Inkling vs. Ai2 OLMoE

Inkling is an open-weights multimodal AI model from Thinking Machines built to support customization, agentic workflows, coding, reasoning, vision, audio, and enterprise AI use cases. The model is a Mixture-of-Experts transformer with 975 billion total parameters, 41 billion active parameters, 256 routed experts per MoE layer, and six routed experts active per token. It supports context windows up to 1 million tokens and was pretrained on 45 trillion tokens across text, images, audio, and video. Inkling is designed as a broad foundation model rather than a narrowly optimized benchmark model, giving it balanced capabilities across reasoning, coding, factuality, instruction following, vision, audio, tool use, and safety. Its controllable thinking effort lets developers adjust how much computation and generated reasoning the model uses, helping teams balance quality, latency, and cost for different production needs. The model can run agentic coding tasks, use tools, create web apps, generate polished multi-page artifacts, reason over long contexts, and work through iterative refinement loops. For multimodal tasks, Inkling can process images, answer questions about visual content, transcribe and reason over audio, follow spoken instructions, and combine visual reasoning with code-based tools such as Python. Thinking Machines trained Inkling for calibration, instruction following, factual reliability, refusal behavior, and safety across multiple modalities, including evaluations for dangerous capabilities and human-AI threat vectors. Inkling is available on Tinker for fine-tuning, with 64K and 256K context options, an Inkling Playground for testing, cookbook recipes, and support for multimodal post-training workflows. Its full weights are available on Hugging Face, and deployment support is available through APIs and infrastructure partners such as TogetherAI, Fireworks, Modal, Databricks, Baseten, SGLang, vLLM, llama.cpp, and transformers.

Ai2 OLMoE is a completely open-source language model that utilizes a mixture-of-experts approach, designed to operate fully on-device, which allows users to explore its capabilities in a secure and private environment. The primary goal of this application is to aid researchers in enhancing on-device intelligence while enabling developers to rapidly prototype innovative AI applications without relying on cloud services. As a highly efficient version within the Ai2 OLMo model family, OLMoE empowers users to engage with advanced local models in practical situations, explore strategies to improve smaller AI systems, and locally test their models using the provided open-source framework. Furthermore, OLMoE can be smoothly integrated into a variety of iOS applications, prioritizing user privacy and security by functioning entirely on-device. Users can easily share the results of their conversations with friends or colleagues, enjoying the benefits of a completely open-source model and application code. This makes Ai2 OLMoE an outstanding resource for personal experimentation and collaborative research, offering extensive opportunities for innovation and discovery in the field of artificial intelligence. By leveraging OLMoE, users can contribute to a growing ecosystem of on-device AI solutions that respect user privacy while facilitating cutting-edge advancements.