LangMart: Qwen: Qwen3 Coder 30B A3B Instruct
Model Overview
| Property | Value |
|---|---|
| Model ID | openrouter/qwen/qwen3-coder-30b-a3b-instruct |
| Name | Qwen: Qwen3 Coder 30B A3B Instruct |
| Provider | qwen |
| Released | 2025-07-31 |
Description
Qwen3-Coder-30B-A3B-Instruct is a 30.5B parameter Mixture-of-Experts (MoE) model with 128 experts (8 active per forward pass), designed for advanced code generation, repository-scale understanding, and agentic tool use. Built on the Qwen3 architecture, it supports a native context length of 256K tokens (extendable to 1M with Yarn) and performs strongly in tasks involving function calls, browser use, and structured code completion.
This model is optimized for instruction-following without “thinking mode”, and integrates well with OpenAI-compatible tool-use formats.
Description
LangMart: Qwen: Qwen3 Coder 30B A3B Instruct is a language model provided by qwen. This model offers advanced capabilities for natural language processing tasks.
Provider
qwen
Specifications
| Spec | Value |
|---|---|
| Context Window | 160,000 tokens |
| Modalities | text->text |
| Input Modalities | text |
| Output Modalities | text |
Pricing
| Type | Price |
|---|---|
| Input | $0.07 per 1M tokens |
| Output | $0.27 per 1M tokens |
Capabilities
- Frequency penalty
- Max tokens
- Presence penalty
- Repetition penalty
- Response format
- Seed
- Stop
- Structured outputs
- Temperature
- Tool choice
- Tools
- Top k
- Top p
Detailed Analysis
Qwen3-Coder-30B-A3B-Instruct is a Mixture-of-Experts specialized coding model, combining large-scale code capabilities with efficient sparse activation. Released May 2025. Key characteristics: (1) Architecture: 30B total parameters with ~3B activated per forward pass (A3B), achieving ~90% compute reduction versus dense 30B while maintaining coding quality; experts specialized for different programming paradigms, languages, and coding tasks; (2) Performance: Approaches dense 30B coding model quality through efficient expert activation; excels at diverse coding tasks by routing to specialized experts for language-specific patterns, algorithms, and best practices; (3) Language Support: Comprehensive 40+ programming language support with expert specialization enabling efficient handling of language-specific features; (4) Use Cases: Cost-sensitive production coding services, high-throughput code generation, multi-language development environments, cloud-based coding assistants, scalable code review systems, automated code generation pipelines; (5) Context Window: 131K tokens for repository-level understanding; (6) Trade-offs: MoE architecture provides coding capability at fraction of compute cost versus dense models. Best for production coding services requiring large model capabilities while optimizing inference costs - demonstrates that sparse activation can deliver professional-grade coding assistance efficiently.