Leanstral 1.5 vs. Inkling

Leanstral 1.5 is a model under the Apache-2.0 license, crafted for proficient proof engineering within Lean 4, with the goal of improving both the functionality and accessibility of formal verification. This model features a remarkable total of 119 billion parameters, of which 6 billion are actively utilized, representing a major leap in efficiency for tasks related to theorem proving, agent-based proof engineering, and practical code verification. The evolution of Leanstral 1.5 included an extensive three-phase training regimen, which encompassed mid-training, supervised fine-tuning, and reinforcement learning through CISPO. In a multiturn environment, the model is designed to accept a theorem statement, propose a proof, and iteratively adjust its strategy based on input from the Lean compiler until the proof compiles successfully or resources run out. Operating in the code agent framework, Leanstral acts similarly to a developer navigating through a file system, enabling it to modify files, run bash commands, and engage with the Lean language server to track goals, errors, and type information in real time. This cutting-edge methodology not only simplifies the proof engineering workflow but also significantly enriches the overall user experience in formal verification tasks, making the process both more efficient and user-friendly. Furthermore, the integration of these capabilities positions Leanstral 1.5 as a transformative tool in the landscape of formal verification.

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.