GPT-5.5 VS Deepseek V4 Pro VS Opus 4.7: I tested THEM on My KingBench 2.0 Questions!

Key Concepts

• Kingbench 2.0: A refreshed benchmarking framework designed to evaluate LLMs on coding tasks, ranging from simple queries to complex agentic workflows.
• Mixture of Experts (MoE): An architecture where only a subset of parameters is activated per inference pass, increasing efficiency.
• Muon Optimizer: A specialized optimization algorithm used for training large-scale models, noted for its scalability.
• Token Consumption: The efficiency of a model in processing input/output; lower consumption is critical for cost-effectiveness.
• Agentic Behavior: The ability of an AI model to perform multi-step tasks or interact with external tools/environments autonomously.

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1. Model Specifications and Architectures

The video evaluates three primary models: DeepSeek V4 (Pro/Flash), GPT 5.5, and Opus 4.7.

• DeepSeek V4 Pro: A 1.6 trillion parameter MoE model with 49 billion active parameters per pass.
• DeepSeek V4 Flash: A 284 billion parameter model with 13 billion active parameters per pass.
• Technical Note: Both DeepSeek models utilize the Muon optimizer, which the presenter highlights as a significant choice for scalability, challenging the notion that Muon is only suitable for smaller models.
• GPT 5.5: An anticipated release aimed at improving front-end task performance and token efficiency.
• Opus 4.7: Positioned as the current gold standard for overall performance and reliability in coding tasks.

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2. Benchmarking Methodology (Kingbench 2.0)

The presenter utilizes Kingbench 2.0 to test models on real-world coding and simulation tasks. The benchmark is designed to be "model-agnostic" and tests both standard coding capabilities and agentic workflows.

• Testing Environment: Currently managed via Excel sheets while a dedicated UI is under development.
• Evaluation Criteria: Models are judged on their ability to handle complex front-end/back-end integration, 3D rendering logic, and game simulation.

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3. Performance Analysis: Case Studies

The models were subjected to several specific coding challenges:

• Elevator Simulator: Required spawning people and managing elevator logic.
  • DeepSeek V4 Pro: Failed; positioning was random and incorrect.
  • GPT 5.5: Functional but suffered from significant UI flickering and poor design.
  • Opus 4.7: Successfully executed the task with professional-grade results.
• 3D Contact Lens Case: A task involving 3D logic and interactive UI.
  • DeepSeek V4: Produced a "brick with two holes."
  • GPT 5.5 & Opus 4.7: Both struggled with orientation (flipping L/R) and cap-opening logic, though Opus was closer to the desired output.
• 3D Folding Table:
  • DeepSeek V4: Performed surprisingly well, meeting basic requirements.
  • GPT 5.5: Failed; partitions overlapped when folded.
• Bow and Arrow Simulator:
  • DeepSeek V4: Highly buggy/non-functional.
  • GPT 5.5: Good functionality, though hampered by "dirty card" UI designs.
  • Opus 4.7: Professional, high-quality output; identified as the best performer.

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4. Economic and Practical Considerations

• Pricing: DeepSeek V4 is noted for being extremely cost-effective. The Pro version costs ~$1.74 (input) and ~$3.78 (output) per million tokens. The Flash version is significantly cheaper at ~$0.04 and ~$0.28 per million tokens.
• The "Token Trap": The presenter argues that while DeepSeek is cheap, its high token consumption may lead to higher long-term costs for API users.
• GPT 5.5 Criticism: The presenter suggests that for GPT 5.5 to justify its price point relative to DeepSeek, it would need to be a 16-trillion parameter model, which it currently does not appear to be.
• Accessibility: All models are integrated into platforms like Kilo CLI, OpenRouter, and Kilo Gateway, making them widely accessible for developers.

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5. Synthesis and Conclusion

The presenter concludes that Opus 4.7 remains the superior model for most users due to its consistency and high-quality output, despite the frustrations regarding usage limits in the "Claude Code" plan.

• DeepSeek V4: Deemed "not good" and difficult to recommend, as it failed to meet expectations in complex coding tasks.
• GPT 5.5: Viewed as a mixed bag—good at specific tasks but lacking the overall polish and reliability of Opus.
• Final Verdict: While the industry is moving toward cheaper, more efficient models (like DeepSeek), the current generation of models still struggles with advanced agentic tasks and complex 3D logic, with Opus 4.7 currently holding the lead in professional-grade coding applications.