Inkling-Small vs. Qwen Code Comparison


Inkling-Small Thinking Machines Lab	Qwen Code Qwen	+	+
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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.	About Qwen3‑Coder is an agentic code model available in multiple sizes, led by the 480B‑parameter Mixture‑of‑Experts variant (35B active) that natively supports 256K‑token contexts (extendable to 1M) and achieves state‑of‑the‑art results on Agentic Coding, Browser‑Use, and Tool‑Use tasks comparable to Claude Sonnet 4. Pre‑training on 7.5T tokens (70 % code) and synthetic data cleaned via Qwen2.5‑Coder optimized both coding proficiency and general abilities, while post‑training employs large‑scale, execution‑driven reinforcement learning and long‑horizon RL across 20,000 parallel environments to excel on multi‑turn software‑engineering benchmarks like SWE‑Bench Verified without test‑time scaling. Alongside the model, the open source Qwen Code CLI (forked from Gemini Code) unleashes Qwen3‑Coder in agentic workflows with customized prompts, function calling protocols, and seamless integration with Node.js, OpenAI SDKs, and more.
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 Supported Mac Supported Linux Supported Cloud Not Supported On-Premises Not Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported
Audience AI researchers and developers seeking an efficient open-weight multimodal model for reasoning, agentic tasks, and lower-cost deployment	Audience AI researchers and software engineers interested in a tool providing an agentic coding model with large‑context support and turnkey CLI tools for real‑world, and automated code generation
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 Not Supported
Screenshots and Videos View more images or videos	Screenshots and Videos View more images or videos
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	Pricing Free Free Version Supported Free Trial Not Supported
Reviews/Ratings Overall 5.0 / 5 features 4.0 / 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 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 Supported In Person Not Supported
Company Information Thinking Machines Lab Founded: 2025 United States thinkingmachines.ai/news/inkling-small/	Company Information Qwen Founded: 2023 China github.com/QwenLM/qwen-code
Alternatives GPT-5.6 Luna OpenAI	Alternatives Muse Code Meta
Inkling Thinking Machines Lab	iFlow
Muse Spark 1.1 Meta	MiMo Code Xiaomi Technology
ALL-INKL.COM	Qwen3-Coder Qwen
Beam Reflection View All	Qwen3-Coder-Next Alibaba View All
Categories AI Coding Models Supported AI Models Supported AI Reasoning Models Supported AI Vision Models Supported Small Language Models Supported	Categories Agentic AI Supported AI Coding Agents Supported

Integrations Alibaba AI Coding Plan Not Supported Claude Fable 5 Not Supported Claude Fable 5.1 Not Supported Claude Fable 5.5 Not Supported Claude Opus 4.6 Not Supported Claude Opus 4.8 Not Supported Claude Opus 5 Not Supported Claude Sonnet 4 Not Supported Claude Sonnet 4.5 Not Supported Melaya Not Supported Model Context Protocol (MCP) Supported Node.js Not Supported OpenAI Not Supported Qwen 4 Not Supported Qwen2.5 Not Supported Qwen3.7-Max Not Supported Qwen3.8-Omni-Flash Not Supported QwenCloud Not Supported Step 5 Preview Not Supported Tinker Supported Show More Integrations View All 2 Integrations	Integrations Alibaba AI Coding Plan Supported Claude Fable 5 Supported Claude Fable 5.1 Supported Claude Fable 5.5 Supported Claude Opus 4.6 Supported Claude Opus 4.8 Supported Claude Opus 5 Supported Claude Sonnet 4 Supported Claude Sonnet 4.5 Supported Melaya Supported Model Context Protocol (MCP) Not Supported Node.js Supported OpenAI Supported Qwen 4 Supported Qwen2.5 Supported Qwen3.7-Max Supported Qwen3.8-Omni-Flash Supported QwenCloud Supported Step 5 Preview Supported Tinker Not Supported Show More Integrations View All 33 Integrations
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