Best AI Reasoning Models for Tinker
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Compare the Top AI Reasoning Models that integrate with Tinker as of August 2026
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This a list of AI Reasoning 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 Reasoning Models for Tinker?
AI reasoning models are artificial intelligence models designed to perform complex problem-solving, logical reasoning, planning, and multi-step decision-making beyond traditional language generation. These models use advanced inference techniques to break down difficult tasks, evaluate alternatives, apply logic, and generate more accurate responses for domains such as mathematics, programming, scientific research, business analysis, and autonomous AI agents. AI reasoning models often support extended context windows, tool use, code execution, structured outputs, and agentic workflows to solve complex real-world problems. Many are available through APIs, cloud platforms, and AI development frameworks, enabling developers to build intelligent applications and autonomous systems. By combining language understanding with advanced reasoning capabilities, AI reasoning models help organizations improve decision-making, automate complex workflows, and power next-generation AI applications. Compare and read user reviews of the best AI Reasoning 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 -
3
Qwen3
Alibaba
Qwen3, the latest iteration of the Qwen family of large language models, introduces groundbreaking features that enhance performance across coding, math, and general capabilities. With models like the Qwen3-235B-A22B and Qwen3-30B-A3B, Qwen3 achieves impressive results compared to top-tier models, thanks to its hybrid thinking modes that allow users to control the balance between deep reasoning and quick responses. The platform supports 119 languages and dialects, making it an ideal choice for global applications. Its pre-training process, which uses 36 trillion tokens, enables robust performance, and advanced reinforcement learning (RL) techniques continue to refine its capabilities. Available on platforms like Hugging Face and ModelScope, Qwen3 offers a powerful tool for developers and researchers working in diverse fields.Starting Price: Free
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