Inkling-Small vs. SWE-2 Flash Comparison


Inkling-Small Thinking Machines Lab	SWE-2 Flash Cognition	+	+
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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 SWE-2 Flash is an AI coding model developed by Cognition to provide fast, cost-efficient software engineering capabilities for autonomous development agents. Built on Thinking Machines' Inkling-Small foundation model, it uses mid-training and reinforcement learning to improve coding performance, reasoning, and task execution. The model supports software development activities such as code generation, code reviews, feature development, documentation updates, and automated engineering workflows. SWE-2 Flash generates approximately 200 tokens per second, making it three times faster and five times less expensive than the larger SWE-2 model. Its 276-billion-parameter mixture-of-experts architecture activates approximately 12 billion parameters during inference to balance performance and computational efficiency. SWE-2 Flash is available through Devin Desktop and CLI, with Cognition also developing a secure model-weight sharing program for trusted research and enterprise partners.
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 AI researchers and developers seeking an efficient open-weight multimodal model for reasoning, agentic tasks, and lower-cost deployment	Audience Software developers, AI coding assistant users, software engineering teams, DevOps professionals, autonomous agent developers, AI researchers, enterprise development organizations, and cybersecurity researchers seeking fast, cost-efficient AI coding, automated code reviews, feature implementation, software maintenance, technical reasoning, and agentic development workflows
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 $20/month Free Version Supported Free Trial Not Supported
Reviews/Ratings Overall 5.0 / 5 features 4.0 / 5 Read all reviews	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
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.	Pros & Cons from Real Users Pros This is the kind of model I would use all day for normal engineering work. It is fast enough that it does not feel like I am waiting on the model, but it is still strong enough for feature work, code review, docs, automations, and debugging. The speed is probably the biggest win. Cognition says it runs at around 200 tokens per second versus about 60 for SWE-2, and that difference matters a lot once an agent starts iterating through files and tools. The cost-performance balance is also really good. It is one-fifth the price of SWE-2, while scoring 45.5 on FrontierCode 1.1 Main, 60.1 on DeepSWE 1.1, and 89.9 on SWE-Bench Pro v2 Hard. Cons My main complaint is that it is not the model I would choose for the hardest repo-wide work. SWE-2 still has a noticeable edge on some tougher benchmarks, so I would move up when the task really needs it.
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 Thinking Machines Lab Founded: 2025 United States thinkingmachines.ai/news/inkling-small/	Company Information Cognition Founded: 2023 United States cognition.com
Alternatives Muse Spark Open Meta	Alternatives Gemini 4 Carbon Google
GPT-5.6 Luna OpenAI	GPT-6.1 Terra OpenAI
Inkling Thinking Machines Lab	GPT-6.1 Sol OpenAI
SWE-2 Flash Cognition	Inkling Thinking Machines Lab
Beam Reflection View All	Nemotron 3.5 Super VL NVIDIA View All
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	Categories AI Coding Models Supported AI Models Supported

Integrations .NET Not Supported C Not Supported C++ Not Supported CSS Not Supported Cerebras Not Supported Dart Not Supported Devin Not Supported Go Not Supported JSON Not Supported JavaScript Not Supported Kubernetes Not Supported Lua Not Supported MATLAB Not Supported PHP Not Supported Python Not Supported Rust Not Supported Scala Not Supported Terraform Not Supported Tinker Supported TypeScript Not Supported Show More Integrations View All 2 Integrations	Integrations .NET Supported C Supported C++ Supported CSS Supported Cerebras Supported Dart Supported Devin Supported Go Supported JSON Supported JavaScript Supported Kubernetes Supported Lua Supported MATLAB Supported PHP Supported Python Supported Rust Supported Scala Supported Terraform Supported Tinker Not Supported TypeScript Supported Show More Integrations View All 32 Integrations
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