LangMart: Qwen: Qwen3 30B A3B Thinking 2507
Model Overview
| Property | Value |
|---|---|
| Model ID | openrouter/qwen/qwen3-30b-a3b-thinking-2507 |
| Name | Qwen: Qwen3 30B A3B Thinking 2507 |
| Provider | qwen |
| Released | 2025-08-28 |
Description
Qwen3-30B-A3B-Thinking-2507 is a 30B parameter Mixture-of-Experts reasoning model optimized for complex tasks requiring extended multi-step thinking. The model is designed specifically for “thinking mode,” where internal reasoning traces are separated from final answers.
Compared to earlier Qwen3-30B releases, this version improves performance across logical reasoning, mathematics, science, coding, and multilingual benchmarks. It also demonstrates stronger instruction following, tool use, and alignment with human preferences. With higher reasoning efficiency and extended output budgets, it is best suited for advanced research, competitive problem solving, and agentic applications requiring structured long-context reasoning.
Description
LangMart: Qwen: Qwen3 30B A3B Thinking 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 | 32,768 tokens |
| Modalities | text->text |
| Input Modalities | text |
| Output Modalities | text |
Pricing
| Type | Price |
|---|---|
| Input | $0.05 per 1M tokens |
| Output | $0.34 per 1M tokens |
Capabilities
- Frequency penalty
- Include reasoning
- Max tokens
- Presence penalty
- Reasoning
- Repetition penalty
- Response format
- Seed
- Structured outputs
- Temperature
- Tool choice
- Tools
- Top k
- Top p
Detailed Analysis
Qwen3-30B-A3B-Thinking-2507 is the July 2025 version-stable MoE model with explicit reasoning mode, combining reproducibility with reasoning transparency. Key characteristics: (1) Architecture: 30B total/3B activated MoE model frozen at July 2025 with toggleable /think and /no_think tokens controlling reasoning output; demonstrates how expert activation contributes to reasoning; (2) Performance: Consistent July 2025 performance with reasoning steps improving accuracy on math, logic, and multi-step problems; version stability ensures reproducible reasoning patterns; (3) Use Cases: Applications requiring both reasoning transparency AND version stability, explainable AI systems needing audit trails, educational applications showing reasoning evolution, debugging production systems, regulated industries requiring fixed reasoning behavior; (4) Context Window: 131K tokens (reasoning consumes additional context); (5) Pricing: MoE efficiency plus reasoning token costs; (6) Trade-offs: Version-frozen means no improvements, but critical for applications requiring reproducible explainable reasoning. Best for production explainable AI systems where both the reasoning process AND the model version must be fixed and auditable - unique combination of MoE efficiency, reasoning transparency, and version stability.