Deepseek v4: Best Opensource Model Ever? (Fully Tested)

Key Concepts

• DeepSeek V4 (Preview): The latest iteration of the DeepSeek model series, featuring two variants: Pro and Flash.
• Parameter Architecture: The Pro model utilizes 1.6 trillion total parameters with 49 billion active parameters; the Flash model uses 284 billion total parameters with 13 billion active parameters.
• Context Window: Both models support a 1 million token context length.
• Agentic Workflows: The ability of an AI model to perform complex, multi-step tasks (e.g., coding, UI generation, simulation).
• Benchmark Optimization: The practice of tuning models specifically to score high on standardized tests, which may not always correlate with real-world performance.
• Open Weights: Models released under the MIT license, allowing for public access and modification.

---

1. Overview of DeepSeek V4 Models

The DeepSeek team has released a preview of their V4 models, positioning them as high-performance, cost-effective alternatives to closed-source models.

• DeepSeek V4 Pro: The flagship model designed for high-level reasoning, STEM, and coding. It claims to rival top-tier closed-source models like Claude Opus 4.6 and Gemini 3.1 Pro.
• DeepSeek V4 Flash: A lightweight, high-speed version optimized for efficiency and simpler agentic tasks.

2. Technical Specifications and Pricing

The models are marketed heavily on their cost-efficiency and architectural design:

• Pro Pricing: $0.14 per 1 million input tokens; $0.48 per 1 million output tokens.
• Flash Pricing: $0.03 per 1 million input tokens; $0.28 per 1 million output tokens.
• Accessibility: Available via HuggingFace (open weights), Ollama, and the official DeepSeek chatbot.

3. Performance Analysis and Real-World Testing

The reviewer conducted several comparative tests against competitors like Qwen 3.6 Plus, Kimi K 2.6, MiniMax N2.7, and Claude Opus 4.7. The findings suggest a significant gap between the model's marketing claims and its actual output:

• Coding and UI Generation: In tests involving Mac OS clones, Slack clones, and SaaS landing pages, DeepSeek V4 Pro struggled with basic structure, lack of creativity, and failure to compile code.
• 3D Modeling/Simulation: When tasked with creating a 3D PS5 controller or a Minecraft clone, the model produced "sloppy" and "lazy" results compared to the Qwen or MiniMax models, which provided more refined textures and functional mechanics.
• Benchmark vs. Reality: The reviewer argues that DeepSeek V4 is "benchmark maxed," meaning it performs well on static tests but fails in complex, real-world agentic workflows. This is supported by its current third-place ranking in the Code Arena, trailing behind GLM 5.1 and Kimi K 2.6.

4. Key Arguments and Perspectives

• The "Mid" Verdict: The reviewer characterizes the model as "mid" (mediocre), noting that while it is impressive on paper regarding efficiency and cost, it lacks the polish and reliability required for professional-grade tasks.
• Cost vs. Quality: A central argument is that "cheaper doesn't make it better." While the pricing is highly competitive, the output quality is described as subpar compared to industry leaders.
• Expectation Management: The reviewer acknowledges that this is a "preview" release, suggesting that future iterations may address the current lack of creativity and execution errors.

5. Notable Quotes

• "The model isn't even ranked number one in the code category. It's actually sitting number three in Code Arena behind GLM 5.1 and even the Kim K 2.6."
• "At the end of the day, cheaper doesn't make it better. It just means it's cheaper."

---

Synthesis and Conclusion

DeepSeek V4 represents a significant effort in the open-source space, particularly regarding cost-efficiency and context length. However, based on the provided testing, the model currently fails to live up to its claims of outperforming top-tier proprietary models like Claude Opus. While it serves as a functional base for future development, its current real-world performance in coding, UI design, and complex simulation is inconsistent and lacks the refinement seen in competitors like Qwen or MiniMax. The model is best viewed as a work-in-progress that prioritizes economic accessibility over high-fidelity output.