares the accuracy (Pass@1) of four AI models across four datasets: AIME 2024, MATH-500, GPQA Diamond, and LiveCodeBench. Models include AM-Distill-Qwen-32B, DeepSeek-R1-Distill-Qwen-32B, AM-Distill-Qwen-72B, and DeepSeek-R1-Distill-Llama-70B. Accuracy is measured in percentage (%) on a y-axis from 30% to 100%. ### Components/Axes - **X-axis**: Datasets (AIME 2024, MATH-500, GPQA Diamond, LiveCodeBench). - **Y-axis**: Accuracy (Pass@1) in percentage (%) from 30% to 100%. - **Legend**: Located in the top-right corner, mapping colors to models: - Blue (striped): AM-Distill-Qwen-32B - Red (solid): DeepSeek-R1-Distill-Qwen-32B - Green (striped): AM-Distill-Qwen-72B - Orange (solid): DeepSeek-R1-Distill-Llama-70B ### Detailed Analysis 1. **AIME 2024**: - AM-Distill-Qwen-32B: 72.7% - DeepSeek-R1-Distill-Qwen-32B: 72.6% - AM-Distill-Qwen-72B: 76.5% - DeepSeek-R1-Distill-Llama-70B: 70.0% 2. **MATH-500**: - AM-Distill-Qwen-32B: 96.2% - DeepSeek-R1-Distill-Qwen-32B: 94.3% - AM-Distill-Qwen-72B: 97.0% - DeepSeek-R1-Distill-Llama-70B: 94.5% 3. **GPQA Diamond**: - AM-Distill-Qwen-32B: 64.3% - DeepSeek-R1-Distill-Qwen-32B: 62.1% - AM-Distill-Qwen-72B: 65.9% - DeepSeek-R1-Distill-Llama-70B: 65.2% 4. **LiveCodeBench**: - AM-Distill-Qwen-32B: 59.1% - DeepSeek-R1-Distill-Qwen-32B: 57.2% - AM-Distill-Qwen-72B: 59.7% - DeepSeek-R1-Distill-Llama-70B: 57.5% ### Key Observations - **Highest Performance**: MATH-500 dataset shows the highest accuracies, with AM-Distill-Qwen-72B achieving 97.0%. - **Lowest Performance**: LiveCodeBench dataset has the lowest accuracies, with DeepSeek-R1-Distill-Llama-70B at 57.5%. - **Model Trends**: - AM-Distill-Qwen-72B consistently outperforms other models across all datasets. - DeepSeek-R1-Distill-Qwen-32B and DeepSeek-R1-Distill-Llama-70B show lower accuracy than their Qwen-based counterparts. - The 72B model (AM-Distill-Qwen-72B) demonstrates superior performance compared to the 32B and 70B models. ### Interpretation The data suggests that larger model sizes (e.g., 72B parameters) and specific architectures (e.g., AM-Distill-Qwen) yield higher accuracy. The DeepSeek-R1-Distill models, while based on Qwen, underperform compared to their non-distilled counterparts, indicating potential trade-offs in distillation processes. MATH-500’s high accuracy across models implies it is the "easiest" dataset, while LiveCodeBench’s lower scores suggest greater complexity. The AM-Distill-Qwen-72B model emerges as the most robust performer, highlighting the importance of model scale and architecture in task-specific performance.