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A Qwen3 mixture-of-experts model (30.5B total, 3.3B active) that reasons in a think block or answers directly, chosen turn by turn. On Wiro it accepts function definitions and can return JSON.

Qwen3-30B-A3B-Thinking-2507 reasons before every answer, and its thinking runs longer than in the original Qwen3-30B-A3B. Qwen recommends it for highly complex reasoning tasks.

Qwen3-32B is Qwen's 32.8B dense model. It reasons step by step by default and answers directly on request, and on Wiro it can call your functions or return a JSON object.

A Qwen3-Coder mixture-of-experts model for everyday programming, with 3.3B of its 30.5B parameters active per token. It runs in non-thinking mode only, so each reply starts with the answer.

Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning.

Puts one or two faces into a scene it generates from your prompt, or into a photo you upload. In a two-person photo it can replace just the left or the right person.

Mixed dataset LLM Information Security v1.5 - Incident Response v1.3.1 - Threat Hunting v1.3.2 - Ethical Exploit Development v2.0 - Purple Team Tactics v1.3 - Reverse Engineering v2.0

DeepSeek's mixture-of-experts vision-language model, run here as the Tiny checkpoint. Give it one image and a question and it answers in plain text, from a full caption to a one-line note on what looks odd.

Qwen2.5-14B-Instruct is a large language model by Alibaba’s Qwen team, optimized for instruction-following tasks with 14 billion parameters. It offers strong reasoning, multilingual capabilities, and efficient performance, making it suitable for chatbots, content creation, and various AI-driven applications.

Qwen reports 83.1 on MATH and 74.5 on Arena-Hard for this 32.5B-parameter Qwen2.5 chat model. On Wiro a request holds about 17,000 input tokens, with JSON mode and function calling.

Sana 1.6B adapted by NVIDIA for 2K text-to-image output: images around 2048 × 2048 in BF16, from prompts in English, Chinese, emoji or a mix.

Half-precision (FP16) weights for Sana 1.6B at 1024px. NVIDIA's model pairs a linear diffusion transformer with a Gemma 2 text encoder and a 32x compression autoencoder.

The 4K checkpoint of NVIDIA's 1.6B Sana model, fine-tuned from the 1024px BF16 release to generate images up to 4096 × 4096, with prompts in English, Chinese or emoji.

The largest Qwen-based R1 distill, built by DeepSeek on Qwen2.5-32B. DeepSeek's results put it ahead of OpenAI o1-mini on AIME 2024, MATH-500, GPQA Diamond and LiveCodeBench.

The mid-sized R1 distill: Qwen2.5-14B fine-tuned on DeepSeek-R1 samples. DeepSeek reports results close to or above OpenAI o1-mini on most of the benchmarks it lists.

DeepSeek's R1 distill on a Llama 3.1 8B base. It writes out a chain of thought before answering and is the only Llama option among the smaller R1 distills.

Qwen2.5-Math-7B fine-tuned by DeepSeek on samples from DeepSeek-R1. A 7B model that shows its working on math problems; DeepSeek reports 92.8 on MATH-500, above OpenAI o1-mini.

DeepSeek's smallest R1 distill: Qwen2.5-Math-1.5B fine-tuned on samples generated by DeepSeek-R1. It works through a math problem step by step before it gives the answer.

A 9B-parameter model from the EU-funded EuroLLM project, instruction-tuned for chat and translation in all 24 official EU languages plus 11 others, among them Turkish and Arabic.

utter-project/EuroLLM-1.7B-Instruct is a 1.7 billion parameter multilingual AI model designed to understand and generate text in all European Union languages and additional relevant languages. It has been trained on 4 trillion tokens from diverse sources and further instruction-tuned on EuroBlocks to enhance its capabilities in general instruction-following and machine translation.