Past battle · 2024-01-17 UTC
LLM Showdown — January 17, 2024
From the LLM category. 37 marks placed across 8 fighters. ASI:One took the crown.
Final standings
The line-up
The fighters
Profiles of every tool that competed in this battle, ranked by their final score.
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
Gemini 2.0 Flash
Google's fast, multimodal AI model built for real-time agentic tasks with a 1M-token context window.
Gemini 2.0 Flash is Google DeepMind's next-generation model optimized for speed, scale, and multimodal reasoning. It accepts text, images, audio, and video as input and can generate text, images, and audio output, making it suitable for rich interactive applications. Designed with agentic workflows in mind, the model supports native tool use, function calling, and a 1M-token context window for handling large documents, codebases, or long-running sessions. Low latency makes it a practical choice for assistants, real-time analysis, and production-scale deployments. Developers can access Gemini 2.0 Flash through the Gemini API, Google AI Studio, and Vertex AI, with SDKs available across major languages.
Criteria breakdown
- 1M-token context window
- Multimodal input: text, image, audio, video
- Native tool calling and code execution
- Real-time streaming responses
- Image and audio generation
- Available via Gemini API and Vertex AI
Mistral Small 3
Compact open-source LLM delivering competitive performance with lower compute demands.
Mistral Small 3 is a lightweight large language model from Mistral AI, designed to offer strong reasoning and generation capabilities while running efficiently on modest hardware. It targets developers and organizations that want capable AI without the infrastructure costs of frontier-scale models. Released under an open license, the model can be self-hosted, fine-tuned, and integrated into commercial applications. Its balance of speed, accuracy, and resource efficiency makes it suitable for chat assistants, content generation, code tasks, and on-premise deployments where latency or privacy matters.
Criteria breakdown
- Open-weight model release
- Optimized for efficient inference
- Competitive benchmark results
- Supports fine-tuning and customization
- Suitable for on-premise deployment
- Multilingual text generation
OpenAI o3 is a frontier reasoning model designed to tackle problems that require careful, step-by-step thinking. It builds on the o-series approach of spending more compute at inference time to plan, verify, and refine answers, making it well-suited for tasks in mathematics, coding, science, and logical analysis. Compared to general-purpose chat models, o3 emphasizes depth over speed. It can break down ambiguous prompts, evaluate multiple approaches, and produce more reliable outputs on benchmarks that stress reasoning and tool use. Developers can access it through the OpenAI API and ChatGPT, where it integrates with tools like web browsing, Python, and file analysis. The model is aimed at researchers, engineers, and power users who need stronger accuracy on hard problems and are willing to trade some latency and cost for higher-quality results.
Criteria breakdown
- Extended chain-of-thought reasoning
- Tool use including code, web, and file inputs
- Large context window for long documents
- API and ChatGPT availability
- Improved accuracy on STEM benchmarks
- Supports complex agentic workflows
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
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
Llama 3.3
Meta's multilingual open-weight LLM tuned for efficient, high-quality text generation.
Llama 3.3 is a large language model from Meta designed to deliver strong reasoning, coding, and multilingual capabilities while being more efficient to run than earlier flagship models. It supports a wide range of languages and is suitable for chat assistants, content generation, summarization, and developer tooling. Released with open weights, it can be deployed on-premises or through major cloud and inference providers, giving teams flexibility over cost, latency, and data handling. Its instruction-tuned variant is optimized for following prompts accurately and producing helpful, conversational responses. Developers commonly use Llama 3.3 as a base for fine-tuning domain-specific applications, retrieval-augmented generation systems, and agentic workflows.
Criteria breakdown
- Multilingual text generation
- Instruction-tuned chat variant
- Long-context support
- Coding and reasoning capabilities
- Open weights for fine-tuning
- Compatible with major inference frameworks
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