Gemma 4 vs. Inkling 2 Small Comparison


Gemma 4 Google 5.0 out of 5 stars	Inkling 2 Small Thinking Machines Lab	+	+
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About Gemma 4 is an AI model introduced by Google and built on the Gemini architecture to deliver improved performance and flexibility. The model is designed to run efficiently on a single GPU or TPU, making it more accessible to developers and researchers. Gemma 4 enhances capabilities in natural language understanding and text generation, supporting a wide range of AI-driven applications. Its architecture allows it to handle complex tasks while maintaining efficient resource usage. Developers can use the model to build applications that rely on advanced language processing and automation. The design emphasizes scalability so that it can support both smaller projects and larger AI systems. By combining efficiency with powerful language capabilities, Gemma 4 helps advance the development of modern AI solutions.	About Inkling 2 Small is a multimodal AI model developed by Thinking Machines Lab for advanced reasoning, autonomous software engineering, visual understanding, and AI agent development. The model uses a mixture-of-experts architecture with 594 billion total parameters and 17 billion active parameters to balance intelligence and computational efficiency. It supports text and image inputs, a context window of up to one million tokens, and configurable reasoning effort for tasks requiring different levels of analysis. Its agentic capabilities enable autonomous code generation, debugging, website development, scientific research, and complex multistep workflows involving external tools. Inkling 2 Small also supports fine-tuning and reinforcement learning through Thinking Machines Lab's Tinker platform, allowing developers to customize the model for specialized applications.
Platforms Supported Windows Supported Mac Supported Linux Supported Cloud Not Supported On-Premises Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported	Platforms Supported Windows Not Supported Mac Not Supported Linux Not Supported Cloud Supported On-Premises Not Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported
Audience Developers, AI researchers, and organizations that want an efficient and scalable language model for building AI-powered applications and research projects	Audience AI developers, machine learning engineers, software developers, autonomous agent researchers, data scientists, AI research laboratories, DevOps professionals, computational scientists, enterprise AI teams, and organizations seeking multimodal language models, long-context reasoning, AI coding agents, reinforcement learning, model fine-tuning, visual understanding, and autonomous research capabilities
Support Phone Support Not Supported 24/7 Live Support Not Supported Online Not Supported	Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported
API Offers API Not Supported	API Offers API Supported
Screenshots and Videos No images available	Screenshots and Videos View more images or videos
Pricing Free Open source Free Version Supported Free Trial Not Supported	Pricing $0.30 per million input tokens $0.30 per million input tokens, $0.03 per million cached input tokens, and $1.00 per million output tokens Free Version Not Supported Free Trial Not Supported
Reviews/Ratings Overall 5.0 / 5 ease 5.0 / 5 features 5.0 / 5 design 5.0 / 5 support 5.0 / 5 Read all reviews	Reviews/Ratings Overall 0.0 / 5 ease 0.0 / 5 features 0.0 / 5 design 0.0 / 5 support 0.0 / 5 This software hasn't been reviewed yet. Be the first to provide a review: Review this Software
Pros & Cons from Real Users Pros Gemma 4 delivers exceptional performance for an open model. Responses are fast, accurate, and reliable across coding, reasoning, and general knowledge tasks. The model supports multimodal inputs, native system prompts, and function calling, making it an excellent choice for developers building AI applications. It is optimized to run efficiently on consumer GPUs and even smaller devices, offering impressive speed without sacrificing quality. Cons The largest models still require capable hardware to unlock their full potential. Some advanced enterprise features and cloud integrations may require additional setup compared to fully managed AI services.
Training Documentation Supported Webinars Not Supported Live Online Not Supported In Person Not Supported	Training Documentation Supported Webinars Not Supported Live Online Not Supported In Person Not Supported
Company Information Google Founded: 1998 United States deepmind.google/models/gemma/	Company Information Thinking Machines Lab Founded: 2025 United States thinkingmachines.ai/
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Gemma 3 Google	Inkling-Small Thinking Machines Lab 5.0 out of 5 stars
Gemma Google View All	Inkling Thinking Machines Lab View All
Categories AI Models Supported Large Language Models Supported Open-Weight AI Models Supported Small Language Models Supported	Categories AI Coding Models Supported AI Models Supported AI Reasoning Models Supported AI Vision Models Supported Open-Weight AI Models Supported Small Language Models Supported

Integrations BLACKBOX AI Supported BaseRT Supported C Supported EmbeddingGemma Supported HTML Supported JavaScript Supported Kaggle Supported Keras Supported MedGemma Supported Mirai Supported Model Context Protocol (MCP) Not Supported Nebius Token Factory Supported OpenCode Supported Ruby Supported Rust Supported SQL Supported Scala Supported Tinker Not Supported Vercel AI Gateway Supported Visual Basic Supported Show More Integrations View All 49 Integrations	Integrations BLACKBOX AI Not Supported BaseRT Not Supported C Not Supported EmbeddingGemma Not Supported HTML Not Supported JavaScript Not Supported Kaggle Not Supported Keras Not Supported MedGemma Not Supported Mirai Not Supported Model Context Protocol (MCP) Supported Nebius Token Factory Not Supported OpenCode Not Supported Ruby Not Supported Rust Not Supported SQL Not Supported Scala Not Supported Tinker Supported Vercel AI Gateway Not Supported Visual Basic Not Supported Show More Integrations View All 2 Integrations
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