Gemini 3 Pro Early Checkpoint Review – Unprecedented Performance and Multimodal Capabilities

Introduction

Google’s AI Studio has quietly released an early checkpoint of Gemini 3 Pro, sparking excitement among developers and AI enthusiasts. Accessible through an A/B test that occasionally swaps the default Gemini 2.5 Pro model for the newer version, this checkpoint (identified by a network‑log ID beginning with 2HT) appears roughly once every fifty prompts. After a painstaking series of tests, the results demonstrate a significant leap in both generation quality and multimodal functionality. This article summarizes the testing process, highlights the most striking outputs, and places Gemini 3 Pro in the context of current leading models.

Accessing the Gemini 3 Pro Checkpoint

• Open Google AI Studio and select Gemini 2.5 Pro as the model.
• Send a prompt; when an A/B test is triggered, the backend may serve Gemini 3.0 Flash or Gemini 3.0 Pro.
• Verify the model by inspecting network logs for a checkpoint ID that starts with 2HT.
• Because the Pro checkpoint appears infrequently, multiple attempts are required to capture it for testing.

Testing Methodology

The author evaluated the model with a curated set of 13 general‑purpose prompts covering layout generation, graphic creation, interactive simulations, code generation, and reasoning tasks. Each prompt was run in a single‑shot mode to mimic typical user interactions. Performance metrics such as visual fidelity, logical consistency, and response latency were recorded, and token usage was estimated to gauge pricing relative to existing Google models.

Key Findings

1. Architectural Floor‑Plan Generation

The model produced a remarkably coherent floor plan:

• Correct placement of entry, living room, kitchen, and dining area.
• Accurate door locations and spatial relationships.
• Minor flaw: the washroom was positioned at the front, requiring passage through it to reach other rooms.

Overall, this is the most sensible architectural generation observed from any AI model to date.

2. SVG Panda with Burger

A whimsical SVG illustration featured a panda interacting naturally with a detailed burger. The rendering captured fine‑grained details and maintained proper perspective, showcasing the model’s vector‑graphic capabilities.

3. Pokéball Rendered with Three.js

The generated Three.js code produced a high‑quality Pokéball with realistic lighting. The scene demonstrated:

• Accurate material shaders.
• Proper illumination and shadows.
• Seamless integration of WebGL elements.

4. Autoplay Chess Game

Gemini 3 Pro delivered a fully functional chess interface without the typical purple‑blue color scheme common in earlier models. Notable improvements include:

• A clean, modern aesthetic.
• Automatic piece removal and repositioning after captures.
• Smooth animation and responsive UI.

5. Minecraft‑Style Scene in Kandinsky Aesthetic

A prompt for a Minecraft‑like environment rendered in a Kandinsky‑style yielded:

• Detailed trees and terrain.
• Consistent visual style across blocks.
• High frame‑rate performance, indicating efficient rendering pipelines.

6. Butterfly Garden Simulation

The simulation produced a pleasant visual of butterflies fluttering in a garden. While competent, it fell short of the top‑tier output seen from GPT‑5, suggesting room for refinement in dynamic particle effects.

7. CLI Tool for Image Conversion

The generated command‑line interface correctly handled image format conversion, though the solution was solid rather than groundbreaking.

8. Blender Script for a Pokéball

The model authored a comprehensive Blender script that:

• Modeled the Pokéball geometry.
• Configured lighting and camera angles.
• Produced realistic reflections and shading, surpassing the quality of previous Google models and rivaling the Opus benchmark.

9. Reasoning and Riddle Solving

Gemini 3 Pro excelled in a series of AIM questions and a simple riddle:

• Answered each query correctly on the first attempt, a task that typically requires multiple tries from GPT‑4 or GPT‑5.
• Demonstrated superior logical reasoning, outperforming Sonnet 4.5 by approximately 25 % on the author’s internal leaderboard.

Performance, Pricing, and Token Consumption

• Token counts suggest a cost structure comparable to Google’s Sonnet tier.
• The model exhibits a noticeable latency before emitting the first token, hinting at an internal “thinking” phase despite the absence of explicit chain‑of‑thought traces.
• Given the quality‑to‑price ratio, Gemini 3 Pro would likely be positioned as a premium offering, potentially matching Sonnet’s price point.

Comparison with Competing Models

Feature	Gemini 3 Pro	Sonnet 4.5	GPT‑5 (Zenith)
Architectural Layout	Highly coherent (minor washroom issue)	Moderate	Not available
Multimodal Rendering (SVG, 3D)	Excellent, detailed lighting	Good	Competitive
Interactive Simulations	Chess UI, Minecraft scene, smooth FPS	Basic	Advanced
Reasoning Accuracy	Near‑perfect on test set	75 % of Gemini 3 Pro	Comparable
Latency (first token)	Slight delay (thinking)	Faster	Variable

Overall, Gemini 3 Pro presents a clear upgrade over Sonnet 4.5 and rivals the performance of the unreleased GPT‑5 Zenith checkpoint, which has not yet been made publicly available.

Implications for the Gemini 3 Ecosystem

The early checkpoint indicates that Google is close to launching a Gemini 3 Pro tier that will power a range of products:

• Gemini CLI enhancements for developers.
• Updated Jules AI assistant capabilities.
• More sophisticated AI Studio app generators.

If the model is released as a multimodal offering, it could dramatically improve the utility of Google’s AI suite, positioning it ahead of competitors such as Anthropic and OpenAI in both breadth and depth of functionality.

Conclusion

The Gemini 3 Pro checkpoint, though accessed only through a rare A/B test, showcases a significant leap in generative quality, multimodal versatility, and reasoning power. Its performance on architectural design, 3‑D rendering, interactive simulations, and logical tasks places it at the forefront of current AI models. Assuming a pricing structure similar to Sonnet, Gemini 3 Pro offers an exceptional price‑to‑performance ratio that could redefine Google’s AI product lineup. The AI community eagerly awaits an official release, which promises to raise the bar for both research and commercial applications.