This website uses cookies to anonymously analyze website traffic using Google Analytics.
Company

Qwen3-Coder: The Most Capable Agentic Coding Model Now Available on Together AI

July 25, 2025

By 

Together AI

Code Smarter with Qwen3-Coder on Together AI's frontier AI cloud

Starting today on Together AI, you can access Qwen3-Coder-480B-A35B-Instruct from the Qwen herd — the most capable agentic coding model available. Unlike traditional coding assistants that excel at individual functions but struggle with complex workflows, Qwen3-Coder delivers frontier-level performance on the messy, interconnected work that defines real software engineering.

Summary

  • Most capable agentic coding model: 480B parameters with 256K context natively (1M with extrapolation)
  • Frontier performance: State-of-the-art SWE-bench Verified results, comparable to Claude Sonnet 4
  • Production-ready deployment: Together AI's optimized infrastructure makes massive models instantly accessible
  • Real engineering workflows: Handles entire codebases, not just isolated code snippets

Performance That Actually Matters

📊 Benchmark 🤖 Qwen3-Coder 🏛️ Claude Sonnet 4 📈 Other Open Models
🔧 SWE-bench Verified 69.6% 70.4% ~40-50%
🎯 Agentic Coding 37.5 39.0 ~25-30
🌐 Agentic Browser Use 49.9 47.4 ~35-40
🛠️ Agentic Tool Use 68.7 65.2 ~45-55
🚀 Qwen3-Coder achieves frontier-level performance on complex autonomous workflows.

These aren't toy benchmarks — they represent the messy, interconnected engineering work that traditional coding models can't handle. Together AI's continuous optimizations mean these capabilities improve over time without requiring any migration work on your end.

Why This Changes Everything for Development Teams

Most coding models hit the same wall when faced with real engineering work. They can write clean functions in isolation, but ask them to refactor a legacy system or implement a feature spanning multiple services, and they fall apart.

The breakthrough: Qwen3-Coder can hold your entire codebase in working memory while autonomously executing complex engineering workflows. Need to modernize authentication across a microservices architecture? It understands the database schema, API contracts, frontend implications, test requirements, and deployment considerations — all simultaneously.

What makes this possible on Together AI is our infrastructure built ground-up for AI workloads, not retrofitted from general cloud services. This architectural advantage means deploying a 480B parameter model becomes as simple as calling a standard API.

Massive Scale

480B total
35B active parameters
MoE efficiency

🧠

Advanced Training

7.5T tokens
70% code ratio
Complex RL workflows

🚀

Production Ready

Zero setup
Instant deployment
4x faster inference

Real Engineering Applications

Qwen3-Coder excels at the complex tasks that define modern software development:

🔄 Legacy System Modernization

Comprehensive analysis, security vulnerability identification, migration planning, and implementation across multiple services while maintaining backward compatibility. Perfect for OAuth migrations, framework upgrades, and architectural refactoring.

⚙️ Cross-System Feature Development

End-to-end implementation spanning backend APIs, frontend components, database changes, and deployment pipelines with proper error handling. Handles rate limiting, payment integrations, and multi-tenant features that touch every part of your stack.

🔍 Complex Debugging & Root Cause Analysis

Distributed system issue investigation, understanding failure propagation, and implementing systematic fixes that address underlying problems. Traces issues across microservices, identifies performance bottlenecks, and suggests architectural improvements.

Deploy on Together AI's Optimized Infrastructure

Deploying a 480-billion parameter model for production development workflows presents real challenges. Most cloud providers force impossible tradeoffs between performance, reliability, and cost. Together AI's infrastructure eliminates these compromises entirely.

🚀

Performance

Research-driven optimizations
Custom kernels & scaling

Reliability

99.9% uptime SLA
Multi-region deployment

🔒

Security

SOC 2 compliant
North American infrastructure

Our platform delivers native AI performance through custom optimizations specifically designed for large language models. Automatic scaling handles unpredictable AI traffic patterns without throttling, while continuous infrastructure improvements benefit all users automatically — no migration required.

Getting Started

Deploy Qwen3-Coder immediately through Together AI's production APIs:

Use our Python SDK to quickly integrate Qwen3-Coder into your applications:

    
        from together import Together

        client = Together()

        response = client.chat.completions.create(
            model="Qwen/Qwen3-Coder-480B-A35B-Instruct",
            messages=[],
            stream=True
        )
        for token in response:
            if hasattr(token, 'choices'):
                print(token.choices[0].delta.content, end='', flush=True)
    

Start building today:

  • Interactive Playground — Test complex workflows before production
  • API Documentation — Integration guides and examples
  • Batch API — Cost-effective processing for large refactoring tasks
  • Fine-tuning access — Customize for your specific engineering practices
  • Lower
    Cost
    20%
  • faster
    training
    4x
  • network
    compression
    117x

Q: Should I use the RedPajama-V2 Dataset out of the box?

RedPajama-V2 is conceptualized as a pool of data that serves as a foundation for creating high quality datasets. The dataset is thus not intended to be used out of the box and, depending on the application, data should be filtered out using the quality signals that accompany the data. With this dataset, we take the view that the optimal filtering of data is dependent on the intended use. Our goal is to provide all the signals and tooling that enables this.

Try Qwen3-Coder

Contact us to discuss enterprise deployments, custom integrations, or volume pricing for Qwen3-Coder

No items found.
Start
building
yours
here →