Gemini 4 Argon vs. Inkling-Small Comparison


Gemini 4 Argon Google	Inkling-Small Thinking Machines Lab	+	+
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About Gemini 4 Argon is a frontier artificial intelligence model developed by Google DeepMind for complex reasoning, software engineering, cybersecurity, and professional knowledge work. The model supports multimodal inputs, including text, images, audio, video, and PDF documents, with a unified context window of approximately one million tokens. Its advanced reasoning capabilities enable it to analyze difficult problems, maintain context across lengthy workflows, and execute multistep tasks. Gemini 4 Argon supports autonomous AI agents, code generation, application development, scientific research, and analysis of legal and financial information. Developer capabilities include function calling, code execution, structured outputs, search grounding, and preview support for computer use. The model is available through the Gemini API, Google AI Studio, Google Antigravity, and supported Gemini applications and enterprise services.	About 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.
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	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 Software developers, engineers, AI teams, cybersecurity professionals, financial and legal professionals, researchers, enterprises, and other advanced users that need frontier AI for complex reasoning, long-horizon coding, knowledge work, multimodal analysis, and multi-step agentic workflows	Audience AI researchers and developers seeking an efficient open-weight multimodal model for reasoning, agentic tasks, and lower-cost deployment
Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported	Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported
API Offers API Supported	API Offers API Supported
Screenshots and Videos View more images or videos	Screenshots and Videos View more images or videos
Pricing $2 per 1M tokens (input) $2 per million input tokens and $10 per million output tokens, with cached input tokens priced at 95% off input token price. Free Version Not Supported Free Trial Not Supported	Pricing $0.30 per million input tokens $0.30 per million input tokens and $1.20 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 5.0 / 5 features 4.0 / 5 Read all reviews
Pros & Cons from Real Users Pros I can give it a large coding problem, a long research task, or something involving multiple tools and it does a much better job keeping the goal in view instead of slowly wandering off course. Coding is especially strong. Google reports 77.9% on DeepSWE v1.1 and 91.9% on Vibe Code Bench, and it feels very comfortable with repo-level work, multi-file changes, debugging, and longer agent loops. The 1M-token context window is also genuinely useful. I can keep a huge amount of code, documentation, logs, PDFs, and prior work in the same session without constantly trimming context. I also like the tool support. Computer use, code execution, search grounding, file search, function calling, and URL context make Argon feel much more like an agent model than a normal chatbot. Cons The main downside is that it is overkill for simple tasks. I would still use Flash for quick coding help, lightweight automation, or anything where speed and cost matter more than maximum reasoning depth.	Pros & Cons from Real Users Pros Inkling-Small is really interesting from a developer’s point of view because it hits a sweet spot between serious model capability and practical deployability. A 276B-parameter model with only 12B active parameters per token is exactly the kind of architecture that makes sense if you care about cost, speed, and scaling real AI workflows. I also like that it is open weights under Apache 2.0. That makes it way more appealing for developers who want to fine-tune, inspect, customize, or build on top of the model without being completely locked into a closed API. The multimodal support is a big plus too. Being able to work with text, images, and audio inputs gives Inkling-Small a lot of room for developer tools, coding agents, support bots, document workflows, and internal automation. Cons The main downside is that “small” here is still not tiny. Even with only 12B active parameters, this is still a large open model that will require real infrastructure if you want to host it yourself. I would also want to test it deeply before making it the backbone of a production coding agent. The model card and early coverage look promising, but real developer workflows expose problems that benchmarks do not always catch: messy repos, flaky tests, weird dependencies, tool failures, and long multi-step tasks.
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 gemini.com	Company Information Thinking Machines Lab Founded: 2025 United States thinkingmachines.ai/news/inkling-small/
Alternatives Claude Sonnet 5.5 Anthropic	Alternatives GPT-5.6 Luna OpenAI
Claude Fable 5.1 Anthropic	Inkling Thinking Machines Lab
Claude Mythos 5.1 Anthropic	Muse Spark 1.1 Meta
Gemini 3.5 Flash Google	ALL-INKL.COM
Gemini 3.6 Flash Google View All	Beam Reflection View All
Categories AI Coding Models Supported AI Models Supported AI Reasoning Models Supported AI Vision Models Supported Foundation Models Supported Large Language Models Supported Multimodal Models Supported	Categories AI Coding Models Supported AI Models Supported AI Reasoning Models Supported AI Vision Models Supported Small Language Models Supported

Integrations Model Context Protocol (MCP) Supported C# Supported Gemini 3.5 Flash-Lite Supported Gemini 3.8 Flash Cyber Supported Gemini Managed Agents Supported Go Supported Google AI Overviews Supported Google AI Plus Supported Google AI Ultra Supported Java Supported OfoxAI Supported Python Supported R Supported Replit Supported Ruby Supported Rust Supported Solidity Supported Tinker Not Supported Vercel AI Gateway Supported XML Supported Show More Integrations View All 63 Integrations	Integrations Model Context Protocol (MCP) Supported C# Not Supported Gemini 3.5 Flash-Lite Not Supported Gemini 3.8 Flash Cyber Not Supported Gemini Managed Agents Not Supported Go Not Supported Google AI Overviews Not Supported Google AI Plus Not Supported Google AI Ultra Not Supported Java Not Supported OfoxAI Not Supported Python Not Supported R Not Supported Replit Not Supported Ruby Not Supported Rust Not Supported Solidity Not Supported Tinker Supported Vercel AI Gateway Not Supported XML Not Supported Show More Integrations View All 2 Integrations
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