## Bar Chart: Model Performance Across Benchmarks ### Overview The image presents a series of bar charts comparing the performance of different language models across various benchmarks, including Math, Code, Vision, and General knowledge. The models being compared are Kimi k1.5 short-CoT, OpenAI 4o, Claude 3.5 Sonnet, Qwen2-VL, LLaMA-3.1 405B-Inst., DeepSeek V3, and Qwen2.5 72B-Inst. Each chart displays the performance (likely accuracy or a similar metric) of these models on a specific task. ### Components/Axes * **X-axis:** Each chart represents a different benchmark or task. The specific tasks are: * Math: AIME 2024 (Pass@1), MATH-500 (EM) * Code: LiveCodeBench v4 24.08-24.11 (Pass@1-COT) * Vision: MathVista\_test (Pass@1), MMMU\_val (Pass@1) * General: MMLU (EM), IF-Eval (Prompt Strict), CLUEWSC (EM), C-Eval (EM) * **Y-axis:** The y-axis represents the performance score, presumably accuracy or a similar metric, but the scale is not explicitly labeled. * **Legend:** Located at the top of the image. * Blue: Kimi k1.5 short-CoT * Light Blue: OpenAI 4o * Gray: Claude 3.5 Sonnet * Light Gray: Qwen2-VL * Dark Gray: LLaMA-3.1 405B-Inst. * White: DeepSeek V3 * Lightest Gray: Qwen2.5 72B-Inst. ### Detailed Analysis #### Math Benchmarks * **AIME 2024 (Pass@1):** * Kimi k1.5 short-CoT (Blue): 60.8 * OpenAI 4o (Light Blue): 9.3 * Claude 3.5 Sonnet (Gray): 16 * Qwen2-VL (Light Gray): 39.2 * LLaMA-3.1 405B-Inst. (Dark Gray): 23.3 * DeepSeek V3 (White): 23.3 * **MATH-500 (EM):** * Kimi k1.5 short-CoT (Blue): 94.6 * OpenAI 4o (Light Blue): 74.6 * Claude 3.5 Sonnet (Gray): 78.3 * Qwen2-VL (Light Gray): 90.2 * LLaMA-3.1 405B-Inst. (Dark Gray): 73.8 * Qwen2.5 72B-Inst. (Lightest Gray): 80 #### Code Benchmark * **LiveCodeBench v4 24.08-24.11 (Pass@1-COT):** * Kimi k1.5 short-CoT (Blue): 47.3 * OpenAI 4o (Light Blue): 33.4 * Claude 3.5 Sonnet (Gray): 36.3 * Qwen2-VL (Light Gray): 40.5 * LLaMA-3.1 405B-Inst. (Dark Gray): 28.4 * Qwen2.5 72B-Inst. (Lightest Gray): 31.1 #### Vision Benchmarks * **MathVista\_test (Pass@1):** * Kimi k1.5 short-CoT (Blue): 70.1 * OpenAI 4o (Light Blue): 63.8 * Claude 3.5 Sonnet (Gray): 65.3 * Qwen2-VL (Light Gray): 69.7 * **MMMU\_val (Pass@1):** * Kimi k1.5 short-CoT (Blue): 68 * OpenAI 4o (Light Blue): 66.1 * Claude 3.5 Sonnet (Gray): 66.4 * Qwen2-VL (Light Gray): 64.5 #### General Benchmarks * **MMLU (EM):** * Kimi k1.5 short-CoT (Blue): 87.4 * OpenAI 4o (Light Blue): 87.2 * Claude 3.5 Sonnet (Gray): 88.3 * Qwen2-VL (Light Gray): 88.6 * LLaMA-3.1 405B-Inst. (Dark Gray): 88.5 * Qwen2.5 72B-Inst. (Lightest Gray): 85.3 * **IF-Eval (Prompt Strict):** * Kimi k1.5 short-CoT (Blue): 87.2 * OpenAI 4o (Light Blue): 84.3 * Claude 3.5 Sonnet (Gray): 86.5 * Qwen2-VL (Light Gray): 86 * LLaMA-3.1 405B-Inst. (Dark Gray): 86.1 * Qwen2.5 72B-Inst. (Lightest Gray): 84.1 * **CLUEWSC (EM):** * Kimi k1.5 short-CoT (Blue): 91.7 * OpenAI 4o (Light Blue): 87.9 * Claude 3.5 Sonnet (Gray): 85.4 * Qwen2-VL (Light Gray): 90.9 * LLaMA-3.1 405B-Inst. (Dark Gray): 84.7 * Qwen2.5 72B-Inst. (Lightest Gray): 91.4 * **C-Eval (EM):** * Kimi k1.5 short-CoT (Blue): 88.3 * OpenAI 4o (Light Blue): 76 * Claude 3.5 Sonnet (Gray): 76.7 * LLaMA-3.1 405B-Inst. (Dark Gray): 61.5 * Qwen2.5 72B-Inst. (Lightest Gray): 86.1 ### Key Observations * Kimi k1.5 short-CoT generally performs well across all benchmarks, often achieving the highest scores. * OpenAI 4o shows variable performance, with lower scores on AIME 2024 and C-Eval compared to other benchmarks. * The performance of LLaMA-3.1 405B-Inst. is generally lower than Kimi k1.5 short-CoT and Qwen2-VL. * The models show relatively consistent performance on the General benchmarks, with scores generally above 80. ### Interpretation The data suggests that Kimi k1.5 short-CoT is a strong performer across a variety of tasks, including math, code, vision, and general knowledge. The performance differences between the models highlight their strengths and weaknesses in different areas. For example, OpenAI 4o seems to struggle with the AIME 2024 math problem set compared to other models. The consistent performance on the General benchmarks suggests that these models have a good grasp of general knowledge tasks. The relatively lower performance of LLaMA-3.1 405B-Inst. may be due to its architecture or training data.

## Bar Chart: Large Language Model Performance on Various Benchmarks ### Overview The image presents a bar chart comparing the performance of seven Large Language Models (LLMs) – Kimi k1.5 short-CoT, OpenAI 4o, Claude 3.5 Sonnet, Qwen2-VL, LLaMA-3.1 405B-Inst, DeepSeek V3, and Qwen2.5 72B-Inst – across eight different benchmarks. The benchmarks are categorized into Math, Code, Vision, and General reasoning tasks. Performance is measured as a percentage score, likely representing accuracy or pass rate. ### Components/Axes * **X-axis:** Represents the eight benchmarks: AME 2024 (Pass@1), MATH-500 (EM), LiveCodeBench v4 24.08-24.11 (Pass@1-CoT), MathVista (Pass@1), MMMLU (Pass@1), MMLU (EM), CLUEWSC (EM), C-Eval (EM). * **Y-axis:** Represents the performance score, ranging from approximately 0 to 100 (percentage). No explicit Y-axis label is present, but it is implied. * **Bars:** Each benchmark has seven bars, one for each LLM. * **Legend:** Located at the top of the chart, the legend maps colors to each LLM: * Kimi k1.5 short-CoT: Dark Blue * OpenAI 4o: Blue * Claude 3.5 Sonnet: Light Blue * Qwen2-VL: Orange * LLaMA-3.1 405B-Inst: Red * DeepSeek V3: Grey * Qwen2.5 72B-Inst: Purple * **Benchmark Categories:** The chart is visually divided into four sections: Math, Code, Vision, and General. ### Detailed Analysis or Content Details **Math:** * **AME 2024 (Pass@1):** Kimi k1.5 short-CoT: 60.6%, OpenAI 4o: 9.3%, Claude 3.5 Sonnet: 21.3%, Qwen2-VL: 39.2%, LLaMA-3.1 405B-Inst: 16%, DeepSeek V3: 23.3%, Qwen2.5 72B-Inst: 0. * **MATH-500 (EM):** Kimi k1.5 short-CoT: 94.6%, OpenAI 4o: 74.6%, Claude 3.5 Sonnet: 76.3%, Qwen2-VL: 73.8%, LLaMA-3.1 405B-Inst: 90.2%, DeepSeek V3: 80%, Qwen2.5 72B-Inst: 0. **Code:** * **LiveCodeBench v4 24.08-24.11 (Pass@1-CoT):** Kimi k1.5 short-CoT: 33.4%, OpenAI 4o: 28.4%, Claude 3.5 Sonnet: 40.5%, Qwen2-VL: 31.1%, LLaMA-3.1 405B-Inst: 0%, DeepSeek V3: 0%, Qwen2.5 72B-Inst: 0. **Vision:** * **MathVista (Pass@1):** Kimi k1.5 short-CoT: 70.1%, OpenAI 4o: 63.6%, Claude 3.5 Sonnet: 65.3%, Qwen2-VL: 69.7%, LLaMA-3.1 405B-Inst: 0%, DeepSeek V3: 68.1%, Qwen2.5 72B-Inst: 64.6%. * **MMMLU (Pass@1):** Kimi k1.5 short-CoT: 68%, OpenAI 4o: 66.4%, Claude 3.5 Sonnet: 69.1%, Qwen2-VL: 64.5%, LLaMA-3.1 405B-Inst: 0%, DeepSeek V3: 0%, Qwen2.5 72B-Inst: 0. **General:** * **MMLU (EM):** Kimi k1.5 short-CoT: 87.4%, OpenAI 4o: 83.2%, Claude 3.5 Sonnet: 86.8%, Qwen2-VL: 85.3%, LLaMA-3.1 405B-Inst: 88.5%, DeepSeek V3: 86.5%, Qwen2.5 72B-Inst: 84.1%. * **IF-Eval (Prompt Strict):** Kimi k1.5 short-CoT: 87.2%, OpenAI 4o: 84.3%, Claude 3.5 Sonnet: 86%, Qwen2-VL: 84.1%, LLaMA-3.1 405B-Inst: 85.6%, DeepSeek V3: 86.6%, Qwen2.5 72B-Inst: 84.1%. * **CLUEWSC (EM):** Kimi k1.5 short-CoT: 91.7%, OpenAI 4o: 85.4%, Claude 3.5 Sonnet: 90.4%, Qwen2-VL: 84.7%, LLaMA-3.1 405B-Inst: 0%, DeepSeek V3: 0%, Qwen2.5 72B-Inst: 0. * **C-Eval (EM):** Kimi k1.5 short-CoT: 86.8%, OpenAI 4o: 79%, Claude 3.5 Sonnet: 76.7%, Qwen2-VL: 81.5%, LLaMA-3.1 405B-Inst: 86.1%, DeepSeek V3: 61.5%, Qwen2.5 72B-Inst: 88.1%. ### Key Observations * **Kimi k1.5 short-CoT** consistently performs very well, often achieving the highest scores, particularly in Math and General reasoning tasks. * **OpenAI 4o** shows moderate performance across all benchmarks, generally falling in the middle range. * **Claude 3.5 Sonnet** demonstrates strong performance, often comparable to or slightly below Kimi k1.5 short-CoT. * **Qwen2-VL** and **Qwen2.5 72B-Inst** show variable performance, with some strong results but also some scores of 0. * **LLaMA-3.1 405B-Inst** and **DeepSeek V3** frequently score 0 on several benchmarks, indicating very poor performance on those specific tasks. * There is a clear disparity in performance across different benchmarks. Some benchmarks (e.g., MATH-500, MMLU) show high scores for several models, while others (e.g., LiveCodeBench, CLUEWSC) have significantly lower scores. ### Interpretation The chart provides a comparative analysis of the capabilities of several LLMs across a diverse set of reasoning tasks. Kimi k1.5 short-CoT emerges as a leading performer, particularly in mathematical and general knowledge domains. The significant variation in performance across benchmarks suggests that LLM capabilities are highly task-specific. The consistently low scores of LLaMA-3.1 405B-Inst and DeepSeek V3 on certain benchmarks indicate potential weaknesses in their architecture or training data for those specific tasks. The presence of zero scores highlights the challenges in achieving robust performance across all areas of reasoning. The data suggests that no single LLM excels in all areas, and the choice of model should be guided by the specific requirements of the application. The benchmarks used (AME, MATH-500, etc.) represent standardized tests designed to evaluate different aspects of LLM intelligence, and the results provide valuable insights into the strengths and weaknesses of each model.

## Bar Chart Comparison: AI Model Performance Across Multiple Benchmarks ### Overview The image is a composite bar chart comparing the performance of seven different large language models (LLMs) across eight distinct evaluation benchmarks. The benchmarks are grouped into four categories: Math, Code, Vision, and General. The chart uses a consistent color-coding scheme for each model, as defined in the legend at the top. ### Components/Axes * **Legend (Top Center):** A horizontal legend identifies seven models with associated color codes: * **Kimi k1.5 short-CoT:** Dark Blue * **OpenAI 4o:** Light Blue * **Claude 3.5 Sonnet:** Light Gray * **Qwen2-VL:** Medium Gray * **LLaMA-3.1 405B-Inst.:** Dark Gray * **DeepSeek V3:** Very Light Gray * **Qwen2.5 72B-Inst.:** Lightest Gray/White * **Chart Structure:** The image is divided into four main rectangular panels, each containing one or two bar charts. * **Top Left Panel (Math):** Contains two bar charts. * **Top Center Panel (Code):** Contains one bar chart. * **Top Right Panel (Vision):** Contains two bar charts. * **Bottom Row (General):** Contains four bar charts. * **Axes:** Each individual bar chart has: * **Y-axis:** Represents the performance score (percentage or metric-specific value). The scale is not explicitly numbered, but values are annotated on top of each bar. * **X-axis:** Lists the specific benchmark name for each group of bars. ### Detailed Analysis #### **Math Category (Top Left Panel)** 1. **Benchmark: AIME 2024 (Pass@1)** * **Kimi k1.5 short-CoT (Dark Blue):** 40.8 * **OpenAI 4o (Light Blue):** 9.3 * **Claude 3.5 Sonnet (Light Gray):** 16 * **Qwen2-VL (Medium Gray):** 23.3 * **LLaMA-3.1 405B-Inst. (Dark Gray):** 39.2 * **DeepSeek V3 (Very Light Gray):** 33.3 * **Qwen2.5 72B-Inst. (Lightest Gray):** 33.3 * **Trend:** Kimi k1.5 and LLaMA-3.1 are the top performers, significantly ahead of others. OpenAI 4o has the lowest score. 2. **Benchmark: MATH-500 (EM)** * **Kimi k1.5 short-CoT (Dark Blue):** 94.6 * **OpenAI 4o (Light Blue):** 74.6 * **Claude 3.5 Sonnet (Light Gray):** 78.3 * **Qwen2-VL (Medium Gray):** 73.8 * **LLaMA-3.1 405B-Inst. (Dark Gray):** 86.2 * **DeepSeek V3 (Very Light Gray):** 80 * **Qwen2.5 72B-Inst. (Lightest Gray):** 80 * **Trend:** Kimi k1.5 shows a dominant lead. LLaMA-3.1 is second. The remaining models cluster in the 73-80 range. #### **Code Category (Top Center Panel)** 1. **Benchmark: LiveCodeBench v4 24.08-24.11 (Pass@1-COT)** * **Kimi k1.5 short-CoT (Dark Blue):** 47.9 * **OpenAI 4o (Light Blue):** 33.4 * **Claude 3.5 Sonnet (Light Gray):** 36.3 * **Qwen2-VL (Medium Gray):** 29.4 * **LLaMA-3.1 405B-Inst. (Dark Gray):** 40.5 * **DeepSeek V3 (Very Light Gray):** 31.1 * **Qwen2.5 72B-Inst. (Lightest Gray):** (Bar present but value not clearly visible, appears to be the lowest) * **Trend:** Kimi k1.5 leads, followed by LLaMA-3.1 and Claude 3.5 Sonnet. Qwen2-VL and DeepSeek V3 are at the lower end. #### **Vision Category (Top Right Panel)** 1. **Benchmark: MathVista_test (Pass@1)** * **Kimi k1.5 short-CoT (Dark Blue):** 70.1 * **OpenAI 4o (Light Blue):** 63.6 * **Claude 3.5 Sonnet (Light Gray):** 65.3 * **Qwen2-VL (Medium Gray):** 69.7 * **LLaMA-3.1 405B-Inst. (Dark Gray):** (Bar present but value not clearly visible) * **DeepSeek V3 (Very Light Gray):** (Bar present but value not clearly visible) * **Qwen2.5 72B-Inst. (Lightest Gray):** (Bar present but value not clearly visible) * **Trend:** Kimi k1.5 and Qwen2-VL are the top performers, very close in score. OpenAI 4o is the lowest among the clearly labeled scores. 2. **Benchmark: MMMU_val (Pass@1)** * **Kimi k1.5 short-CoT (Dark Blue):** 68 * **OpenAI 4o (Light Blue):** 69.1 * **Claude 3.5 Sonnet (Light Gray):** 66.4 * **Qwen2-VL (Medium Gray):** 64.5 * **LLaMA-3.1 405B-Inst. (Dark Gray):** (Bar present but value not clearly visible) * **DeepSeek V3 (Very Light Gray):** (Bar present but value not clearly visible) * **Qwen2.5 72B-Inst. (Lightest Gray):** (Bar present but value not clearly visible) * **Trend:** OpenAI 4o has a slight lead over Kimi k1.5. Claude 3.5 Sonnet and Qwen2-VL follow closely. #### **General Category (Bottom Row)** 1. **Benchmark: MMLU (EM)** * **Kimi k1.5 short-CoT (Dark Blue):** 87.4 * **OpenAI 4o (Light Blue):** 87.2 * **Claude 3.5 Sonnet (Light Gray):** 88.3 * **Qwen2-VL (Medium Gray):** 88.6 * **LLaMA-3.1 405B-Inst. (Dark Gray):** 88.5 * **DeepSeek V3 (Very Light Gray):** 88.3 * **Qwen2.5 72B-Inst. (Lightest Gray):** (Bar present but value not clearly visible) * **Trend:** Extremely tight clustering. Qwen2-VL has a marginal lead. All models score between approximately 87.2 and 88.6. 2. **Benchmark: IFEval (Prompt Strict)** * **Kimi k1.5 short-CoT (Dark Blue):** 87.2 * **OpenAI 4o (Light Blue):** 84.3 * **Claude 3.5 Sonnet (Light Gray):** 86.5 * **Qwen2-VL (Medium Gray):** 86 * **LLaMA-3.1 405B-Inst. (Dark Gray):** 86.1 * **DeepSeek V3 (Very Light Gray):** 84.1 * **Qwen2.5 72B-Inst. (Lightest Gray):** (Bar present but value not clearly visible) * **Trend:** Kimi k1.5 leads. Claude, Qwen2-VL, and LLaMA-3.1 are tightly grouped in the mid-86s. OpenAI 4o and DeepSeek V3 are slightly lower. 3. **Benchmark: CLUEWSC (EM)** * **Kimi k1.5 short-CoT (Dark Blue):** 91.7 * **OpenAI 4o (Light Blue):** 87.9 * **Claude 3.5 Sonnet (Light Gray):** 85.4 * **Qwen2-VL (Medium Gray):** 86.7 * **LLaMA-3.1 405B-Inst. (Dark Gray):** 90.8 * **DeepSeek V3 (Very Light Gray):** 91.4 * **Qwen2.5 72B-Inst. (Lightest Gray):** (Bar present but value not clearly visible) * **Trend:** Kimi k1.5 and DeepSeek V3 are the top performers, both above 91. LLaMA-3.1 is also strong at 90.8. Claude 3.5 Sonnet is the lowest. 4. **Benchmark: C-Eval (EM)** * **Kimi k1.5 short-CoT (Dark Blue):** 88.2 * **OpenAI 4o (Light Blue):** 76 * **Claude 3.5 Sonnet (Light Gray):** 76.7 * **Qwen2-VL (Medium Gray):** 81.5 * **LLaMA-3.1 405B-Inst. (Dark Gray):** 86.5 * **DeepSeek V3 (Very Light Gray):** 86.1 * **Qwen2.5 72B-Inst. (Lightest Gray):** (Bar present but value not clearly visible) * **Trend:** Kimi k1.5 has a clear lead. LLaMA-3.1 and DeepSeek V3 are strong seconds. OpenAI 4o and Claude 3.5 Sonnet are notably lower. ### Key Observations 1. **Model Dominance:** The **Kimi k1.5 short-CoT** model (dark blue bar) is the top performer in 6 out of the 8 benchmarks shown (AIME 2024, MATH-500, LiveCodeBench, MathVista_test, IFEval, CLUEWSC, C-Eval). It shows particular strength in mathematical and coding reasoning tasks. 2. **Competitive Tiers:** Performance is not uniform. In benchmarks like **MMLU**, all models are extremely competitive (within ~1.4 points). In others like **AIME 2024** or **MATH-500**, there is a significant performance gap between the leader and the rest. 3. **Vision Benchmark Split:** In the Vision category, the leadership changes. **Qwen2-VL** is very competitive with Kimi k1.5 on MathVista, and **OpenAI 4o** takes a slight lead on MMMU_val. 4. **Language-Specific Benchmark:** The **C-Eval** benchmark (likely Chinese-language focused) shows a different ranking, with Kimi k1.5 leading, followed by LLaMA-3.1 and DeepSeek V3, while OpenAI 4o and Claude 3.5 Sonnet score significantly lower. 5. **Data Gaps:** For several models (particularly Qwen2.5 72B-Inst., and some bars for LLaMA-3.1 and DeepSeek V3 in the Vision section), the exact numerical score is not clearly legible on the chart, though the bar height is visible. ### Interpretation This chart provides a snapshot of the competitive landscape among leading LLMs as of the evaluation period (likely late 2024/early 2025 based on benchmark names). The data suggests that **Kimi k1.5 short-CoT** is a highly capable model, especially in tasks requiring complex reasoning (math, code, logic). Its consistent high performance across diverse domains indicates strong generalization. The tight clustering in general knowledge benchmarks like **MMLU** suggests that top-tier models have reached a similar plateau of broad knowledge. Differentiation now occurs in specialized, harder tasks (e.g., competition-level math, live coding) and in specific domains like vision-language understanding or instruction following (**IFEval**). The variation in rankings across benchmarks underscores that no single model is universally "best." The optimal choice depends on the specific application: **Qwen2-VL** for certain vision tasks, **OpenAI 4o** for MMMU, **DeepSeek V3** for CLUEWSC, etc. The strong showing of **LLaMA-3.1 405B-Inst.**, an open-weights model, across many benchmarks is notable, demonstrating that open models can compete closely with proprietary ones. The chart effectively communicates that the field is highly competitive, with rapid iteration leading to frequent changes in the state-of-the-art across different evaluation axes.

## Bar Chart: AI Model Performance Comparison Across Tasks ### Overview The image is a grouped bar chart comparing the performance of seven AI models across four task categories: Math, Code, Vision, and General. Each model is represented by a distinct color-coded bar, with numerical performance scores (in percentages) displayed atop each bar. The chart includes a legend at the top mapping colors to model names. ### Components/Axes - **Legend**: Located at the top, mapping colors to models: - Blue: Kimi 1.5 short-CoT - Dark Blue: OpenAI 4o - Light Blue: Claude 3.5 Sonnet - Gray: Gwen2-VL - Dark Gray: LLaMA-3.1 405B-Inst - Brown: DeepSeek V3 - Light Brown: Gwen2.5 72B-Inst - **X-Axis**: Task categories and benchmarks: - Math: AIME 2024 (Pass@81), MATH-500 (EM) - Code: LiveCodeBench v4 24.08-24.11 (Pass@1-CoT) - Vision: MathVista_math (Pass@81), MMU_va (Pass@81) - General: MMLU (EM), IF-Eval (Prompt Strict), CLUEWSC (EM), C-Eval (EM) - **Y-Axis**: Performance scores (%), ranging from ~9% to ~94.6%. ### Detailed Analysis #### Math - **AIME 2024 (Pass@81)**: - Kimi 1.5 short-CoT: 60.8% - OpenAI 4o: 9.3% - Claude 3.5 Sonnet: 16% - Gwen2-VL: 23.3% - LLaMA-3.1 405B-Inst: 22.3% - DeepSeek V3: 16% - Gwen2.5 72B-Inst: 23.3% - **MATH-500 (EM)**: - Kimi 1.5 short-CoT: 94.6% - OpenAI 4o: 74.6% - Claude 3.5 Sonnet: 79.5% - Gwen2-VL: 80% - LLaMA-3.1 405B-Inst: 73.8% - DeepSeek V3: 80.2% - Gwen2.5 72B-Inst: 80% #### Code - **LiveCodeBench v4 24.08-24.11 (Pass@1-CoT)**: - Kimi 1.5 short-CoT: 42.3% - OpenAI 4o: 36.3% - Claude 3.5 Sonnet: 28.4% - Gwen2-VL: 40.5% - LLaMA-3.1 405B-Inst: 31.1% - DeepSeek V3: 33.4% - Gwen2.5 72B-Inst: 31.1% #### Vision - **MathVista_math (Pass@81)**: - Kimi 1.5 short-CoT: 70.1% - OpenAI 4o: 63.8% - Claude 3.5 Sonnet: 65.3% - Gwen2-VL: 69.7% - LLaMA-3.1 405B-Inst: 63.8% - DeepSeek V3: 65.3% - Gwen2.5 72B-Inst: 69.7% - **MMU_va (Pass@81)**: - Kimi 1.5 short-CoT: 68% - OpenAI 4o: 66.4% - Claude 3.5 Sonnet: 64.5% - Gwen2-VL: 69.1% - LLaMA-3.1 405B-Inst: 66.4% - DeepSeek V3: 64.5% - Gwen2.5 72B-Inst: 69.1% #### General - **MMLU (EM)**: - Kimi 1.5 short-CoT: 87.4% - OpenAI 4o: 87.2% - Claude 3.5 Sonnet: 88.6% - Gwen2-VL: 85.5% - LLaMA-3.1 405B-Inst: 85.3% - DeepSeek V3: 88.6% - Gwen2.5 72B-Inst: 85.3% - **IF-Eval (Prompt Strict)**: - Kimi 1.5 short-CoT: 87.2% - OpenAI 4o: 84.3% - Claude 3.5 Sonnet: 86.5% - Gwen2-VL: 86% - LLaMA-3.1 405B-Inst: 86.1% - DeepSeek V3: 86.1% - Gwen2.5 72B-Inst: 84.1% - **CLUEWSC (EM)**: - Kimi 1.5 short-CoT: 91.7% - OpenAI 4o: 87.9% - Claude 3.5 Sonnet: 85.4% - Gwen2-VL: 84.7% - LLaMA-3.1 405B-Inst: 85.4% - DeepSeek V3: 90.9% - Gwen2.5 72B-Inst: 91.4% - **C-Eval (EM)**: - Kimi 1.5 short-CoT: 83.3% - OpenAI 4o: 70% - Claude 3.5 Sonnet: 76.2% - Gwen2-VL: 86.5% - LLaMA-3.1 405B-Inst: 61.5% - DeepSeek V3: 86.1% - Gwen2.5 72B-Inst: 86.1% ### Key Observations 1. **Kimi 1.5 short-CoT** dominates in **Math (AIME 2024)** with 60.8% but underperforms in Code and General tasks compared to other models. 2. **LLaMA-3.1 405B-Inst** and **DeepSeek V3** show strong performance in Code (40.5% and 33.4%, respectively) and General tasks (90.9% and 88.6% for DeepSeek V3 in CLUEWSC). 3. **Gwen2-VL** and **Gwen2.5 72B-Inst** consistently score mid-to-high in Vision tasks but lag in Math and Code. 4. **OpenAI 4o** performs well in General tasks (87.2% in MMLU) but struggles in Math (9.3% in AIME 2024). 5. **Claude 3.5 Sonnet** has balanced performance across tasks, with no extreme outliers. ### Interpretation The chart highlights **task-specific strengths** among AI models: - **Kimi 1.5 short-CoT** excels in **Math (AIME 2024)** but lacks consistency in other domains. - **LLaMA-3.1 405B-Inst** and **DeepSeek V3** demonstrate robustness in **Code and General tasks**, suggesting scalability with larger parameter counts. - **Gwen2-VL** and **Gwen2.5 72B-Inst** specialize in **Vision tasks**, aligning with their architecture focused on visual reasoning. - **OpenAI 4o** shows a trade-off between General and Math performance, indicating potential limitations in specialized reasoning. The data underscores the importance of **model architecture and training focus** in determining task-specific efficacy. For example, Kimi’s high AIME score suggests advanced mathematical reasoning capabilities, while LLaMA’s performance in Code reflects its general-purpose design. However, no single model dominates all tasks, emphasizing the need for task-aware model selection.