Multiverse Computing vs DeepSeek
Side-by-side comparison of AI visibility scores, market position, and capabilities
Quantum and quantum-inspired software company solving financial and industrial optimization problems; San Sebastian Spain-based;
About
Multiverse Computing is a San Sebastian, Spain-based quantum software company that develops algorithms for solving complex optimization and machine learning problems in finance, energy, and manufacturing using quantum and quantum-inspired computing techniques. The company's CompactifAI platform uses tensor network methods — mathematical approaches borrowed from quantum physics — to compress and accelerate large AI models, enabling LLM inference and training on less powerful hardware. Multiverse's finance applications include portfolio optimization, fraud detection, and risk assessment using quantum algorithms designed for NISQ (Noisy Intermediate-Scale Quantum) hardware. The company serves financial institutions, energy companies, and industrial manufacturers that have identified specific optimization problems where quantum approaches provide advantage. Founded in 2019, Multiverse raised over $27M from investors including Columbus Venture Partners, Quantonation, and the European Innovation Council. It competes with 1QBit, QC Ware, and Zapata Computing in the quantum software and applications market.
DeepSeek-V3 and R1 models shocked the AI industry with top-tier performance at <1% of OpenAI training costs. 96.88M MAU; open-weights model downloaded 5M+ times. Owned by High-Flyer (Chinese quant fund); demonstrated efficient AI without massive GPU clusters.
About
DeepSeek is a Chinese AI research company and LLM platform founded in 2023 as a subsidiary of High-Flyer, a quantitative hedge fund. The company made global headlines in early 2025 when it released DeepSeek-V3 and DeepSeek-R1, large language models that achieved top-tier performance on reasoning and coding benchmarks at a fraction of the training cost of comparable Western models. DeepSeek's engineering innovations—including mixture-of-experts architectures, multi-head latent attention, and efficient RLHF pipelines—demonstrated that frontier AI capability could be achieved with far less compute than previously assumed.\n\nDeepSeek offers its models through an API platform competitive with OpenAI and Anthropic, as well as releasing open-weights versions that can be downloaded and self-hosted. Its R1 reasoning model became especially popular for STEM tasks, coding, and mathematical problem solving. The open-weights strategy has made DeepSeek models a foundational choice for researchers, enterprises running private deployments, and developers seeking cost-efficient inference. DeepSeek's pricing is dramatically below Western API competitors, accelerating adoption globally.\n\nDeepSeek-R1's open-weights release was downloaded over 100 million times and triggered significant recalibration across the AI industry about training efficiency and the cost of frontier capabilities. The platform now serves 96.88 million monthly active users, rivaling major Western AI products in scale. DeepSeek's emergence reshaped the competitive landscape in 2025-2026, forcing cost reductions from OpenAI, Google, and Anthropic, and raising important questions about AI export controls and the global race for AI supremacy.
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