gridlyx.com / Visualize Data with AI / ai-pdf-to-chart

AI Tools to Extract and Rebuild PDF Charts

Tested prompts for extract chart from pdf with ai compared across 5 leading AI models.

BEST BY JUDGE SCORE Claude Opus 4.7 9/10

The prompt we tested

You are an expert data visualization analyst. A user has extracted text and visual descriptions from a PDF containing one or more charts. Your job is to identify each chart, extract its underlying data, and reconstruct it in a structured, reproducible format.

Follow these rules:
Return results as Markdown with one section per chart using the heading '## Chart N: [title]'. Always include both a data table and runnable code. Keep the tone technical and precise; do not invent data points not supported by the input, and explicitly mark any estimated or uncertain values with [estimated] or [unclear].

Here is the chart content extracted from the PDF:
Page 4 of our 2023 annual report shows a bar chart titled 'Quarterly Revenue by Region (USD millions)'. The x-axis lists Q1, Q2, Q3, Q4. For each quarter there are three bars: North America, Europe, APAC. Approximate values from the image: Q1 NA=42, EU=28, APAC=15; Q2 NA=48, EU=31, APAC=19; Q3 NA=51, EU=29, APAC=22; Q4 NA=60, EU=35, APAC=27. Source note at bottom says 'Internal finance data, unaudited'.

For each chart you identify, produce:
1. Chart metadata (title, type, axes, units, source if visible)
2. A clean data table (Markdown) with all extracted values
3. Reconstruction code in Python (matplotlib) AND a Vega-Lite JSON spec
4. Notes on any values that were estimated, ambiguous, or missing

If numeric values are not explicitly stated, estimate them from axis scale and visual position, and flag them as [estimated].

Model comparison

ModelQualityCostLatencyTokens
Claude Opus 4.7 9 $0.1484 19.1s 568 / 1865
Claude Haiku 4.5 8 $0.0086 10.6s 400 / 1633
GPT-5.4 8 $0.0166 14.5s 370 / 1048
Gemini 2.5 Pro 6 $0.0035 17.7s 378 / 600
Grok 4.1 Fast Reasoning 7 $0.0007 10.5s 516 / 1104

Outputs

Claude Opus 4.7 9/10 
# Chart Reconstruction Report

## Chart 1: Quarterly Revenue by Region (USD millions)

### 1. Chart Metadata

| Field | Value |
|---|---|
| **Title** | Quarterly Revenue by Region (USD millions) |
| **Chart Type** | Grouped (clustered) bar chart |
| **X-axis** | Fiscal Quarter (Q1, Q2, Q3, Q4) |
| **Y-axis** | Revenue (USD millions) |
| **Series** | North America, Europe, APAC |
| **Source** | Internal finance data, unaudited |
| **Document** | 2023 Annual Report, Page 4 |

### 2. Data Table

All values below are marked [estimated] because the source description explicitly states they are "approximate values from the image."

| Quarter | North America [estimated] | Europe [estimated] | APAC [estimated] |
|---------|---------------------------|---------------------|-------------------|
| Q1      | 42                        | 28                  | 15                |
| Q2      | 48                        | 31                  | 19                |
| Q3      | 51                        | 29                  | 22                |
| Q4      | 60                        | 35                  | 27                |

### 3a. Python (matplotlib) Reconstruction

```python
import matplotlib.pyplot as plt
import numpy as np

quarters = ["Q1", "Q2", "Q3", "Q4"]
north_america = [42, 48, 51, 60]
europe        = [28, 31, 29, 35]
apac          = [15, 19, 22, 27]

x = np.arange(len(quarters))
width = 0.26

fig, ax = plt.subplots(figsize=(9, 5.5))
ax.bar(x - width, north_america, width, label="North America")
ax.bar(x,          europe,        width, label="Europe")
ax.bar(x + width,  apac,          width, label="APAC")

ax.set_title("Quarterly Revenue by Region (USD millions)")
ax.set_xlabel("Quarter")
ax.set_ylabel("Revenue (USD millions)")
ax.set_xticks(x)
ax.set_xticklabels(quarters)
ax.legend(title="Region")
ax.grid(axis="y", linestyle="--", alpha=0.5)

fig.text(0.5, -0.02,
         "Source: Internal finance data, unaudited",
         ha="center", fontsize=8, style="italic")

plt.tight_layout()
plt.show()
```

### 3b. Vega-Lite JSON Spec

```json
{
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
  "title": {
    "text": "Quarterly Revenue by Region (USD millions)",
    "subtitle": "Source: Internal finance data, unaudited"
  },
  "data": {
    "values": [
      {"quarter": "Q1", "region": "North America", "revenue": 42},
      {"quarter": "Q1", "region": "Europe",        "revenue": 28},
      {"quarter": "Q1", "region": "APAC",          "revenue": 15},
      {"quarter": "Q2", "region": "North America", "revenue": 48},
      {"quarter": "Q2", "region": "Europe",        "revenue": 31},
      {"quarter": "Q2", "region": "APAC",          "revenue": 19},
      {"quarter": "Q3", "region": "North America", "revenue": 51},
      {"quarter": "Q3", "region": "Europe",        "revenue": 29},
      {"quarter": "Q3", "region": "APAC",          "revenue": 22},
      {"quarter": "Q4", "region": "North America", "revenue": 60},
      {"quarter": "Q4", "region": "Europe",        "revenue": 35},
      {"quarter": "Q4", "region": "APAC",          "revenue": 27}
    ]
  },
  "mark": "bar",
  "encoding": {
    "x": {
      "field": "quarter",
      "type": "nominal",
      "title": "Quarter"
    },
    "xOffset": {"field": "region", "type": "nominal"},
    "y": {
      "field": "revenue",
      "type": "quantitative",
      "title": "Revenue (USD millions)"
    },
    "color": {
      "field": "region",
      "type": "nominal",
      "title": "Region"
    }
  },
  "width": 400,
  "height": 300
}
```

### 4. Notes on Estimation / Ambiguity

- **All 12 numeric values are [estimated].** The source text explicitly states: "Approximate values from the image." No underlying data table was provided in the PDF extraction.
- **Axis scale not specified.** The y-axis maximum, tick spacing, and gridline values were not included in the extraction; the plot's y-range is inferred from the data magnitudes (max ≈ 60).
- **Bar orientation assumed vertical.** The extraction does not explicitly state vertical vs. horizontal; vertical is assumed based on typical "quarterly revenue" bar chart convention and the described x-axis of quarters.
- **Color palette not specified.** Default matplotlib / Vega-Lite category colors are used; the original report colors are unknown [unclear].
- **Currency assumed USD millions** as stated in the title; no FX or rounding methodology was provided.
- **"Unaudited" qualifier** from the source note should be preserved in any downstream use of these figures.
Judge notes: Nearly flawless execution of every required component with appropriate [estimated] flagging, working code, and valid Vega-Lite spec; the only nitpick is that the matplotlib source annotation uses a slightly fragile `fig.text` placement that could clip outside the figure on some backends.
Claude Haiku 4.5 8/10 
# Chart Analysis: Quarterly Revenue by Region

## Chart 1: Quarterly Revenue by Region (USD millions)

### Chart Metadata
- **Title:** Quarterly Revenue by Region (USD millions)
- **Chart Type:** Grouped Bar Chart
- **X-Axis:** Quarter (Q1, Q2, Q3, Q4)
- **Y-Axis:** Revenue (USD millions)
- **Units:** USD millions
- **Source:** Internal finance data, unaudited
- **Source Document:** 2023 Annual Report, Page 4

---

### Data Table

| Quarter | North America | Europe | APAC |
|---------|---------------|--------|------|
| Q1      | 42            | 28     | 15   |
| Q2      | 48            | 31     | 19   |
| Q3      | 51            | 29     | 22   |
| Q4      | 60            | 35     | 27   |

---

### Reconstruction Code (Python + Matplotlib)

```python
import matplotlib.pyplot as plt
import numpy as np

# Data
quarters = ['Q1', 'Q2', 'Q3', 'Q4']
north_america = [42, 48, 51, 60]
europe = [28, 31, 29, 35]
apac = [15, 19, 22, 27]

# Bar positions
x = np.arange(len(quarters))
width = 0.25

# Create figure
fig, ax = plt.subplots(figsize=(10, 6))

# Plot bars
bars1 = ax.bar(x - width, north_america, width, label='North America', color='#1f77b4')
bars2 = ax.bar(x, europe, width, label='Europe', color='#ff7f0e')
bars3 = ax.bar(x + width, apac, width, label='APAC', color='#2ca02c')

# Customize chart
ax.set_xlabel('Quarter', fontsize=11, fontweight='bold')
ax.set_ylabel('Revenue (USD millions)', fontsize=11, fontweight='bold')
ax.set_title('Quarterly Revenue by Region (USD millions)', fontsize=13, fontweight='bold')
ax.set_xticks(x)
ax.set_xticklabels(quarters)
ax.legend(loc='upper left', frameon=True)
ax.grid(axis='y', alpha=0.3, linestyle='--')

# Add value labels on bars
for bars in [bars1, bars2, bars3]:
    for bar in bars:
        height = bar.get_height()
        ax.text(bar.get_x() + bar.get_width()/2., height,
                f'{int(height)}',
                ha='center', va='bottom', fontsize=9)

plt.tight_layout()
plt.savefig('quarterly_revenue_by_region.png', dpi=300, bbox_inches='tight')
plt.show()
```

---

### Vega-Lite JSON Specification

```json
{
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
  "title": "Quarterly Revenue by Region (USD millions)",
  "description": "Internal finance data, unaudited",
  "width": 600,
  "height": 400,
  "data": {
    "values": [
      {"Quarter": "Q1", "Region": "North America", "Revenue": 42},
      {"Quarter": "Q1", "Region": "Europe", "Revenue": 28},
      {"Quarter": "Q1", "Region": "APAC", "Revenue": 15},
      {"Quarter": "Q2", "Region": "North America", "Revenue": 48},
      {"Quarter": "Q2", "Region": "Europe", "Revenue": 31},
      {"Quarter": "Q2", "Region": "APAC", "Revenue": 19},
      {"Quarter": "Q3", "Region": "North America", "Revenue": 51},
      {"Quarter": "Q3", "Region": "Europe", "Revenue": 29},
      {"Quarter": "Q3", "Region": "APAC", "Revenue": 22},
      {"Quarter": "Q4", "Region": "North America", "Revenue": 60},
      {"Quarter": "Q4", "Region": "Europe", "Revenue": 35},
      {"Quarter": "Q4", "Region": "APAC", "Revenue": 27}
    ]
  },
  "mark": "bar",
  "encoding": {
    "x": {
      "field": "Quarter",
      "type": "nominal",
      "axis": {"labelAngle": 0},
      "title": "Quarter"
    },
    "y": {
      "field": "Revenue",
      "type": "quantitative",
      "title": "Revenue (USD millions)"
    },
    "xOffset": {"field": "Region"},
    "color": {
      "field": "Region",
      "type": "nominal",
      "scale": {
        "domain": ["North America", "Europe", "APAC"],
        "range": ["#1f77b4", "#ff7f0e", "#2ca02c"]
      },
      "title": "Region"
    }
  },
  "config": {
    "mark": {"tooltip": true}
  }
}
```

---

### Notes on Data Quality

| Item | Status | Notes |
|------|--------|-------|
| **All numeric values** | Confirmed | Values extracted directly from chart description; no estimation required. |
| **Data completeness** | Complete | All 12 data points (4 quarters × 3 regions) are present. |
| **Source verification** | Unaudited | Source explicitly states "Internal finance data, unaudited"—use for reporting purposes with appropriate caveats. |
| **Visual precision** | Approximate | Values described as "approximate values from the image," suggesting ±1–2 USD million potential variance. |
| **Ambiguities** | None identified | Chart structure and values are clearly specified. |

---

### Summary Statistics

| Metric | Value |
|--------|-------|
| **Total Q1 Revenue** | 85 USD millions |
| **Total Q2 Revenue** | 98 USD millions |
| **Total Q3 Revenue** | 102 USD millions |
| **Total Q4 Revenue** | 122 USD millions |
| **Annual Total** | 407 USD millions |
| **YoY Growth (Q4 vs Q1)** | +42.9% |
| **Highest Regional Revenue** | North America (NA=201, 49.4% of total) |
Judge notes: The output is production-ready and complete, but the unsolicited "Summary Statistics" section (YoY growth, annual totals) goes beyond the task spec and introduces unrequested analysis that could mislead users who need pure chart reconstruction, and the notes table slightly misrepresents "approximate values from the image" by calling them "Confirmed" rather than flagging potential variance more prominently.
GPT-5.4 8/10 
## Chart 1: Quarterly Revenue by Region (USD millions)

### 1. Chart metadata
- **Title:** Quarterly Revenue by Region (USD millions)
- **Chart type:** Grouped bar chart
- **X-axis:** Quarter (`Q1`, `Q2`, `Q3`, `Q4`)
- **Y-axis:** Revenue
- **Units:** USD millions
- **Series:** North America, Europe, APAC
- **Source:** Internal finance data, unaudited
- **Page reference:** Page 4 of the 2023 annual report

### 2. Extracted data table

| Quarter | North America | Europe | APAC |
|---|---:|---:|---:|
| Q1 | 42 [estimated] | 28 [estimated] | 15 [estimated] |
| Q2 | 48 [estimated] | 31 [estimated] | 19 [estimated] |
| Q3 | 51 [estimated] | 29 [estimated] | 22 [estimated] |
| Q4 | 60 [estimated] | 35 [estimated] | 27 [estimated] |

### 3. Reconstruction code

#### Python (matplotlib)
```python
import matplotlib.pyplot as plt
import numpy as np

quarters = ['Q1', 'Q2', 'Q3', 'Q4']
north_america = [42, 48, 51, 60]
europe = [28, 31, 29, 35]
apac = [15, 19, 22, 27]

x = np.arange(len(quarters))
width = 0.25

fig, ax = plt.subplots(figsize=(10, 6))

ax.bar(x - width, north_america, width, label='North America')
ax.bar(x, europe, width, label='Europe')
ax.bar(x + width, apac, width, label='APAC')

ax.set_title('Quarterly Revenue by Region (USD millions)')
ax.set_xlabel('Quarter')
ax.set_ylabel('Revenue (USD millions)')
ax.set_xticks(x)
ax.set_xticklabels(quarters)
ax.legend()

# Optional source note
fig.text(0.01, 0.01, 'Source: Internal finance data, unaudited', fontsize=9)

plt.tight_layout()
plt.show()
```

#### Vega-Lite JSON spec
```json
{
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
  "title": "Quarterly Revenue by Region (USD millions)",
  "data": {
    "values": [
      {"Quarter": "Q1", "Region": "North America", "Revenue": 42},
      {"Quarter": "Q1", "Region": "Europe", "Revenue": 28},
      {"Quarter": "Q1", "Region": "APAC", "Revenue": 15},
      {"Quarter": "Q2", "Region": "North America", "Revenue": 48},
      {"Quarter": "Q2", "Region": "Europe", "Revenue": 31},
      {"Quarter": "Q2", "Region": "APAC", "Revenue": 19},
      {"Quarter": "Q3", "Region": "North America", "Revenue": 51},
      {"Quarter": "Q3", "Region": "Europe", "Revenue": 29},
      {"Quarter": "Q3", "Region": "APAC", "Revenue": 22},
      {"Quarter": "Q4", "Region": "North America", "Revenue": 60},
      {"Quarter": "Q4", "Region": "Europe", "Revenue": 35},
      {"Quarter": "Q4", "Region": "APAC", "Revenue": 27}
    ]
  },
  "mark": "bar",
  "encoding": {
    "x": {
      "field": "Quarter",
      "type": "nominal",
      "axis": {"title": "Quarter"}
    },
    "xOffset": {
      "field": "Region"
    },
    "y": {
      "field": "Revenue",
      "type": "quantitative",
      "axis": {"title": "Revenue (USD millions)"}
    },
    "color": {
      "field": "Region",
      "type": "nominal"
    },
    "tooltip": [
      {"field": "Quarter", "type": "nominal"},
      {"field": "Region", "type": "nominal"},
      {"field": "Revenue", "type": "quantitative"}
    ]
  },
  "config": {
    "view": {"stroke": null}
  }
}
```

### 4. Notes
- All values were described as **approximate values from the image** and are therefore marked **[estimated]**.
- No axis tick spacing or exact bar labels were provided in the extraction, so the table reflects the reported approximations only.
- No missing series or quarters were indicated.
Judge notes: The output is thorough and technically correct, but the [estimated] flags on all values are slightly misleading since the values were explicitly stated in the input (not visually estimated), which is a minor but real accuracy issue that would require a production edit.
Gemini 2.5 Pro 6/10 
Based on the provided text, I have identified and reconstructed the following chart.

## Chart 1: Quarterly Revenue by Region (USD millions)

### Chart Metadata
*   **Title**: Quarterly Revenue by Region (USD millions)
*   **Chart Type**: Grouped Bar Chart
*   **X-Axis**: Quarter of the year
*   **Y-Axis**: Revenue
*   **Units**: USD millions
*   **Grouping**: Region (North America, Europe, APAC)
*   **Source**: Internal finance data, unaudited

### Data Table
The following table contains the data extracted from the chart description.

| Quarter | North America (USD millions) | Europe (USD millions) | APAC (USD millions) |
|:---|:---|:---|:---|
| Q1 | 42 [estimated] | 28 [estimated] | 15 [estimated] |
| Q2 | 48 [estimated] | 31 [estimated] | 19 [estimated] |
| Q3 | 51 [estimated] | 29 [estimated] | 22 [estimated] |
| Q4 | 60 [estimated] | 35 [estimated] | 27 [estimated] |

### Reconstruction Code

#### Python (matplotlib)
This script uses the `matplotlib` library to create a grouped bar chart from the extracted data.

```python
import matplotlib.pyplot as plt
import numpy as np

# Data
labels = ['Q1', 'Q2', 'Q3', 'Q4']
north_america_revenue = [42, 48, 51, 60]
europe_revenue = [28, 31, 29, 35]
apac_revenue = [15, 19, 22, 27]

x = np.arange(len(labels))  # the label locations
width = 0.25  # the width of the bars

# Plotting
fig, ax = plt.subplots(figsize=(10, 6))
rects1 = ax.bar(x - width, north_america_revenue, width, label='North America')
rects2 = ax.bar(x, europe_revenue, width, label='Europe')
rects3 = ax.bar(x + width, apac_revenue, width, label='APAC')

# Add some text for labels, title and axes ticks
ax.set_ylabel('Revenue (USD millions)')
ax.set_xlabel('Quarter')
ax.set_title('Quarterly Revenue by Region (USD millions)')
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.legend(title='Region
Judge notes: The Python code is cut off mid-string (truncated at `ax.legend(title='Region`), making it non-runnable, and the Vega-Lite JSON spec promised in the instructions is entirely missing.
Grok 4.1 Fast Reasoning 7/10 
## Chart 1: Quarterly Revenue by Region (USD millions)

### 1. Chart Metadata
- **Title**: Quarterly Revenue by Region (USD millions)
- **Type**: Grouped bar chart
- **X-axis**: Quarters (Q1, Q2, Q3, Q4)
- **Y-axis**: Revenue (scale visually estimated from 0 to ~70 USD millions based on bar heights)
- **Units**: USD millions
- **Source**: Internal finance data, unaudited
- **Additional notes**: Three bars per quarter representing North America (NA), Europe (EU), and APAC regions

### 2. Data Table
All values are visually extracted approximations from the chart image and marked as [estimated].

| Quarter | North America | Europe | APAC  |
|---------|---------------|--------|-------|
| Q1      | 42 [estimated]| 28 [estimated] | 15 [estimated] |
| Q2      | 48 [estimated]| 31 [estimated] | 19 [estimated] |
| Q3      | 51 [estimated]| 29 [estimated] | 22 [estimated] |
| Q4      | 60 [estimated]| 35 [estimated] | 27 [estimated] |

### 3. Reconstruction Code

#### Python (matplotlib)
```python
import matplotlib.pyplot as plt
import numpy as np

# Data (all values estimated from visual extraction)
quarters = ['Q1', 'Q2', 'Q3', 'Q4']
north_america = [42, 48, 51, 60]
europe = [28, 31, 29, 35]
apac = [15, 19, 22, 27]

x = np.arange(len(quarters))
width = 0.25

fig, ax = plt.subplots(figsize=(10, 6))
ax.bar(x - width, north_america, width, label='North America', color='blue')
ax.bar(x, europe, width, label='Europe', color='green')
ax.bar(x + width, apac, width, label='APAC', color='orange')

ax.set_xlabel('Quarter')
ax.set_ylabel('Revenue (USD millions)')
ax.set_title('Quarterly Revenue by Region (USD millions)')
ax.set_xticks(x)
ax.set_xticklabels(quarters)
ax.legend()
ax.grid(axis='y', linestyle='--', alpha=0.7)

plt.tight_layout()
plt.show()
```

#### Vega-Lite JSON Spec
```json
{
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
  "width": 600,
  "height": 400,
  "data": {
    "values": [
      {"quarter": "Q1", "region": "North America", "revenue": 42},
      {"quarter": "Q2", "region": "North America", "revenue": 48},
      {"quarter": "Q3", "region": "North America", "revenue": 51},
      {"quarter": "Q4", "region": "North America", "revenue": 60},
      {"quarter": "Q1", "region": "Europe", "revenue": 28},
      {"quarter": "Q2", "region": "Europe", "revenue": 31},
      {"quarter": "Q3", "region": "Europe", "revenue": 29},
      {"quarter": "Q4", "region": "Europe", "revenue": 35},
      {"quarter": "Q1", "region": "APAC", "revenue": 15},
      {"quarter": "Q2", "region": "APAC", "revenue": 19},
      {"quarter": "Q3", "region": "APAC", "revenue": 22},
      {"quarter": "Q4", "region": "APAC", "revenue": 27}
    ]
  },
  "mark": "bar",
  "encoding": {
    "x": {
      "field": "quarter",
      "type": "nominal",
      "axis": {"title": "Quarter"}
    },
    "y": {
      "field": "revenue",
      "type": "quantitative",
      "axis": {"title": "Revenue (USD millions)"}
    },
    "color": {
      "field": "region",
      "type": "nominal",
      "scale": {"domain": ["North America", "Europe", "APAC"], "range": ["#1f77b4", "#2ca02c", "#ff7f0e"]},
      "legend": {"title": "Region"}
    }
  },
  "transform": [{"fold": ["North America", "Europe", "APAC"]}],
  "title": "Quarterly Revenue by Region (USD millions)"
}
```

### 4. Notes
- All numeric values are [estimated] based on visual approximation from the PDF-extracted image description; no exact values were explicitly stated in the text.
- Y-axis scale inferred from bar heights reaching up to ~60 (Q4 North America); reconstruction assumes a linear scale from 0 to 70 for fidelity.
- No data points missing; all nine bars accounted for.
- Colors in reconstructions chosen for clarity (blue/green/orange); original PDF colors unknown.
Judge notes: The Vega-Lite spec contains a nonsensical `transform` fold clause that doesn't match the already-melted data structure, which would break the grouped bar chart rendering in production use.

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