Google Gemini 3.0 Checkpoint X28 Tested – Performance Review and New Features

Introduction

Google has recently introduced a new checkpoint for its upcoming Gemini 3.0 Pro model, labeled X28, in AI Studio. The checkpoint arrives just weeks after the earlier X58 checkpoint was withdrawn, sparking speculation that the full Gemini 3.0 model may be released soon. This article examines the X28 checkpoint’s capabilities across a range of visual and coding tasks, compares its performance to previous Gemini checkpoints and competing models such as Sonnet, and outlines the implications for developers and AI enthusiasts.

Accessing the New Checkpoint

AI Studio restricts certain checkpoints to specific geographic regions, which can impede testing. Users often rely on VPN services to bypass these limitations. While this article does not promote any particular VPN, it is worth noting that unrestricted access is essential for comprehensive evaluation of emerging AI models.

Visual Generation Tests

The X28 checkpoint was evaluated using a set of eleven benchmark prompts that cover architecture, vector graphics, 3‑D scenes, and UI generation. Below is a summary of the results.

1. Architectural Floor Plan

• Coherence: Walls, doors, and furniture are placed logically, producing a layout that reads naturally.
• Lighting Controls: The model correctly adjusts shadows for different times of day.
• Interactivity: Users can now drag furniture within the generated scene, a notable improvement over the previous checkpoint.
• Consistency: Repeated prompts yield highly similar outputs, reducing variance compared with Sonnet’s more divergent responses.

2. SVG Panda Illustration

• The panda is depicted eating a burger rather than merely holding it, demonstrating better adherence to the prompt.
• Vector details and color palettes are more cohesive, resulting in a cleaner illustration.

3. Pokéball Rendered in 3.js

• The checkpoint delivers a polished 3‑D Pokéball with a vibrant background.
• Color blending and shading are noticeably refined compared to earlier outputs.

4. Minecraft‑Style Landscape

• Generates a recognizable terrain with rivers, realistic lighting, and appropriate block textures.
• The scene is suitable for one‑shot generation, showing the model’s ability to create complete environments quickly.

5. Majestic Butterfly in a Garden

• The butterfly animation is fluid, and the surrounding flora is detailed.
• Minor clipping issues appear occasionally, but overall the visual quality is among the best observed for a single prompt.

6. CLI Tool Script in Rust

• Produces functional Rust code for a command‑line interface, adhering to best practices and compiling without errors.

7. Blender Script for a Pokéball

• Generates a Blender‑compatible script that recreates the Pokéball geometry and materials with high fidelity.

Quantitative Performance Gains

Based on the visual and code generation tests, the X28 checkpoint appears to deliver 5‑10 % improvement over the previous X58 checkpoint. The gains are most evident in:

• Prompt fidelity: The model follows instructions more precisely.
• Output consistency: Reduced randomness leads to predictable results, which is valuable for production pipelines.
• Aesthetic quality: Color harmony and lighting are more realistic across generated assets.

These enhancements position Gemini 3.0 as a serious contender against current market leaders, potentially reviving the performance level reminiscent of the Sonnet 3.5 era.

Tool‑Calling Capabilities

The X28 checkpoint also supports tool calling, allowing the model to invoke external utilities during a session. In a test using a human‑relay mode, the model correctly triggered a tool on the first request, demonstrating reliable integration. While the current implementation is limited to simple calls, future extensions—such as incorporation into a Gemini‑CLI—could make this feature a powerful asset for developers.

Anticipated Release Timeline and Pricing

Industry chatter suggests that the full Gemini 3.0 model may roll out in the next two weeks, possibly around October 20. Pricing expectations remain speculative, but the community hopes it will be comparable to or lower than Sonnet, which would make the model accessible for a broader audience and encourage adoption in cost‑sensitive projects.

Conclusion

Google’s X28 checkpoint offers a compelling glimpse into the capabilities of the upcoming Gemini 3.0 Pro. Across architectural layouts, vector graphics, 3‑D scenes, code generation, and tool calling, the model demonstrates measurable improvements in fidelity, consistency, and visual appeal. If the projected release schedule holds and pricing is competitive, Gemini 3.0 could re‑establish Google’s AI offerings as a top tier option for developers seeking high‑quality, multi‑modal generation.

The evaluation presented here reflects a single‑shot testing methodology; real‑world performance may vary based on prompt complexity and integration context.