Best AI Vision Models for Tinker
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Compare the Top AI Vision Models that integrate with Tinker as of August 2026
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This a list of AI Vision Models that integrate with Tinker. Use the filters on the left to add additional filters for products that have integrations with Tinker. View the products that work with Tinker in the table below.
What are AI Vision Models for Tinker?
AI vision models, also known as computer vision models, are designed to enable machines to interpret and understand visual information from the world, such as images or video. These models use deep learning techniques, often employing convolutional neural networks (CNNs), to analyze patterns and features in visual data. They can perform tasks like object detection, image classification, facial recognition, and scene segmentation. By training on large datasets, AI vision models improve their accuracy and ability to make predictions based on visual input. These models are widely used in fields such as healthcare, autonomous driving, security, and augmented reality. Compare and read user reviews of the best AI Vision Models for Tinker currently available using the table below. This list is updated regularly.
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1
Inkling
Thinking Machines Lab
Inkling is an open-weights multimodal AI model from Thinking Machines designed as a customizable foundation model for developers, researchers, and enterprises. The model is a Mixture-of-Experts transformer with 975 billion total parameters, 41 billion active parameters, and support for context windows up to 1 million tokens. Inkling was trained from scratch on text, images, audio, and video, giving it native capabilities across reasoning, coding, agentic tool use, vision, audio, factuality, and instruction following. It is built with controllable thinking effort so users can balance performance, latency, and token efficiency for different workloads. The model is available for fine-tuning on Tinker, with playground access, API availability through ecosystem partners, and full weights published on Hugging Face. Built for customization, Inkling gives teams an open-weights base model for building domain-specific AI systems, multimodal agents, coding workflows, research tools, and more.Starting Price: Free -
2
Inkling-Small
Thinking Machines Lab
Inkling-Small is an efficient model that offers performance comparable to Inkling at a quarter of its size. It is a Mixture-of-Experts transformer with 276 billion total parameters and 12 billion active parameters, trained on NVIDIA GB300 NVL72 systems. It supports native reasoning across text, images, and audio, variable thinking effort, and context windows of up to one million tokens. Users adjust reasoning effort from minimal to extra high to balance performance and compute. Improved pre-training data, post-training with on-policy distillation from Inkling, and extended agentic coding reinforcement learning helped Inkling-Small surpass its larger counterpart on reasoning and coding benchmarks. It performs well in coding and tool-use harnesses, exceeds 80% on SWE-bench Verified, and combines strong reasoning with efficient output. Its encoder-free multimodal architecture processes audio as dMel spectrograms and images as 40-by-40-pixel patches alongside text tokens.Starting Price: $0.30 per million input tokens
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