## Line Chart: Success Rate vs. Number of Actions for AI Models ### Overview The image is a line chart comparing the performance of four different AI models. It plots the "Success rate" (y-axis) against the "Number of actions" (x-axis), showing how each model's performance degrades as the task complexity (number of actions) increases. All data series show a downward trend. ### Components/Axes * **Chart Type:** Line chart with markers. * **X-Axis:** * **Label:** "Number of actions" * **Scale:** Linear, ranging from 0 to 300. * **Major Ticks:** 0, 50, 100, 150, 200, 250, 300. * **Y-Axis:** * **Label:** "Success rate" * **Scale:** Linear, ranging from 0.0 to 1.0. * **Major Ticks:** 0.0, 0.2, 0.4, 0.6, 0.8, 1.0. * **Legend:** Located in the top-right corner of the chart area. It contains four entries, each with a colored line segment and a circular marker: 1. **Blue line with circle marker:** "GPT-5" 2. **Orange line with circle marker:** "OSS-120B" 3. **Green line with circle marker:** "OSS-20B" 4. **Red line with circle marker:** "Llama-4-Maverick" * **Grid:** A light gray grid is present, aligned with the major ticks on both axes. ### Detailed Analysis The chart displays four distinct data series, each representing a model's success rate at different action counts. The following data points are approximate, read from the chart's grid. **1. GPT-5 (Blue Line)** * **Trend:** The line slopes downward consistently from left to right, indicating a steady decrease in success rate as the number of actions increases. It maintains the highest success rate among all models at every data point. * **Approximate Data Points:** * 0 actions: ~1.00 * ~25 actions: ~0.99 * ~35 actions: ~0.96 * 50 actions: ~0.85 * 100 actions: ~0.62 * 140 actions: ~0.51 * 180 actions: ~0.25 * 220 actions: ~0.18 * 260 actions: ~0.17 * 300 actions: ~0.08 **2. OSS-120B (Orange Line)** * **Trend:** The line slopes downward, starting very high but declining more steeply than GPT-5. It crosses below the 0.5 success rate mark between 50 and 100 actions. * **Approximate Data Points:** * 0 actions: ~0.97 * ~25 actions: ~0.95 * ~35 actions: ~0.91 * 50 actions: ~0.72 * 100 actions: ~0.23 * 140 actions: ~0.04 * 180 actions: ~0.02 * 220 actions: ~0.00 (appears to be at or near zero) * 260 actions: ~0.00 * 300 actions: ~0.00 **3. OSS-20B (Green Line)** * **Trend:** The line shows a very steep initial decline, dropping below a 0.5 success rate before 50 actions. It approaches zero success rate by 100 actions. * **Approximate Data Points:** * 0 actions: ~0.88 * ~20 actions: ~0.74 * ~35 actions: ~0.53 * 50 actions: ~0.31 * 100 actions: ~0.02 * 140 actions: ~0.00 * 180 actions: ~0.00 * 220 actions: ~0.00 * 260 actions: ~0.00 * 300 actions: ~0.00 **4. Llama-4-Maverick (Red Line)** * **Trend:** The line exhibits the most severe and rapid decline. It starts at the lowest initial success rate and plummets to near-zero performance by 50 actions. * **Approximate Data Points:** * 0 actions: ~0.65 * ~20 actions: ~0.39 * ~35 actions: ~0.18 * 50 actions: ~0.05 * 100 actions: ~0.01 * 140 actions: ~0.00 * 180 actions: ~0.00 * 220 actions: ~0.00 * 260 actions: ~0.00 * 300 actions: ~0.00 ### Key Observations 1. **Universal Negative Correlation:** All four models demonstrate a clear negative correlation between the number of actions and success rate. Performance universally degrades with increased task length/complexity. 2. **Performance Hierarchy:** A consistent performance hierarchy is maintained across the entire range: GPT-5 > OSS-120B > OSS-20B > Llama-4-Maverick. 3. **Divergence in Decay Rates:** The models differ significantly in how quickly their performance decays. GPT-5 has the most gradual slope, while Llama-4-Maverick has the steepest. 4. **Convergence to Zero:** Three of the four models (OSS-120B, OSS-20B, Llama-4-Maverick) reach a success rate at or near zero by 100-150 actions. GPT-5 is the only model that maintains a measurable, albeit low, success rate (≈0.08) at 300 actions. 5. **Initial Performance Gap:** There is a significant spread in initial success rates (at 0 actions), ranging from ~0.65 (Llama-4-Maverick) to ~1.00 (GPT-5). ### Interpretation This chart likely illustrates the results of a benchmark evaluating AI models on sequential decision-making or multi-step reasoning tasks. The "Number of actions" represents the length or complexity of the task sequence required for completion. * **What the data suggests:** The data strongly suggests that maintaining performance over long action sequences is a major challenge for current AI models. The ability to handle extended context or maintain coherence over many steps appears to be a key differentiator between models, with GPT-5 showing significantly greater robustness. * **Relationship between elements:** The x-axis (complexity) is the independent variable causing the change in the y-axis (performance). The different colored lines represent different model architectures or sizes, isolating the variable of model capability. The steepness of each line is a direct visual measure of that model's "contextual robustness" or "planning horizon." * **Notable patterns and anomalies:** * The most striking pattern is the **exponential-like decay** for OSS-20B and Llama-4-Maverick, suggesting a critical failure point is reached relatively early in the action sequence. * The **near-perfect initial performance** of GPT-5 and OSS-120B at 0 actions indicates they can solve the base task flawlessly, but the challenge lies entirely in scaling that success. * The **plateauing of GPT-5's curve** between 220-260 actions (≈0.18 to ≈0.17) before a final drop is a minor anomaly that could indicate a subset of tasks solvable within that action range or a measurement artifact. In summary, the chart provides a clear, quantitative comparison showing that while all models struggle with longer tasks, there is a substantial performance gap, with larger or more advanced models (like GPT-5) demonstrating a markedly superior ability to sustain performance as task complexity grows.