Gemma 4 vs. SWE-2 Flash Comparison


Gemma 4 Google	SWE-2 Flash Cognition	+	+
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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 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 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 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 Not Supported	Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported
API Offers API Not Supported	API Offers API Not 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 $20/month Free Version 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 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 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.	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 Google Founded: 1998 United States deepmind.google/models/gemma/	Company Information Cognition Founded: 2023 United States cognition.com
Alternatives GPT-6.1 Terra OpenAI	Alternatives Gemini 4 Carbon Google
GPT-6.1 Sol OpenAI	GPT-6.1 Terra OpenAI
GPT-5.6 Sol OpenAI	GPT-6.1 Sol OpenAI
Gemma 3 Google	Inkling Thinking Machines Lab
Gemma Google View All	Nemotron 3.5 Super VL NVIDIA 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

Integrations C Supported C# Supported C++ Supported CSS Supported HTML Supported JavaScript Supported Kotlin Supported Python Supported R Supported Ruby Supported Rust Supported SQL Supported Scala Supported TypeScript Supported Devin Not Supported Locally AI Supported Ollama Supported Swift Not Supported Visual Basic Supported kluster.ai Supported Show More Integrations View All 49 Integrations	Integrations C Supported C# Supported C++ Supported CSS Supported HTML Supported JavaScript Supported Kotlin Supported Python Supported R Supported Ruby Supported Rust Supported SQL Supported Scala Supported TypeScript Supported Devin Supported Locally AI Not Supported Ollama Not Supported Swift Supported Visual Basic Not Supported kluster.ai Not Supported Show More Integrations View All 32 Integrations
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