Qwen3.8-Max

Qwen3.8-Max

Alibaba
SWE-2

SWE-2

Cognition
+
+

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About

Qwen3.8-Max is Qwen’s most capable model to date, built as a Max-class AI model for coding, work, research, long-horizon tasks, and multimodal agents. It scales to 2.4 trillion parameters with 95 billion active parameters and is available through QwenCloud. The model is designed to complete complex, open-ended tasks end to end with greater reliability and minimal human involvement. Qwen3.8-Max supports autonomous coding workflows, agentic development, research reproduction, visual reasoning, document understanding, video analysis, and real-world productivity tasks. It can integrate with popular agent frameworks and coding assistants, including Claude Code, Codex, Qoder CLI, Qwen Code, and OpenClaw. Built for developers, researchers, enterprises, and AI agent builders, Qwen3.8-Max helps teams automate sophisticated work across code, documents, tools, interfaces, and multimodal content.

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 and AI teams that need scalable access to text, image, video, audio, and multimodal AI models for building 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

$2 per 1M (input)
$2 per 1 million tokens (input), $6 per 1 million tokens (output)
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

  • Qwen3.8-Max is really interesting from a developer’s perspective. A 2.4T-parameter model that can handle text, images, video, and documents has a lot of potential for coding agents, repo analysis, multimodal debugging, and workflows where the model needs to understand more than just plain text.

Cons

  • The biggest downside is that a lot of the hype still needs proof. Alibaba is claiming Qwen3.8-Max trails only Fable 5, but current reports say there are no public benchmark scores, no model card, no activated-parameter count, and no independent leaderboard results yet.

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

Alibaba
Founded: 1999
China
qwen.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
SWE-1.7

SWE-1.7

Cognition
Qwen3.5

Qwen3.5

Alibaba
SWE-1.6

SWE-1.6

Cognition

Categories

Categories

Integrations

Python
.NET
Alibaba Cloud Model Studio
C#
C++
CSS
Cerebras
Dart
Devin Desktop
Go
Hermes Agent
ModelScope
Odysseus
OfoxAI
OpenClaw
PHP
Qwen Studio
QwenWork
XML
YAML

Integrations

Python
.NET
Alibaba Cloud Model Studio
C#
C++
CSS
Cerebras
Dart
Devin Desktop
Go
Hermes Agent
ModelScope
Odysseus
OfoxAI
OpenClaw
PHP
Qwen Studio
QwenWork
XML
YAML
Claim Qwen3.8-Max and update features and information
Claim Qwen3.8-Max and update features and information
Claim SWE-2 and update features and information
Claim SWE-2 and update features and information