Deepseek V4 (Fully Tested): I JUST GOT ACCESS & TESTED DEEPSEEK-V4!!

Key Concepts

• DeepSeek V4: The latest iteration of the DeepSeek model, currently in a rollout phase.
• Multimodal Capabilities: The ability of the model to process and generate content based on both text and image inputs.
• Instant vs. Expert Modes: A dual-interface system where "Expert" mode provides access to the most powerful model version, while "Instant" mode handles faster, lighter tasks (including image processing).
• Three.js: A cross-browser JavaScript library used to create and display animated 3D computer graphics in a web browser.
• One-Shot Prompting: A technique where the model is expected to provide a complete, functional output (e.g., a single HTML file) based on a single instruction.

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Overview of DeepSeek V4 Interface and Functionality

The video details the initial testing of the newly released DeepSeek interface, widely believed to be DeepSeek V4. The platform introduces a bifurcated user experience:

• Expert Mode: Designed for high-complexity tasks. It is restricted to single-thread processing, meaning users cannot run multiple prompts simultaneously.
• Instant Mode: Optimized for speed and multimodal tasks. The system automatically switches to this mode when an image is uploaded.

Performance Testing and Case Studies

The author conducted several tests to evaluate the model's coding and generative capabilities, specifically requesting single-file HTML outputs containing HTML, CSS, and JavaScript.

1. Architectural Design (Floor Plan):
   • Task: Create a 3D floor plan for a 1585 sq. ft. area with two rooms and two washrooms using Three.js.
   • Result: Highly successful. The model generated a functional, well-laid-out 3D representation that accurately reflected the spatial requirements.
2. SVG Generation (Panda with Burger):
   • Task: Create an SVG graphic.
   • Result: Poor performance. The model struggled with anatomical proportions, resulting in a low-quality visual output.
3. 3D Modeling (Pokéball):
   • Task: Create a 3D Pokéball using Three.js.
   • Result: Successful. The model demonstrated a strong aesthetic preference for dark blue color palettes, similar to the stylistic tendencies observed in GPT-4.
4. Interactive Game Development (Chessboard):
   • Task: Create a functional chessboard with an "autoplay" feature for legal moves.
   • Result: Mixed. While the visual rendering of the board and pieces was high-quality, the functional controls for the game logic were non-responsive.
5. 3D Animation (Butterfly in a Garden):
   • Task: Create a 3D butterfly with camera movement controls.
   • Result: Failure. The model produced a visual that did not resemble a butterfly, with the author noting it looked more like a "Gardevoir" (a Pokémon character).

Logical Analysis and Limitations

• Reasoning Capabilities: The model showed significant limitations in solving simple logic problems, often getting "stuck midway" through the reasoning process.
• Comparison to R1: The author posits that while DeepSeek V4 is a capable tool, it may not represent the same level of breakthrough or "size" as the previously released DeepSeek R1 model.
• Technical Constraints: The current iteration appears to have stability issues, particularly regarding complex logic and interactive code execution.

Synthesis and Conclusion

The initial assessment of DeepSeek V4 suggests a model that excels in specific 3D web-based coding tasks (using Three.js) but lacks consistency in visual generation (SVG) and complex logical reasoning. The interface's "Expert" mode provides a robust environment for coding, though it is currently hampered by single-thread limitations and occasional failures in functional interactivity. The author concludes that while the model is "pretty cool," it requires further refinement—likely through an API release—before it can be fully ranked against industry-leading models.