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features
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support

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Write a Review

Description

DeepSeek-Coder-V2 is an open-source model tailored for excellence in programming and mathematical reasoning tasks. Utilizing a Mixture-of-Experts (MoE) architecture, it boasts a staggering 236 billion total parameters, with 21 billion of those being activated per token, which allows for efficient processing and outstanding performance. Trained on a massive dataset comprising 6 trillion tokens, this model enhances its prowess in generating code and tackling mathematical challenges. With the ability to support over 300 programming languages, DeepSeek-Coder-V2 has consistently outperformed its competitors on various benchmarks. It is offered in several variants, including DeepSeek-Coder-V2-Instruct, which is optimized for instruction-based tasks, and DeepSeek-Coder-V2-Base, which is effective for general text generation. Additionally, the lightweight options, such as DeepSeek-Coder-V2-Lite-Base and DeepSeek-Coder-V2-Lite-Instruct, cater to environments that require less computational power. These variations ensure that developers can select the most suitable model for their specific needs, making DeepSeek-Coder-V2 a versatile tool in the programming landscape.

Description

Qwen3-Coder is an advanced code model that comes in various sizes, prominently featuring the 480B-parameter Mixture-of-Experts version (with 35B active) that inherently accommodates 256K-token contexts, which can be extended to 1M, and demonstrates cutting-edge performance in Agentic Coding, Browser-Use, and Tool-Use activities, rivaling Claude Sonnet 4. With a pre-training phase utilizing 7.5 trillion tokens (70% of which are code) and synthetic data refined through Qwen2.5-Coder, it enhances both coding skills and general capabilities, while its post-training phase leverages extensive execution-driven reinforcement learning across 20,000 parallel environments to excel in multi-turn software engineering challenges like SWE-Bench Verified without the need for test-time scaling. Additionally, the open-source Qwen Code CLI, derived from Gemini Code, allows for the deployment of Qwen3-Coder in agentic workflows through tailored prompts and function calling protocols, facilitating smooth integration with platforms such as Node.js and OpenAI SDKs. This combination of robust features and flexible accessibility positions Qwen3-Coder as an essential tool for developers seeking to optimize their coding tasks and workflows.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

C# Yes 
C++ Yes 
Claude Opus 4.5 No 
Claude Opus 4.6 No 
Claude Opus 4.7 No 
Claude Sonnet 5.5 No 
Clojure Yes 
Java Yes 
JavaScript Yes 
Julia Yes 
Node.js No 
OpenSpec No 
Python Yes 
Qwen3.8-2.4T-A95B No 
Qwen3.8-27B No 
Qwen3.8-Max No 
Qwen3.8-Omni-Flash No 
Ruby Yes 
Scala Yes 
Step 5 Preview No 

Integrations

C# No 
C++ No 
Claude Opus 4.5 Yes 
Claude Opus 4.6 Yes 
Claude Opus 4.7 Yes 
Claude Sonnet 5.5 Yes 
Clojure No 
Java No 
JavaScript No 
Julia No 
Node.js Yes 
OpenSpec Yes 
Python No 
Qwen3.8-2.4T-A95B Yes 
Qwen3.8-27B Yes 
Qwen3.8-Max Yes 
Qwen3.8-Omni-Flash Yes 
Ruby No 
Scala No 
Step 5 Preview Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person No 

Vendor Details

Company Name

DeepSeek

Founded

2023

Country

China

Website

www.deepseek.com

Vendor Details

Company Name

Qwen

Founded

2023

Country

China

Website

github.com/QwenLM/qwen-code

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