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Wiro AI

Social Media & ViralEcommerceHuman ResourcesFast Inference

Video Generation

Text to VideoImage to VideoSpeech to VideoTalking Head

Image Generation

Text to ImageImage to ImageImage Editing

Audio & Speech

Text to SpeechSpeech to TextText to MusicText to SongImage to SongVideo to MusicVoice CloneRealtime Conversation

3D Generation

3D Generation

LLM & Chat

ChatLLMPartner LLMRAGReasoning

Partners

WiroOpenAIGoogleByteDanceRunwayElevenlabsBlack Forest LabsKling-AIMiniMaxPixVerse
Qwen3.5-27B - AI Tool Cover Image
Chat

Qwen/Qwen3.5-27B

Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency.
1
GLM-4.7-Flash - AI Tool Cover Image
Chat

zai-org/GLM-4.7-Flash

A chat-based language model optimized for conversational AI tasks with support for custom prompts and session management.
1
rag-chat-github - AI Tool Cover Image
Chat

wiro/rag-chat-github

Instantly retrieve and analyze content from any GitHub repository. Select your LLM model, extract relevant information from codebases or documentation, and generate context-aware responses with ease!
0
rag-chat-youtube - AI Tool Cover Image
Chat

wiro/rag-chat-youtube

Extract insights directly from YouTube videos by simply providing a URL. Choose your LLM model, access video transcripts or summaries, and create contextually rich conversations effortlessly!
2
rag-chat-website - AI Tool Cover Image
Chat

wiro/rag-chat-website

Instantly retrieve and analyze content from any website URL. Select your LLM model, fetch key information from the page, and generate context-aware responses with ease!
0
rag-chat - AI Tool Cover Image
Chat

wiro/rag-chat

Wiro LLM Conversational RAG Tool – A unified interface for seamless RAG-based conversations. Select your preferred LLM model, retrieve relevant data from external sources, and generate context-aware responses effortlessly!
0
chat - AI Tool Cover Image
Chat

wiro/chat

Wiro LLM Conversational chat script! Runs every LLM with one tool.
2
wiroai-turkish-llm-9b - AI Tool Cover Image
Chat

wiro/wiroai-turkish-llm-9b

Meet with Wiro AI's Turkish Large Language Model (LLM)! A robust language model with more Turkish language and culture support!
0
gpt-oss-20b - AI Tool Cover Image
Chat

openai/gpt-oss-20b

OpenAI GPT-OSS-20B model.
0
Qwen3-30B-A3B - AI Tool Cover Image
Chat

Qwen/Qwen3-30B-A3B

Qwen3-30B-A3B model.
0
Qwen3-30B-A3B-Thinking-2507 - AI Tool Cover Image
Chat

Qwen/Qwen3-30B-A3B-Thinking-2507

Qwen3-30B-A3B-Thinking-2507 model.
0
Qwen3-32B - AI Tool Cover Image
Chat

Qwen/Qwen3-32B

Qwen3-32B model.
0
Qwen3-Coder-30B-A3B-Instruct - AI Tool Cover Image
Chat

Qwen/Qwen3-Coder-30B-A3B-Instruct

Qwen3-Coder-30B-A3B-Instruct model.
0
SenecaLLM-x-QwQ-32B-Q8_Max-Version - AI Tool Cover Image
Chat

AlicanKiraz0/SenecaLLM-x-QwQ-32B-Q8_Max-Version

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
0
Qwen2.5-14B-Instruct - AI Tool Cover Image
Chat

Qwen/Qwen2.5-14B-Instruct

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.
0
Qwen2.5-32B-Instruct - AI Tool Cover Image
Chat

Qwen/Qwen2.5-32B-Instruct

Qwen2.5-32B-Instruct is a powerful large language model developed by Alibaba’s Qwen team, designed for instruction-following tasks with enhanced reasoning and natural language understanding capabilities. Optimized for efficiency and accuracy, it supports multi-turn conversations and complex queries, making it suitable for applications such as chatbots, content generation, and AI assistants.
0
DeepSeek-R1-Distill-Qwen-32B - AI Tool Cover Image
Chat

deepseek-ai/DeepSeek-R1-Distill-Qwen-32B

DeepSeek-R1-Distill-Qwen-32B is a distilled version of the DeepSeek-R1 model with 32 billion parameters, leveraging the Qwen architecture to deliver high-quality language understanding and generation. Through knowledge distillation, it retains the strengths of larger models while offering improved efficiency and reduced computational requirements. This model is ideal for large-scale AI applications that demand robust performance with optimized resource utilization.
0
DeepSeek-R1-Distill-Qwen-14B - AI Tool Cover Image
Chat

deepseek-ai/DeepSeek-R1-Distill-Qwen-14B

DeepSeek-R1-Distill-Qwen-14B is a distilled version of the DeepSeek-R1 model, featuring 14 billion parameters. Built on the Qwen architecture, it utilizes advanced knowledge distillation techniques to achieve a balance between high performance and computational efficiency. This model is well-suited for a wide range of natural language processing tasks, providing accurate and context-aware responses while optimizing resource consumption for deployment in production environments.
0
DeepSeek-R1-Distill-Llama-8B - AI Tool Cover Image
Chat

deepseek-ai/DeepSeek-R1-Distill-Llama-8B

DeepSeek-R1-Distill-Llama-8B is a distilled version of the DeepSeek-R1 model based on the LLaMA architecture, featuring 8 billion parameters. It offers a balance between performance and efficiency by leveraging knowledge distillation techniques to reduce computational costs while maintaining high-quality language processing capabilities. This model is ideal for applications that require powerful text generation and understanding with optimized resource usage.
0
DeepSeek-R1-Distill-Qwen-7B - AI Tool Cover Image
Chat

deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

DeepSeek-R1-Distill-Qwen-7B is a distilled version of the DeepSeek-R1 model with 7 billion parameters, designed to provide high-quality language understanding while optimizing efficiency. Leveraging advanced knowledge distillation techniques, it retains the core capabilities of larger models with improved speed and lower resource consumption. This model is well-suited for tasks requiring robust natural language processing while maintaining cost-effective deployment.
0