turns-00037.parquet:41224
53b6e2731ae997d30cd390f7
turn 9/11gpt-4o-2024-08-06EnglishArgentina466 words
degenerate_repetitionAbsentFinal dense release
USER
1. data seems correct 2. There isn't any NaN characters 3. They are correctly set as float64 4 the data seems correct but the numbers are really small name pricing.input_price \ 0 Lumimaid v0.2 70B 3.375000e-06 1 Magnum v4 72B 1.875000e-06 2 Anthropic: Claude 3.5 Sonnet 3.000000e-06 3 Anthropic: Claude 3.5 Sonnet (self-moderated) 3.000000e-06 4 xAI: Grok Beta 5.000000e-06 5 Ministral 8B 1.000000e-07 6 Ministral 3B 4.000000e-08 7 Qwen2.5 7B Instruct 2.700000e-07 8 NVIDIA: Llama 3.1 Nemotron 70B Instruct 3.500000e-07 9 Inflection: Inflection 3 Pi 2.500000e-06 10 Inflection: Inflection 3 Productivity 2.500000e-06 11 Google: Gemini 1.5 Flash-8B 3.750000e-08 12 Liquid: LFM 40B MoE (free) 0.000000e+00 13 Liquid: LFM 40B MoE 1.000000e-06 14 Rocinante 12B 2.500000e-07 maybe multiplying them for 1 million will solve this problem? Since in the website of Open Router the prices are in $/1M token? pricing.output_price 0 4.500000e-06 1 2.250000e-06 2 1.500000e-05 3 1.500000e-05 4 1.500000e-05 5 1.000000e-07 6 4.000000e-08 ... 11 1.500000e-07 12 0.000000e+00 13 2.000000e-06 14 5.000000e-07
ASSISTANT
The issue you're encountering seems to be related to the scale of the data. If the pricing is expressed in dollars per million tokens, and you're seeing very small numbers (e.g., `3.375000e-06`), multiplying the prices by 1,000,000 should bring them into a more comprehensible range for visualization purposes. This will not only make the plots clearer but also easier to compare.
Here's how you can scale the prices and then generate the plots:
```python
import pandas as pd
import matplotlib.pyplot as plt
# Assuming `df` is your DataFrame
# Scale the prices to be per million tokens
df['pricing.input_price'] *= 1_000_000
df['pricing.output_price'] *= 1_000_000
# Calculate the average price for each model
df['average_price'] = (df['pricing.input_price'] + df['pricing.output_price']) / 2
# Sort models by average price and select top 10 cheapest and most expensive
cheapest_models = df.nsmallest(10, 'average_price')
most_expensivier_models = df.nlargest(10, 'average_price')
# Remove 'average_price' column if you don't want to keep it
df.drop(columns=['average_price'], inplace=True)
def plot_bar(df_to_plot, price_column, title, color):
plt.figure(figsize=(14, 7))
plt.bar(df_to_plot['name'], df_to_plot[price_column], color=color)
plt.title(title)
plt.xlabel('Model Name')
plt.ylabel(f"{price_column.replace('pricing.', '').capitalize()} ($/million tokens)")
plt.xticks(rotation=90)
plt.tight_layout()
plt.show()
# Plot for cheapest models
plot_bar(cheapest_models, 'pricing.input_price', 'Cheapest 10 Models by Input Price', 'skyblue')
plot_bar(cheapest_models, 'pricing.output_price', 'Cheapest 10 Models by Output Price', 'skyblue')
# Plot for the most expensive models
plot_bar(most_expensivier_models, 'pricing.input_price', 'Most Expensive 10 Models by Input Price', 'salmon')
plot_bar(most_expensivier_models, 'pricing.output_price', 'Most Expensive 10 Models by Output Price', 'salmon')
```
### Explanation:
- **Scaling Prices:** Prices are multiplied by 1,000,000 to convert them from per-token prices to per-million-tokens, aligning with the displayed units on Open Router.
- **Plotting Adjustments:** Labels and titles in the plots are updated to reflect this scaling for clarity.
- **Bar Plot Visualization:** With the adjusted scale, the plots should display varying price levels more distinctly, aiding comparison across models.
These changes should improve plot readability and ensure pricing is correctly represented on a per-million-token basis.