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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

Description

Professional prompt editors utilizing models like GPT-4o, Claude 3 Opus, and Gemini-1.5, along with function call simulation capabilities, enable the creation of diverse projects tailored for various use cases with distinct project members and configurations. Each member can have different access control levels, promoting collaborative prompting and sharing. Users can incorporate multiple image inputs in their messages while having control over individual detail parameters, facilitating easy manipulation of each message. The function call schema editor allows for simulation of function call returns seamlessly, and inline variables in prompts enable the running and comparison of results across different variable groups simultaneously. All sensitive information is secured through RSA-OAEP and AES-256-GCM encryption during both transmission and storage, ensuring privacy and data integrity. With Knit, no edits are ever lost, as all edit history is meticulously saved and can be restored at any moment. The platform is compatible with various models, including OpenAI, Claude, and Azure OpenAI, with plans to expand support for even more models. Almost all API parameters can be adjusted within the prompt editors, allowing users to optimize their prompts effectively and discover the most suitable parameters for their needs. This comprehensive approach ensures a streamlined experience for prompt editing and model interaction, fostering creativity and collaboration across teams.

Description

Qwen3-Coder is a versatile coding model that comes in various sizes, prominently featuring the 480B-parameter Mixture-of-Experts version with 35B active parameters, which naturally accommodates 256K-token contexts that can be extended to 1M tokens. This model achieves impressive performance that rivals Claude Sonnet 4, having undergone pre-training on 7.5 trillion tokens, with 70% of that being code, and utilizing synthetic data refined through Qwen2.5-Coder to enhance both coding skills and overall capabilities. Furthermore, the model benefits from post-training techniques that leverage extensive, execution-guided reinforcement learning, which facilitates the generation of diverse test cases across 20,000 parallel environments, thereby excelling in multi-turn software engineering tasks such as SWE-Bench Verified without needing test-time scaling. In addition to the model itself, the open-source Qwen Code CLI, derived from Gemini Code, empowers users to deploy Qwen3-Coder in dynamic workflows with tailored prompts and function calling protocols, while also offering smooth integration with Node.js, OpenAI SDKs, and environment variables. This comprehensive ecosystem supports developers in optimizing their coding projects effectively and efficiently.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Gemini
Gemini Enterprise
OpenAI
Azure OpenAI Service
Brokk
GPT-4
Gemini 1.5 Flash
Gemini 1.5 Pro
Gemini 2.0
Gemini 2.0 Flash
Gemini Advanced
Node.js
Okara
OpenClaw
OpenCode
Qwen2.5
Shiori
SiliconFlow
Tinfoil
Together AI

Integrations

Gemini
Gemini Enterprise
OpenAI
Azure OpenAI Service
Brokk
GPT-4
Gemini 1.5 Flash
Gemini 1.5 Pro
Gemini 2.0
Gemini 2.0 Flash
Gemini Advanced
Node.js
Okara
OpenClaw
OpenCode
Qwen2.5
Shiori
SiliconFlow
Tinfoil
Together AI

Pricing Details

$7 per month
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

PromptKnit

Website

promptknit.com

Vendor Details

Company Name

Qwen

Founded

2023

Country

China

Website

qwenlm.github.io/blog/qwen3-coder/

Product Features

Product Features

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