SWE-2

SWE-2

Cognition
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About

GLM-5.3-Flash is Z.ai’s natively multimodal model in the GLM-5 series (previously previewed as Ox Alpha), designed to deliver strong coding, agentic, visual, and knowledge-work performance at relatively low inference cost. It uses 320 billion total parameters with 18 billion active parameters, along with a hybrid architecture that combines sparse and linear attention to reduce the cost of long-context processing. The model supports context lengths of up to one million tokens and was trained on a 30-trillion-token multimodal corpus. GLM-5.3-Flash can reason across text, images, documents, interfaces, dashboards, and other visual information while using that feedback to refine its own outputs. Z.ai reports substantial gains over GLM-5.2 on coding and agentic benchmarks, including DeepSWE and AutomationBench, while approaching higher-cost frontier models on several evaluations.

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 engineers, agent builders, researchers, and organizations that need cost-efficient multimodal reasoning, long-context processing, advanced coding, visual analysis, and autonomous workflow capabilities

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

Screenshots and Videos

Pricing

$0.15 per 1M tokens (input)
Input: $0.15 per 1M tokens
Output: $0.50 per 1M tokens
Cached input: $0.03 per 1M tokens
Free Version
Free Trial

Pricing

$20/month
Free Version
Free Trial

Reviews/Ratings

Overall 5.0 / 5
ease 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

  • What makes it exciting is that it seems built for the exact workloads developers care about right now: long-horizon coding, complex reasoning, big-context analysis, and agentic workflows. A million-token context window is especially useful if you want to drop in a large repo, long spec, research corpus, or messy project history and have the model reason across it.

Cons

  • I would treat it as something exciting to test, not something to blindly trust with sensitive work. Even the independent Ox Alpha site warns that messages are processed by the upstream model API, so I would keep secrets, private code, and customer data out of it until there is a clearer owner, model card, privacy policy, and production story.

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

Z.ai
Founded: 2019
China
z.ai

Company Information

Cognition
Founded: 2023
United States
cognition.com

Alternatives

Alternatives

GPT-5.6 Sol

GPT-5.6 Sol

OpenAI
GPT-5.6 Sol

GPT-5.6 Sol

OpenAI
MiniMax M3

MiniMax M3

MiniMax
SWE-1.7

SWE-1.7

Cognition
Qwen3.5

Qwen3.5

Alibaba
SWE-1.6

SWE-1.6

Cognition

Categories

Categories

Integrations

.NET
C++
Cerebras
DeepSeek Harness
Devin
Devin Desktop
GLM Coding Plan
Go
Kotlin
MATLAB
Objective-C
OpenCode Go
OpenRouter
PowerShell
R
Solidity
Swift
Terraform
YAML
omp

Integrations

.NET
C++
Cerebras
DeepSeek Harness
Devin
Devin Desktop
GLM Coding Plan
Go
Kotlin
MATLAB
Objective-C
OpenCode Go
OpenRouter
PowerShell
R
Solidity
Swift
Terraform
YAML
omp
Claim GLM-5.3-Flash and update features and information
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