Compare GPT-6 Astra vs. Inkling

GPT-6 Astra is an advanced general-purpose AI model from OpenAI designed for demanding agentic, technical, scientific, and professional workflows. The model combines reasoning with computer-use capabilities that let it interact with applications, websites, development environments, productivity software, and specialized tools. It can perform activities such as online research, CRM updates, form completion, calendar organization, data analysis, software testing, troubleshooting, and frontend quality assurance. Astra is also trained for professional artifact creation, including documents, presentations, spreadsheets, analyses, websites, applications, and visual designs that follow provided templates and business conventions. Its software engineering capabilities support complex coding, codebase understanding, debugging, database migration, verification, and extended autonomous development tasks. OpenAI has also introduced a Codex context system for Astra that can maintain notes across context windows and search earlier conversation and tool history instead of relying exclusively on repeated summarization. Scientific capabilities span areas such as mathematics, biology, chemistry, health, data analysis, and the use of specialized research software. The model includes strengthened alignment and cybersecurity safeguards intended to help it remain within authorized task boundaries while supporting legitimate activities such as secure code review and vulnerability remediation. GPT-6 Astra is available across supported ChatGPT plans and developer platforms, including the OpenAI API and Amazon Web Services infrastructure.

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.