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Description

DeepSWE is an innovative and fully open-source coding agent that utilizes the Qwen3-32B foundation model, trained solely through reinforcement learning (RL) without any supervised fine-tuning or reliance on proprietary model distillation. Created with rLLM, which is Agentica’s open-source RL framework for language-based agents, DeepSWE operates as a functional agent within a simulated development environment facilitated by the R2E-Gym framework. This allows it to leverage a variety of tools, including a file editor, search capabilities, shell execution, and submission features, enabling the agent to efficiently navigate codebases, modify multiple files, compile code, run tests, and iteratively create patches or complete complex engineering tasks. Beyond simple code generation, DeepSWE showcases advanced emergent behaviors; when faced with bugs or new feature requests, it thoughtfully reasons through edge cases, searches for existing tests within the codebase, suggests patches, develops additional tests to prevent regressions, and adapts its cognitive approach based on the task at hand. This flexibility and capability make DeepSWE a powerful tool in the realm of software development.

Description

Qwen2.5-Coder-32B-Instruct has emerged as the leading open-source code model, effectively rivaling the coding prowess of GPT-4o. It not only exhibits robust and comprehensive programming skills but also demonstrates solid general and mathematical abilities. Currently, Qwen2.5-Coder encompasses six widely used model sizes tailored to the various needs of developers. We investigate the practicality of Qwen2.5-Coder across two different scenarios, such as code assistance and artifact generation, presenting examples that illustrate its potential use cases in practical applications. As the premier model in this open-source initiative, Qwen2.5-Coder-32B-Instruct has outperformed many other open-source models on several prominent code generation benchmarks, showcasing competitive capabilities alongside GPT-4o. Additionally, the skill of code repair is crucial for programmers, and Qwen2.5-Coder-32B-Instruct proves to be an invaluable tool for users aiming to troubleshoot and rectify coding errors, thereby streamlining the programming process and enhancing efficiency. This combination of functionalities positions Qwen2.5-Coder as an indispensable resource in the software development landscape.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Alibaba Cloud
BLACKBOX AI
C
CSS
Clojure
F#
Go
Hyperbolic
Java
JavaScript
Julia
Python
Qwen Studio
R
Ruby
Scala
Sesterce
Together AI
TypeScript
Visual Basic

Integrations

Alibaba Cloud
BLACKBOX AI
C
CSS
Clojure
F#
Go
Hyperbolic
Java
JavaScript
Julia
Python
Qwen Studio
R
Ruby
Scala
Sesterce
Together AI
TypeScript
Visual Basic

Pricing Details

Free
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Agentica Project

Founded

2025

Country

United States

Website

agentica-project.com

Vendor Details

Company Name

Alibaba

Founded

1999

Country

China

Website

qwenlm.github.io/blog/qwen2.5-coder-family/

Product Features

Product Features

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