Shocking Leak Reveals AI Model 100x Leaner And 10x Stronger (Avocado AI)

Meta's Avocado, Google's Paper Banana & Gemini UI Capabilities: A Detailed Overview

Key Concepts:

• Avocado (Meta): Meta’s next-generation AI model, reportedly outperforming Llama 4 in efficiency and performance.
• Llama (Meta): Meta’s series of open-source large language models.
• Mango (Meta): Meta’s upcoming model focused on high-fidelity image and video generation.
• Paper Banana (Google): A framework utilizing multi-agent AI to automate the creation of scientific figures for research papers.
• Gemini (Google): Google’s large language model, with a new checkpoint demonstrating strong UI/SVG generation capabilities.
• SVG (Scalable Vector Graphics): An XML-based vector image format for two-dimensional graphics with support for interactivity and animation.
• Base Model: A pre-trained AI model before fine-tuning for specific tasks.
• Multimodality: The ability of an AI model to process and understand multiple types of data (text, images, audio, etc.).
• Mixture of Experts (MoE): An architecture where different parts of the model specialize in different tasks.

I. Meta’s “Avocado” – A Leap in AI Efficiency

According to an internal memo from Meta’s Elite Super Intelligence Labs, the company is developing a new AI model, codenamed “Avocado,” that is significantly outperforming the Llama 4 series. Megan Fu, a product manager within the labs, authored the memo, describing Avocado as Meta’s “most capable pre-trained base model so far.” This is significant as a base model represents the state after pre-training but before specialized refinement.

Internal testing allegedly shows Avocado achieving 10x compute efficiency gains on text tasks compared to Llama 4 Maverick, Meta’s previous flagship high-performance model. This efficiency jump is crucial, as the cost of serving frontier-level models at scale is a major challenge. Avocado also demonstrates over 100x efficiency improvement compared to Llama 4 Behemoth, a model previously considered resource-intensive. This suggests a shift away from the “bigger is always better” approach towards smarter training and design.

Avocado is described as a “ground-up rebuild,” differing from Llama 4’s focus on native multimodality and mixture of experts architecture. It has completed pre-training and is already reportedly matching the performance of fine-tuned, post-training optimized models in knowledge and visual perception. This implies Avocado possesses unusually strong inherent capabilities even before task-specific training.

The development of Avocado marks the first major milestone for Meta’s Super Intelligence Labs, formed in 2025 and led by Alexander Wang, founder of Scale AI. Wang’s background emphasizes data quality, training pipelines, and model reliability – suggesting a focus on building models that are deployable and robust.

II. Strategic Implications for Meta: Open Source vs. Closed Models

The Avocado development potentially signals a shift in Meta’s AI strategy. The Llama series established Meta as an open-source champion, fostering community adoption and developer contributions. However, the memo reinforces rumors of a move towards a closed model strategy for Avocado. The rationale is that a truly superior model’s weights should be kept private to prevent competitors from gaining a free advantage and to facilitate monetization through premium enterprise tools and product lock-in. This potential shift is linked to the departure of Yan LeCun, Meta’s longtime AI chief and a proponent of open development.

Meta is aiming for a first-half 2026 launch for Avocado, potentially in Q1 or early spring. Alongside Avocado, Meta is also developing “Mango,” a model focused on high-fidelity image and video generation, aiming for a full-stack solution encompassing text, code, and visual generation. This would enable Meta to power a wide range of applications across its platforms, including smarter content understanding, AI assistance, improved ranking, and automated creative tooling.

III. Google’s “Paper Banana” – Automating Scientific Visualization

Researchers from Google and Peking University have introduced “Paper Banana,” a framework designed to automate the creation of scientific figures for research papers. The core problem addressed is the significant time researchers spend on visual presentation – diagram layout, alignment, and ensuring aesthetic quality.

Paper Banana employs a multi-agent system with five specialized agents:

1. Retriever: Searches a database for 10 reference examples of similar figures from published papers.
2. Planner: Translates methodology text into a detailed description of the figure’s content.
3. Stylist: Matches the visual style (color palettes, layout) to established academic publishing standards (NURIPS style).
4. Visualizer: Generates the visual output – using image models (Nano Banana Pro) for diagrams and Python matplotlib code for statistical plots.
5. Critic: Inspects the output for factual errors and visual glitches, providing feedback for iterative refinement.

The workflow consists of two phases: linear planning (retrieval, planning, styling) and iterative refinement (visualization, criticism, repeated for three rounds). The use of matplotlib for charts ensures numerical precision, avoiding the potential for hallucinations common in image generation models.

Benchmark Results: Evaluated on the “Paper Banana Bench” dataset (292 test cases from NURIPS 2025 publications), Paper Banana achieved:

• Overall Score Improvement: 17.0%
• Conciseness Improvement: 37.2%
• Readability Improvement: 12.9%
• Aesthetics Improvement: 6.6%
• Faithfulness Improvement: 2.8%

The largest gains were in conciseness, indicating improved clarity and faster communication. The system also demonstrates a preference for specific aesthetic guidelines based on the figure type (e.g., illustrative visuals for agent diagrams, geometric density for computer vision).

IV. Gemini’s UI/SVG Generation Capabilities – A New Checkpoint in Testing

A new checkpoint of Google’s Gemini 3 Pro model is currently undergoing AB testing within Google’s AI Studio. Early testers report exceptionally strong performance in generating user interfaces (UIs) and Scalable Vector Graphics (SVGs). SVG is particularly challenging due to its demand for precision; errors can render the output unusable.

Testers are reporting the checkpoint produces complex interfaces and SVG structures with high accuracy. This capability is being tested alongside other checkpoints, and Google has not confirmed a public release. The report notes that previous Gemini checkpoints have been tested without resulting in public launches. The checkpoint is also being benchmarked on LM Arena, indicating publicish comparison.

Google’s focus on UI/SVG generation aligns with its strategy of positioning Gemini as a core LLM for creative and productivity workflows. A model capable of generating clean interface code or SVG assets can significantly accelerate prototyping, mockups, and design system development.

Conclusion:

The developments highlighted – Meta’s Avocado, Google’s Paper Banana, and the new Gemini checkpoint – demonstrate rapid advancements in AI capabilities. Avocado promises a significant leap in efficiency, potentially reshaping the economics of large language models. Paper Banana addresses a critical bottleneck in scientific research, automating the creation of high-quality visuals. And Gemini’s UI/SVG generation capabilities point towards a future where AI plays a more prominent role in design and development workflows. These advancements, largely occurring “behind the scenes,” signal a period of intense innovation and competition in the AI landscape.