Pixtral Large

Pixtral Large: Revolutionizing Multimodal AI with Superior Performance

Explore Pixtral Large by Mistral AI, a 124B-parameter multimodal model excelling in text, image, and document understanding. Learn about its innovative use cases, benchmark performance, and enterprise features.

Pixtral Large: Revolutionizing Multimodal AI with Superior Performance
Unleashing the power of Pixtral Large: A new era in Multimodal AI.

Exploring Mistral AI’s Pixtral Large: The New Benchmark in Multimodal AI

On November 18, 2024, Mistral AI unveiled Pixtral Large, a cutting-edge multimodal model extending its Mistral Large 2 foundation. With advanced capabilities in image, text, and document understanding, Pixtral Large promises to redefine AI’s utility across sectors, setting new standards in performance and accessibility.

What is Pixtral Large?

Pixtral Large is a 124-billion-parameter multimodal model designed to excel in understanding and reasoning over complex visual and textual data. Here’s a quick overview of its core attributes:

  • Architecture: Combines a 123B text decoder with a 1B-parameter vision encoder.
  • Context Capacity: 128K tokens, accommodating up to 30 high-resolution images alongside textual inputs.
  • Performance Benchmarks: Achieves frontier-level scores on tasks like MathVista, ChartQA, and DocVQA, showcasing superior multimodal reasoning abilities.

The model is available under two licenses:

  • Mistral Research License (MRL) for research and educational purposes.
  • Mistral Commercial License for enterprise experimentation and production.

Performance Metrics: Where Pixtral Large Excels

Comparison of Pixtral Large with leading multimodal models across MM-MT-Bench and accuracy benchmarks such as MMMU, MathVista, and more.

Mistral AI has benchmarked Pixtral Large against leading models like GPT-4o, Gemini-1.5 Pro, and Claude-3.5 Sonnet, with notable outcomes:

  1. MathVista: Scores 69.4%, surpassing all competitors in mathematical reasoning over visual data.
  2. ChartQA & DocVQA: Outperforms GPT-4o and Gemini-1.5 Pro, excelling in reasoning over complex charts and documents.
  3. MM-MT-Bench: Leads in multimodal real-world tasks, solidifying its utility across diverse scenarios.

The model’s ability to handle OCR, multilingual understanding, and complex visual reasoning further strengthens its position in the AI landscape.

Detailed benchmarks
Detailed benchmark results comparing Pixtral Large with other leading models on key multimodal tasks.

Innovative Use Cases

Pixtral Large demonstrates versatility across industries, supporting use cases such as:

  • Financial Analysis: Extracting insights from financial charts and reports.
  • Education: Assisting with mathematical problem-solving and multimodal content generation.
  • Customer Support: Enhancing visual-text query resolution for better customer experiences.

Qualitative Insights: Real-World Examples

Multilingual OCR
Prompt: “I bought the Medu Wada. How much do I owe? Add an 10% tip.

An example receipt parsed by Pixtral Large, showcasing its multilingual OCR and arithmetic reasoning capabilities.
    • Pixtral Large accurately parses an English receipt, calculates totals, and applies a tip.

Response

To determine the total amount you owe including a 10% tip, let's break down the calculations step-by-step:

  1. Subtotal: The cost of the Medu Wada is 65.00.
  2. Discount: A 10% discount is applied, which is 6.00.
  3. Net Total: Subtract the discount from the subtotal: 65.00 - 6.00 = 59.00.
  4. Taxes:
    • CGST @9%: 5.31
    • SGST @9%: 5.31
    • Total Tax: 5.31 + 5.31 = 10.62
  6. Grand Total: Add the net total and the total tax: 59.00 + 10.62 = 70.00.

Now, let's calculate the 10% tip on the grand total:

  • Tip: 10% of 70.00 = 0.10 * 70.00 = 7.00

Finally, add the tip to the grand total:

  • Total Amount Owed: 70.00 + 7.00 = 77.00

So, you owe a total of 77.00 including the 10% tip.

Chart Analysis:
Prompt: “can you explain to me this chart, and when did the export of cotton go wrong?”

A cotton production and export chart analyzed by Pixtral Large, highlighting trends and identifying anomalies.
    • The model identifies instability points in a training loss curve, pinpointing issues in AI model development.

Response

This chart presents data on cotton production and exports in India over several years, from 2000-01 to 2016-17. Here's a detailed explanation:

  1. Production of Cotton in Gujarat (Green Bars):
    • The green bars represent the production of cotton in Gujarat in million bales.
    • The production shows a general upward trend with some fluctuations.
  3. Production of Cotton in India (Red Bars):
    • The red bars represent the total production of cotton in India in million bales.
    • There is a clear upward trend in cotton production over the years, with significant increases in certain years.
  5. Export of Raw Cotton (Black Line):
    • The black line represents the export of raw cotton from India in million USD.
    • The exports show significant fluctuations over the years.

Key Observations:

  • 2000-01 to 2004-05: Both Gujarat and India's cotton production were relatively low, and exports were minimal.
  • 2005-06 to 2008-09: There was a notable increase in both production and exports.
  • 2009-10 to 2011-12: Production continued to rise, and exports peaked in 2011-12 at 4258 million USD.
  • 2012-13 to 2014-15: Production remained high, but exports dropped significantly after 2011-12.
  • 2015-16 to 2016-17: Production showed a slight decline, but exports started to recover.

When Did the Export of Cotton Go Wrong?

The export of cotton appears to have gone wrong starting in 2012-13. After reaching a peak of 4258 million USD in 2011-12, exports dropped sharply to 2625 million USD in 2012-13 and continued to decline in the following years, reaching a low of 1595 million USD in 2015-16. This significant drop indicates a period of struggle for cotton exports despite relatively high production levels.

Possible Reasons for the Decline:

  • Global Market Conditions: Changes in global demand or competition from other cotton-producing countries.
  • Policy Changes: Government policies affecting exports, such as changes in export subsidies or regulations.
  • Quality Issues: Potential issues with the quality of cotton affecting its demand in the international market.
  • Economic Factors: Economic conditions in importing countries that could affect their ability to purchase cotton.

In summary, the chart shows a significant decline in cotton exports starting in 2012-13, despite continued high production levels in India.

Enterprise Utility:


Pixtral Large identifies companies using Mistral models, such as BNP Paribas and Cloudflare, demonstrating its capability in data extraction and semantic understanding.

Enterprise Features: The New Mistral Large 24.11 Update

Mistral AI also announced an updated Mistral Large 24.11, enhancing:

  • Long-context understanding.
  • Function-calling accuracy.
  • Performance in retrieval-augmented generation (RAG) and agent-based workflows.

This model is tailored for enterprise needs, including:

  • Document comprehension.
  • Task automation.
  • Enhanced customer interactions.

How to Access Pixtral Large

Pixtral Large is accessible through:

  1. Le Chat platform: Integrated multimodal interactions.
  2. API: Available under pixtral-large-latest.
  3. Hugging Face: Downloadable for research or commercial use.

For enterprises, deployment via Google Cloud and Microsoft Azure is expected within the week.

Final Thoughts

Pixtral Large represents a significant leap in multimodal AI, blending robust text and image understanding with unparalleled reasoning abilities. Whether applied to enterprise workflows, educational contexts, or research, its versatility positions it as a transformative tool for the AI era.

Key Takeaway: With Pixtral Large, Mistral AI sets a new benchmark for multimodal performance, cementing its role in driving AI innovation across domains.

Explore Pixtral Large Today
Visit Mistral AI to learn more about Pixtral Large and access the model.

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