LivChart Chart Wizard lets users create charts by describing what they want in natural language.

But which AI model produces the best results?

We tested 12 popular models across 32 real-world business scenarios to find out.

This article shares what we learned about accuracy, speed, and practical reliability for dashboard generation.

Why We Tested 12 AI Models

Chart generation is one of the most practical uses of local AI in business analytics.

When a user types "show me monthly revenue by region," the AI model must:

  • understand the business intent
  • identify the correct data columns
  • select an appropriate chart type
  • generate valid configuration
  • handle edge cases and ambiguous requests

Different models handle these tasks very differently.

We wanted to understand which models work best for real business dashboard workflows, not just general chat quality.

Test Methodology

We designed 32 test scenarios covering common business analytics requests.

Scenario Categories

  • Basic charts: Bar, line, pie, area charts for standard KPIs
  • Multi-dimensional analysis: Grouped comparisons, stacked charts
  • Time-series analysis: Trends over time, period comparisons
  • Conditional formatting: Thresholds, color rules, data-driven styling
  • Multilingual prompts: Requests in Turkish and English
  • Complex queries: Multi-step analysis with follow-up questions

Evaluation Criteria

Each model was scored on:

  • Correctness: Did it generate the right chart type and data mapping?
  • Completeness: Did it include all requested elements?
  • Speed: How quickly did it respond?
  • Stability: Did it produce consistent results across similar prompts?

Speed Rankings

Response time significantly affects user experience in interactive dashboard creation.

Model CPU (no GPU) GPU / Apple Silicon
Gemma 4 E2B ~5s ~1.5s
Llama 3.1 8B ~8s ~2s
Qwen 2.5 7B ~7s ~2s
Qwen 3 8B ~10s ~3s
Mistral 7B ~6s ~2s

Speed matters for interactive use. If users wait more than 10 seconds for a chart, engagement drops significantly.

Chart Accuracy (32 Scenarios)

Model Correct Wrong Partial
Llama 3.1 8B 28/32 2 2
Qwen 2.5 7B 27/32 3 2
Gemma 4 E2B 25/32 4 3
Qwen 3 8B 26/32 3 3
Mistral 7B 24/32 5 3

Llama 3.1 8B produced the most accurate charts overall. However, the gap between top models is narrowing as newer versions are released.

Multilingual Performance

For businesses operating in Turkey and multilingual environments, Turkish language support is critical.

Model Turkish Prompt Accuracy English Prompt Accuracy Overall
Qwen 2.5 7B 26/32 27/32 Best multilingual
Qwen 3 8B 25/32 26/32 Strong multilingual
Llama 3.1 8B 22/32 28/32 English-first
Gemma 4 E2B 20/32 25/32 Weaker Turkish
Mistral 7B 19/32 24/32 Weaker Turkish

Qwen models clearly lead in multilingual scenarios. This is especially important for Turkish-language dashboard generation.

Key Takeaways

  • Llama 3.1 8B is the most accurate for chart generation overall
  • Gemma 4 E2B is the fastest, making it ideal for interactive use
  • Qwen models perform especially well with multilingual data
  • For most business users, speed matters more than the last few percentage points of accuracy

Recommended Models by Use Case

Use Case Recommended Model Why
Turkish-language dashboards Qwen 2.5 7B Best multilingual support
Fast interactive use Gemma 4 E2B Quickest response time
Maximum accuracy Llama 3.1 8B Highest chart correctness
Balanced performance Qwen 3 8B Good accuracy + multilingual
Lightweight deployment Mistral 7B Low hardware requirements

What We Learned About Chart Generation

Chart generation requires a different skill set than general chat.

Structured Output Matters

Models must produce valid, structured chart configurations. Not all models handle this equally well.

Some models generate natural language responses when a chart configuration is expected. Others produce partial configurations that break rendering.

Intent Detection Is Critical

The model must understand what type of chart the user wants from a natural language description.

"Show me revenue by region" could be a bar chart, pie chart, or treemap. The best models infer the most appropriate visualization type based on context.

Edge Cases Cause Failures

Common failure patterns include:

  • misidentifying date columns as categorical data
  • generating chart types that do not match the data structure
  • failing to handle empty or null values
  • confusing similar column names

Better models handle these edge cases more gracefully.

Consistency Across Sessions

Some models produce different charts for identical prompts across sessions.

For production dashboard generation, consistency is essential. Users need reliable, repeatable results.

Hardware Considerations

Model choice affects hardware requirements.

Model RAM Required GPU Recommended Best For
Gemma 4 E2B 8 GB Not required Lightweight workstations
Qwen 2.5 7B 8 GB Helpful General analytics
Mistral 7B 8 GB Helpful Low-resource environments
Llama 3.1 8B 16 GB Recommended High-accuracy workflows
Qwen 3 8B 16 GB Recommended Multilingual analytics

The Gap Is Narrowing

The performance difference between top models is shrinking with each release.

Models that struggled with chart generation six months ago now produce acceptable results.

This trend suggests that within the next year, most mainstream models will handle business chart generation competently.

For now, selecting the right model for your specific use case still matters significantly.

Final Thoughts

Our tests confirm that local AI models are becoming increasingly capable for business dashboard generation.

Llama 3.1 8B leads in accuracy. Gemma 4 E2B leads in speed. Qwen models lead in multilingual support.

The right choice depends on your specific requirements: language, hardware, accuracy needs, and response time expectations.

As the ecosystem evolves, we will continue testing and updating these recommendations.