## [Line Charts]: Performance of Two AI Models Under Increasing Noise
### Overview
The image displays three horizontally arranged line charts comparing the performance of two AI models—**Llama-4-maverick-17b-128e-instruct-fp8** (blue line) and **Gemini-2.5-flash-preview-04-17** (orange line)—across three different metrics as a function of "Noise ratio." The charts share a common x-axis but have different y-axes, measuring "Mean progress ratio," "Mean success rate (pass@1)," and "Cot tokens," respectively.
### Components/Axes
* **Legend:** Located in the top-left corner of the first (leftmost) chart. It identifies the two data series:
* Blue line with circle markers: `(Llama-4-maverick-17b-128e-instruct-fp8)`
* Orange line with circle markers: `(Gemini-2.5-flash-preview-04-17)`
* **Common X-Axis (All Charts):**
* **Label:** `Noise ratio`
* **Scale:** Linear, from `0.00` to `1.00`.
* **Tick Marks:** `0.00`, `0.25`, `0.50`, `0.75`, `1.00`.
* **Left Chart Y-Axis:**
* **Label:** `Mean progress ratio`
* **Scale:** Linear, from `0.0` to `1.0`.
* **Tick Marks:** `0.0`, `0.2`, `0.4`, `0.6`, `0.8`, `1.0`.
* **Middle Chart Y-Axis:**
* **Label:** `Mean success rate (pass@1)`
* **Scale:** Linear, from `0.0` to `1.0`.
* **Tick Marks:** `0.0`, `0.2`, `0.4`, `0.6`, `0.8`, `1.0`.
* **Right Chart Y-Axis:**
* **Label:** `Cot tokens`
* **Scale:** Linear, from `500` to `1750`.
* **Tick Marks:** `500`, `750`, `1000`, `1250`, `1500`, `1750`.
### Detailed Analysis
#### Chart 1: Mean Progress Ratio vs. Noise Ratio
* **Trend Verification:** Both lines show a clear downward trend as noise increases. The orange line (Gemini) has a steeper negative slope than the blue line (Llama).
* **Data Points (Approximate):**
* **Llama (Blue):** Starts at ~0.24 (Noise=0.00), decreases gradually to ~0.18 (0.25), ~0.16 (0.50), ~0.12 (0.75), and ends at ~0.11 (1.00).
* **Gemini (Orange):** Starts significantly higher at ~0.72 (0.00), decreases to ~0.65 (0.25), ~0.54 (0.50), ~0.38 (0.75), and ends at ~0.24 (1.00).
* **Key Observation:** Gemini consistently achieves a higher mean progress ratio than Llama across all noise levels, though the gap narrows as noise increases.
#### Chart 2: Mean Success Rate (pass@1) vs. Noise Ratio
* **Trend Verification:** Both lines show a strong downward trend. The orange line (Gemini) starts high and declines sharply. The blue line (Llama) starts very low and declines to near zero.
* **Data Points (Approximate):**
* **Llama (Blue):** Starts very low at ~0.03 (0.00), drops to ~0.01 (0.25), and is at or near `0.0` for noise ratios of 0.50, 0.75, and 1.00.
* **Gemini (Orange):** Starts at ~0.62 (0.00), decreases to ~0.50 (0.25), ~0.34 (0.50), ~0.19 (0.75), and ends at ~0.04 (1.00).
* **Key Observation:** Gemini has a substantially higher success rate than Llama at all noise levels. Llama's success rate is negligible, especially beyond a noise ratio of 0.25.
#### Chart 3: Cot Tokens vs. Noise Ratio
* **Trend Verification:** The blue line (Llama) shows a clear downward trend. The orange line (Gemini) is relatively flat with a very slight upward trend.
* **Data Points (Approximate):**
* **Llama (Blue):** Starts at ~1720 tokens (0.00), decreases to ~1640 (0.25), ~1620 (0.50), ~1510 (0.75), and ends at ~1460 (1.00).
* **Gemini (Orange):** Remains consistently low, starting at ~350 tokens (0.00), with values around ~350 (0.25), ~360 (0.50), ~355 (0.75), and ~370 (1.00).
* **Key Observation:** There is a massive disparity in token usage. Llama uses approximately 4-5 times more Chain-of-Thought (Cot) tokens than Gemini across all noise levels. Llama's token count decreases with noise, while Gemini's remains stable.
### Key Observations
1. **Performance Hierarchy:** Gemini-2.5-flash-preview-04-17 outperforms Llama-4-maverick-17b-128e-instruct-fp8 on both "Mean progress ratio" and "Mean success rate" metrics at every tested noise level.
2. **Noise Sensitivity:** Both models' performance metrics degrade as the noise ratio increases. The degradation in success rate is particularly severe for both.
3. **Efficiency Disparity:** The models exhibit opposite behaviors in resource usage. The higher-performing Gemini model uses a consistently low and stable number of Cot tokens. The lower-performing Llama model uses a very high number of Cot tokens, which decreases as noise increases (possibly indicating a failure to generate lengthy reasoning chains in noisy conditions).
4. **Llama's Near-Zero Success:** Llama's mean success rate (pass@1) is effectively zero for noise ratios of 0.50 and above, indicating a complete failure to produce correct final answers under moderate to high noise.
### Interpretation
The data suggests a fundamental trade-off or difference in model architecture/behavior between the two tested models. **Gemini-2.5-flash-preview-04-17** appears to be both more robust (higher performance) and more efficient (lower token usage) on this specific task under noisy conditions. Its performance degrades gracefully with noise.
In contrast, **Llama-4-maverick-17b-128e-instruct-fp8** struggles significantly. Its low progress and near-zero success rates, coupled with very high token consumption, indicate it may be generating lengthy but ineffective reasoning chains ("Cot tokens") that fail to lead to correct solutions, especially when the input is corrupted by noise. The decrease in its token count with higher noise might reflect an inability to sustain coherent reasoning rather than increased efficiency.
The "Noise ratio" likely represents the proportion of corrupted or irrelevant information in the input prompt. The charts demonstrate that maintaining performance in the presence of such noise is a critical challenge, and the two models handle it with vastly different levels of efficacy and efficiency. This analysis would be crucial for selecting a model for real-world applications where input data may be imperfect.
