LangMart: Qwen: Qwen3 30B A3B Instruct 2507
Model Overview
| Property | Value |
|---|---|
| Model ID | openrouter/qwen/qwen3-30b-a3b-instruct-2507 |
| Name | Qwen: Qwen3 30B A3B Instruct 2507 |
| Provider | qwen |
| Released | 2025-07-29 |
Description
Qwen3-30B-A3B-Instruct-2507 is a 30.5B-parameter mixture-of-experts language model from Qwen, with 3.3B active parameters per inference. It operates in non-thinking mode and is designed for high-quality instruction following, multilingual understanding, and agentic tool use. Post-trained on instruction data, it demonstrates competitive performance across reasoning (AIME, ZebraLogic), coding (MultiPL-E, LiveCodeBench), and alignment (IFEval, WritingBench) benchmarks. It outperforms its non-instruct variant on subjective and open-ended tasks while retaining strong factual and coding performance.
Description
LangMart: Qwen: Qwen3 30B A3B Instruct 2507 is a language model provided by qwen. This model offers advanced capabilities for natural language processing tasks.
Provider
qwen
Specifications
| Spec | Value |
|---|---|
| Context Window | 262,144 tokens |
| Modalities | text->text |
| Input Modalities | text |
| Output Modalities | text |
Pricing
| Type | Price |
|---|---|
| Input | $0.08 per 1M tokens |
| Output | $0.33 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
Qwen3-30B-A3B-Instruct-2507 is the July 2025 version-stable snapshot of the 30B MoE model, providing reproducible behavior for production deployments. Key characteristics: (1) Architecture: 30B total parameters with 3B activation, frozen at July 2025 capabilities; Qwen 3 MoE design with global-batch load balancing and no shared experts; (2) Performance: Maintains consistent July 2025 baseline performance, ensuring reproducible results across deployments and over time; strong general-purpose capabilities frozen at specific training checkpoint; (3) Use Cases: Production applications requiring version stability, reproducible A/B testing, compliance scenarios needing fixed model behavior, long-term deployments where model drift must be avoided, applications requiring audit trails with consistent model versions; (4) Context Window: 131K tokens; (5) Pricing: Standard MoE pricing based on activated parameters; (6) Trade-offs: Lacks future model improvements but provides critical version stability for enterprise deployments. Best for production systems where reproducible behavior across time is more important than accessing latest model improvements - essential for regulated industries, scientific reproducibility, and applications requiring version control.