Compare Dolly vs. Inkling

Dolly is an economical large language model that surprisingly demonstrates a notable level of instruction-following abilities similar to those seen in ChatGPT. While the Alpaca team's research revealed that cutting-edge models could be encouraged to excel in high-quality instruction adherence, our findings indicate that even older open-source models with earlier architectures can display remarkable behaviors when fine-tuned on a modest set of instructional training data. By utilizing an existing open-source model with 6 billion parameters from EleutherAI, Dolly has been slightly adjusted to enhance its ability to follow instructions, showcasing skills like brainstorming and generating text that were absent in its original form. This approach not only highlights the potential of older models but also opens new avenues for leveraging existing technologies in innovative ways.

Inkling is Thinking Machines’ open-weights foundation model built for customization, multimodal reasoning, and agentic AI workflows. The model uses a Mixture-of-Experts architecture with 975 billion total parameters and 41 billion active parameters, making it large in capacity while activating only a subset of experts per token. Inkling supports up to a 1 million token context window and was pretrained on 45 trillion tokens spanning text, images, audio, and video. It is designed as a broad generalist model with strengths across coding, reasoning, instruction following, factuality, tool use, vision, audio understanding, forecasting, and safety. Developers can tune its thinking effort to trade off latency, cost, and performance, which is useful for production systems that need efficient reasoning at scale. Inkling can be fine-tuned on Tinker, tested in the Inkling Playground, and deployed through partners such as TogetherAI, Fireworks, Modal, Databricks, Baseten, vLLM, SGLang, llama.cpp, and Hugging Face transformers. The model can generate applications, operate tools, create styled artifacts, reason over visual and audio inputs, and support long refinement loops for collaborative work. Thinking Machines also previewed Inkling-Small, a lighter Mixture-of-Experts model with 276 billion total parameters and 12 billion active parameters for lower-cost and lower-latency workloads. By combining open weights, multimodal training, agentic capabilities, efficient reasoning, and fine-tuning support, Inkling gives builders a flexible AI foundation for specialized products and workflows.