SWE-2

SWE-2

Cognition
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About

DeepSeek-V4-Flash is a high-efficiency Mixture-of-Experts (MoE) language model designed for fast, scalable reasoning and text generation. It features 284 billion total parameters with 13 billion activated parameters, delivering strong performance while optimizing computational cost. The model supports an extensive context window of up to one million tokens, enabling it to process large documents and complex workflows with ease. Its hybrid attention architecture enhances long-context efficiency by reducing memory and compute requirements. Trained on over 32 trillion tokens, DeepSeek-V4-Flash demonstrates solid capabilities across knowledge, reasoning, and coding tasks. It is designed for scenarios where speed and efficiency are critical, offering a balance between performance and resource usage. The model also supports multiple reasoning modes, allowing users to adjust between faster outputs and deeper analysis.

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, startups, and enterprises looking for a cost-efficient, scalable language model for fast inference, long-context processing, and real-world AI applications

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

$0.14 per 1M tokens (input)
DeepSeek V4 Flash API pricing per 1 million tokens is $0.14 for regular cache-miss inputs, $0.0028 for cache-hit inputs, and $0.28 for outputs.
Free Version
Free Trial

Pricing

$20/month
Free Version
Free Trial

Reviews/Ratings

Overall 5.0 / 5
features 4.0 / 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

  • DeepSeek-V4-Flash is really compelling because it feels built for developers who care about performance and cost at the same time. A 1M-token context window, open weights, and a low active-parameter MoE setup make it interesting for repo analysis, long-context coding, document-heavy agents, and high-volume automation.

Cons

  • I would still test it carefully before trusting it in production. Cheap inference is great, but coding agents need reliability, strong tool use, clean multi-file edits, good recovery from mistakes, and consistent behavior over long tasks.

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

DeepSeek
Founded: 2023
China
deepseek.com

Company Information

Cognition
Founded: 2023
United States
cognition.com

Alternatives

Alternatives

GPT-6 Astra

GPT-6 Astra

OpenAI
GPT-5.6 Sol

GPT-5.6 Sol

OpenAI
SWE-1.7

SWE-1.7

Cognition
DeepSeek-V4

DeepSeek-V4

DeepSeek
SWE-1.6

SWE-1.6

Cognition

Categories

Categories

Integrations

.NET
C
C#
Cerebras
Cheaper Inference
Dart
Devin
Go
JSON
JavaScript
Kotlin
Novita AI
OpenClaw
PowerShell
R
Rust
SnapVee Studio
TypeScript
Vercel AI Gateway
XML

Integrations

.NET
C
C#
Cerebras
Cheaper Inference
Dart
Devin
Go
JSON
JavaScript
Kotlin
Novita AI
OpenClaw
PowerShell
R
Rust
SnapVee Studio
TypeScript
Vercel AI Gateway
XML
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