Gemma 4

Gemma 4

Google
SWE-2

SWE-2

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 is Cognition’s advanced coding model designed to improve software engineering performance while reducing the cost of agentic coding workflows. The model is post-trained from Kimi K3 and uses reinforcement learning to optimize multiple reasoning-effort levels within a single training run. SWE-2 is designed to explore codebases more selectively, begin implementation sooner, and complete tasks with fewer redundant reads and reasoning steps than earlier Cognition models. Its capabilities include code generation, debugging, test creation, verification, repository analysis, and complex terminal-based software engineering tasks. The model also emphasizes stronger engineering judgment, end-to-end test coverage, instruction following, and evidence-based verification of user assumptions. SWE-2 is available through Devin Desktop and Devin CLI, with broader rollout planned across Devin Web and Fusion.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

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, engineering teams, AI coding agent users, DevOps professionals, and organizations that need capable agentic software engineering with lower execution cost and more efficient reasoning

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

No images available

Screenshots and Videos

Pricing

Free
Open source
Free Version
Free Trial

Pricing

$20/month
Free Version
Free Trial

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5
design 5.0 / 5
support 5.0 / 5

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5
design 5.0 / 5

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

  • The biggest thing that stands out is the cost-performance balance. SWE-2 is not just trying to top one benchmark; it is trying to get very close to frontier coding performance at a much lower cost. For developers, that matters a lot. Coding agents can burn through tokens quickly when they are reading files, making edits, running tests, and iterating. A model that performs near the top while being meaningfully cheaper is much easier to use every day. I also like that SWE-2 seems built for real software engineering workflows, not just isolated code snippets. The strong DeepSWE and Terminal-Bench results make it especially interesting for repo-level tasks, debugging, tool use, and longer agent runs.

Cons

  • Benchmarks are useful, but real projects bring messy architecture, flaky tests, undocumented behavior, and weird edge cases.

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Google
Founded: 1998
United States
deepmind.google/models/gemma/

Company Information

Cognition
Founded: 2023
United States
cognition.com

Alternatives

GPT-5.6 Sol

GPT-5.6 Sol

OpenAI

Alternatives

GPT-6 Astra

GPT-6 Astra

OpenAI
Grok 4.6

Grok 4.6

SpaceXAI
GPT-5.6 Sol

GPT-5.6 Sol

OpenAI
Gemma 3

Gemma 3

Google
SWE-1.7

SWE-1.7

Cognition
Gemma

Gemma

Google
SWE-1.6

SWE-1.6

Cognition

Categories

Categories

Integrations

C
C#
C++
CSS
HTML
JavaScript
Kotlin
Python
R
Ruby
Rust
SQL
Scala
TypeScript
Clojure
Gemini
Julia
Lua
MedGemma
Terraform

Integrations

C
C#
C++
CSS
HTML
JavaScript
Kotlin
Python
R
Ruby
Rust
SQL
Scala
TypeScript
Clojure
Gemini
Julia
Lua
MedGemma
Terraform
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