Past battle · 2026-01-29 UTC
LLM Showdown — January 29, 2026
From the LLM category. 26 marks placed across 8 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
Mistral Large 24.11
Mistral's flagship LLM for multilingual reasoning, coding, and enterprise-grade tasks.
Mistral Large 24.11 is the November 2024 release of Mistral AI's top-tier large language model. It targets advanced reasoning, code generation, mathematical problem-solving, and complex instruction following across multiple languages, making it suitable for both research and production use. The model is positioned for enterprise workflows, with strong support for function calling, structured outputs, and long-context tasks. It is available through Mistral's API and via partner cloud platforms, giving teams flexibility in how they deploy and integrate it. Developers commonly use Mistral Large 24.11 to power coding assistants, multilingual chatbots, document analysis pipelines, and retrieval-augmented applications where reliable reasoning across languages is important.
Criteria breakdown
- Advanced reasoning and instruction following
- Native multilingual support
- Code generation and debugging
- Function calling and JSON outputs
- Long-context handling
- Enterprise deployment options
Simple MP3 to Text
AI-powered MP3 to text converter for turning audio into clean, readable transcripts.
Simple MP3 to Text is a transcription tool that converts audio files such as lectures, podcasts, interviews, and voice notes into written text. It uses AI speech recognition to produce readable transcripts without requiring manual typing or specialized hardware. The service is designed for users who want a quick, low-friction way to extract text from spoken content. Uploaded MP3s are processed automatically, and the resulting text can be copied, edited, or saved for use in notes, articles, study materials, or content repurposing. It suits students, journalists, podcasters, researchers, and anyone who regularly works with recorded audio and prefers a straightforward workflow over more complex transcription platforms.
Criteria breakdown
- MP3 audio to text conversion
- AI-powered speech recognition
- Supports lectures, podcasts, and voice notes
- Clean, copy-ready transcripts
- Browser-based workflow
- Quick turnaround on uploads
Gemma 4 Local Hardware Matcher
Find the right Gemma 4 model variant for your local hardware setup.
Gemma 4 Local Hardware Matcher is a utility that helps users identify which versions of Google's Gemma 4 model family can run effectively on their specific hardware. By analyzing factors like GPU VRAM, system RAM, CPU capabilities, and available storage, it recommends compatible model sizes and quantization levels. The tool is aimed at developers, hobbyists, and researchers who want to run Gemma 4 locally without trial-and-error testing. It removes guesswork around memory requirements and performance expectations, helping users pick a model variant that balances quality and speed for their machine.
Criteria breakdown
- Hardware detection and analysis
- Model size and quantization recommendations
- VRAM and RAM requirement estimates
- Performance expectations per variant
- Support for multiple Gemma 4 versions
- Guidance for CPU and GPU inference
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
Gemma 4 is Google's latest entry in its family of open-weight large language models, designed to give developers and researchers direct access to a capable foundation model they can run, fine-tune, and deploy on their own infrastructure. It builds on the lineage of earlier Gemma releases with improvements in reasoning, instruction following, and multilingual handling. The model is distributed with open weights, making it suitable for experimentation, on-device inference, and integration into custom applications without relying on a hosted API. It can be used through popular frameworks such as Hugging Face Transformers, llama.cpp, Ollama, and JAX, and runs across GPUs, TPUs, and consumer hardware depending on the chosen variant. Gemma 4 targets teams that need flexibility, transparency, and control over their AI stack, including those building private assistants, research prototypes, or specialized domain models through fine-tuning.
Criteria breakdown
- Open-source model weights
- Multiple parameter-size variants
- Instruction-tuned and base versions
- Compatible with Hugging Face and Ollama
- Supports local and cloud deployment
- Fine-tuning and LoRA adaptation friendly
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
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