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Description

Cursor has introduced Composer 2.5, a next-generation AI coding assistant built to deliver stronger reasoning, better collaboration, and improved reliability during software development tasks. The upgraded model performs better on long-running coding workflows and can manage complicated instructions with greater consistency than earlier Composer versions. Cursor expanded the training process by scaling compute resources, generating more advanced reinforcement learning environments, and refining behavioral traits that improve the developer experience. One of the key innovations in Composer 2.5 is its targeted textual feedback system, which helps the model learn from localized mistakes inside long coding trajectories instead of relying only on broad reward signals. This training method allows the AI to improve coding style, communication quality, and tool usage accuracy in a more focused way. The company also increased the amount of synthetic coding data by 25 times compared to Composer 2, giving the model exposure to more difficult and realistic programming tasks. During development, the system demonstrated sophisticated reasoning abilities by uncovering hidden implementation details and reverse-engineering deleted functionality inside synthetic environments. Composer 2.5 additionally uses advanced distributed training methods such as Sharded Muon and dual mesh HSDP to optimize large-scale model training performance. Available directly inside Cursor, the model comes in both standard and fast variants with different pricing tiers designed for developers, teams, and enterprise-scale engineering workflows.

Description

Qwen3.8-Flash-Next represents an open-weight multimodal Mixture-of-Experts architecture and serves as an initial glimpse into the design intended for Qwen4. This model strategically enhances attention mechanisms, residual pathways, embeddings, and optimization techniques to boost its capabilities, improve computational efficiency, expand model capacity, and ensure training stability. Its innovative hybrid architecture merges Gated DeltaNet, which adeptly compresses past information, with Qwen Sparse Attention, enabling the selection of significant context at a micro-block level to lessen both attention and indexing costs associated with lengthy sequences. The Gated Residual feature broadens the residual pathway into four streams, dynamically managing the flow of information across different layers. Additionally, the N-gram Embedding integrates large-scale local-pattern memory with minimal added computation per token, and it can be transferred to host memory for further efficiency. The model is structured around a 125B-parameter main network supplemented by 51B parameters dedicated to N-gram embeddings, activating only 6B parameters for each token processed. This sophisticated framework highlights the ongoing advancements in machine learning architectures, setting a promising stage for future developments.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Python Yes 
.NET Yes 
C Yes 
Cline No 
Cursor Yes 
Go Yes 
Grok Build Yes 
HTML Yes 
Hugging Face No 
Kotlin Yes 
Kubernetes Yes 
Model Context Protocol (MCP) No 
Odysseus No 
OfoxAI No 
Ollama No 
PHP Yes 
Qwen Code No 
Qwen Studio No 
Rust Yes 
Swift Yes 

Integrations

Python Yes 
.NET No 
C No 
Cline Yes 
Cursor No 
Go No 
Grok Build No 
HTML No 
Hugging Face Yes 
Kotlin No 
Kubernetes No 
Model Context Protocol (MCP) Yes 
Odysseus Yes 
OfoxAI Yes 
Ollama Yes 
PHP No 
Qwen Code Yes 
Qwen Studio Yes 
Rust No 
Swift No 

Pricing Details

$0.50/M input
$0.50/M input and $2.50/M output tokens
Free Trial No 
Free Version Yes 

Pricing Details

$2 per 1M (input)
Free Trial No 
Free Version No 

Deployment

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

Deployment

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

Customer Support

Business Hours No 
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) No 
In Person No 

Types of Training

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

Vendor Details

Company Name

Cursor

Founded

2022

Country

United States

Website

cursor.com

Vendor Details

Company Name

Alibaba

Founded

1999

Country

China

Website

qwen.ai/blog

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