Molmo vs. Inkling

Molmo is an advanced suite of multimodal AI models developed by the Allen Institute for AI (Ai2) that aims to bridge the gap between open-source and proprietary technologies, ensuring competitive performance on various academic assessments and evaluations by human users. Unlike many existing multimodal models that rely on synthetic datasets created from proprietary sources, Molmo is solely trained on publicly accessible data, fostering both transparency and reproducibility within the realm of AI research. A key innovation in Molmo's creation is the inclusion of PixMo, a distinctive dataset that features detailed image captions curated by human annotators through speech-based descriptions, complemented by 2D pointing data that allows models to communicate using both natural language and non-verbal cues. This ability enables Molmo to interact with its environment in a more refined way, such as by indicating particular objects within images, which expands its applicability across various domains, including robotics, augmented reality, and interactive user interfaces. Moreover, the strides made by Molmo are poised to redefine standards for future research and development in multimodal AI, opening up new avenues for exploration and application. As the field evolves, the influence of Molmo's innovative approach could inspire similar projects aimed at enhancing human-AI interaction.

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.