Past battle · 2025-08-08 UTC
LLM Showdown — August 8, 2025
From the LLM category. 28 marks placed across 6 fighters. DeepSeek V3 took the crown.
Final standings
The line-up
The fighters
Profiles of every tool that competed in this battle, ranked by their final score.
DeepSeek V3
Open-source mixture-of-experts model offering GPT-4o-level reasoning at a fraction of the cost.
DeepSeek V3 is a large-scale mixture-of-experts (MoE) language model developed by DeepSeek AI. It activates only a subset of its total parameters per token, allowing it to deliver strong performance in reasoning, mathematics, and coding tasks while keeping inference costs significantly lower than comparable dense models. Released with open weights, DeepSeek V3 has become a popular choice for developers and researchers who need a capable foundation model they can self-host, fine-tune, or integrate via API. Benchmarks place it competitively against leading proprietary models like GPT-4o, particularly on math and logical reasoning evaluations. The model is well suited for technical assistants, code generation pipelines, research workflows, and any application where reasoning quality and budget efficiency both matter.
Criteria breakdown
- Mixture-of-experts architecture
- Competitive reasoning and math benchmarks
- Open-source model weights
- API access via DeepSeek platform
- Long context window support
- Fine-tuning friendly
Janus pro
DeepSeek's open multimodal model for image generation and visual understanding in one unified architecture.
Janus Pro is an open-source multimodal AI model from DeepSeek, available in 1B and 7B parameter versions. It unifies visual understanding and image generation in a single framework by decoupling the visual encoding pathways, allowing the model to both interpret images and create them from text prompts. The 7B variant delivers competitive results on benchmarks for text-to-image synthesis and visual question answering, often matching or surpassing larger specialized models. Released under an MIT license, Janus Pro can be self-hosted, fine-tuned, and integrated into research or production pipelines without usage restrictions. It suits developers, researchers, and hobbyists who need a flexible multimodal foundation model for experimentation, prototyping, or building applications that combine image creation with image comprehension.
Criteria breakdown
- Text-to-image generation
- Visual question answering and image analysis
- Unified transformer architecture
- 1B and 7B parameter options
- MIT-licensed open weights
- Multimodal input and output support
DeepSeek R1
An open-source large language model excelling in reasoning, math, and coding tasks with MIT licensing for free use and modification.
DeepSeek R1 is an open-source large language model that excels in reasoning, math, and coding tasks. It utilizes a Mixture of Experts (MoE) architecture with 37B active parameters and 671B total parameters, supporting 128K context length. The model incorporates advanced reinforcement learning techniques to achieve self-verification, multi-step reflection, and human-aligned reasoning capabilities. DeepSeek R1 has achieved state-of-the-art performance in various benchmarks, including 97.3% accuracy on MATH-500, 79.8% pass rate on AIME 2024, and outperforming 96.3% of Codeforces participants. The model is available in multiple variants, ranging from 1.5B to 70B parameters, and is licensed under MIT for free use and modification. DeepSeek R1 can be used online for free, and a WebGPU-accelerated version can run locally in a browser. The model is designed for complex problem-solving, multilingual understanding, and production-grade code generation. Its strengths include exceptional mathematical reasoning, code generation, and natural language understanding capabilities. However, as an open-source model, it may require technical expertise to deploy and fine-tune for specific use cases. DeepSeek R1 is positioned among the top-performing AI models globally, with capabilities comparable to leading proprietary solutions. Its open-source nature allows for community-driven development and continuous upgrades, including planned multimodal support, conversational enhancement, and distributed inference optimization. The model has a pure reinforcement learning development, which allows it to achieve GPT-4-level math performance at a significantly lower cost. The chain-of-thought visualization capability addresses AI "black box" challenges, providing insights into the model's reasoning process. DeepSeek R1's API offers an OpenAI-compatible endpoint for integration, priced at $0.14 per million tokens. The model's weights are open-source, allowing for commercial use and modification.
Criteria breakdown
- Mixture of Experts (MoE) architecture
- Advanced reinforcement learning techniques
- Self-verification and multi-step reflection capabilities
- Human-aligned reasoning
- Support for 128K context length
- OpenAI-compatible API endpoint
ASI:One
Agentic AI assistant that coordinates autonomous agents to complete multi-step tasks.
ASI:One is an AI assistant built around the idea of agentic intelligence, where a conversational interface can dispatch and coordinate specialized autonomous agents to handle complex requests. Instead of returning only text, it can plan, call tools, and execute workflows on a user's behalf. Developed within the Artificial Superintelligence Alliance ecosystem, it is positioned as a personal AI agent that integrates with decentralized agent networks. Users can chat with it for everyday questions or delegate longer tasks such as research, comparisons, scheduling, and data lookups across connected services.
Criteria breakdown
- Conversational chat interface
- Autonomous agent orchestration
- Multi-step task planning and execution
- Connectivity to external agents and services
- Personal assistant style memory and context
- Built on the ASI Alliance agent stack
Latest DeepSeek R2 is the successor to DeepSeek's R1 reasoning model, designed to deliver stronger step-by-step problem solving across math, coding, and analytical tasks. It aims to extend the open research approach that made earlier DeepSeek releases popular with developers and researchers. The model targets improved accuracy, longer context handling, and more efficient inference compared to its predecessor, making it suitable for technical assistants, agentic workflows, and integration into custom applications. Availability and exact specifications depend on DeepSeek's official release channels. Users can typically access the model via API, chat interface, or by running open weights where provided, allowing flexibility for both individual experimentation and production deployment.
Criteria breakdown
- Advanced chain-of-thought reasoning
- Extended context window
- Code generation and debugging support
- Multilingual understanding
- API and chat-based access
- Suitable for agentic applications
Pronoia
Arabic-first large language model fine-tuned for native-quality understanding and generation.
Pronoia is a large language model specifically fine-tuned for Arabic, aiming to deliver more accurate comprehension, generation, and reasoning in the language than general-purpose multilingual models. It is positioned as a leading Arabic-focused LLM, with optimizations for dialects, classical forms, and modern standard Arabic. The model can be used for tasks such as content creation, summarization, translation, customer support, and conversational AI in Arabic-speaking markets. By concentrating its training on Arabic data, Pronoia targets nuances like morphology, diacritics, and cultural context that broader models often handle inconsistently.
Criteria breakdown
- Arabic-optimized language model
- Text generation and summarization
- Translation support
- Conversational and chatbot capabilities
- Suited for enterprise Arabic NLP
- Coverage of MSA and dialects