Search the full model catalog.

Ask questions about a web page: the app reads the URL, retrieves the passages closest to your question and has the LLM you pick answer from them, with optional per-user chat history.

Answers questions from files you upload, such as PDF, Word, PowerPoint or CSV. The chat model you choose writes each answer from the passages that best match the question.

One set of chat inputs for open-weight models hosted on Wiro, such as Llama, Qwen, Mistral and DeepSeek. Choose the base model on each request and keep separate conversation histories per user and session.

Wiro's Turkish chat model, fine-tuned from Google's Gemma 2 9B on more than 500,000 Turkish instructions to answer in natural Turkish with local and cultural context.

OpenAI's smaller open-weight reasoning model, a mixture-of-experts with 21B parameters (3.6B active) under Apache 2.0, served here as a text chat model with per-session history.

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.

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

Qwen2.5-14B-Instruct is the 14.7B instruction-tuned model from Alibaba's Qwen2.5 series, built for chat, multilingual writing, coding help and JSON output.

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.

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.

The first instruction-tuned model in the EU-funded EuroLLM series: 1.7B parameters, tuned on EuroBlocks for translation and general instructions in 35 languages, including all 24 official EU languages.

An 8B clinical chat model from M42, fine-tuned from Llama 3 8B Instruct to answer the medical questions that general-purpose assistants are often tuned to avoid.