LangMart: Qwen: Qwen3 32B
Model Overview
| Property | Value |
|---|---|
| Model ID | openrouter/qwen/qwen3-32b |
| Name | Qwen: Qwen3 32B |
| Provider | qwen |
| Released | 2025-04-28 |
Description
Qwen3-32B is a dense 32.8B parameter causal language model from the Qwen3 series, optimized for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for tasks like math, coding, and logical inference, and a "non-thinking" mode for faster, general-purpose conversation. The model demonstrates strong performance in instruction-following, agent tool use, creative writing, and multilingual tasks across 100+ languages and dialects. It natively handles 32K token contexts and can extend to 131K tokens using YaRN-based scaling.
Description
LangMart: Qwen: Qwen3 32B is a language model provided by qwen. This model offers advanced capabilities for natural language processing tasks.
Provider
qwen
Specifications
| Spec | Value |
|---|---|
| Context Window | 40,960 tokens |
| Modalities | text->text |
| Input Modalities | text |
| Output Modalities | text |
Pricing
| Type | Price |
|---|---|
| Input | $0.08 per 1M tokens |
| Output | $0.24 per 1M tokens |
Capabilities
- Frequency penalty
- Include reasoning
- Logprobs
- Max tokens
- Min p
- Presence penalty
- Reasoning
- Repetition penalty
- Response format
- Seed
- Stop
- Structured outputs
- Temperature
- Tool choice
- Tools
- Top k
- Top logprobs
- Top p
Detailed Analysis
Qwen3-32B is a large-scale dense model in the Qwen 3 series, offering flagship-level capabilities in traditional transformer architecture. Released April 2025. Key characteristics: (1) Architecture: 32B parameter dense transformer trained on 36T tokens; achieves performance surpassing Qwen2.5-72B Max model through architectural improvements, demonstrating remarkable efficiency gains from Qwen 3 innovations; (2) Performance: MMLU-Pro: 79.4 vs 76.1 for Qwen2.5-72B, showing architectural optimization exceeding brute-force parameter scaling; competitive with or exceeding GPT-4 on many reasoning and coding benchmarks; (3) Use Cases: Applications requiring maximum capability in dense architecture, complex multi-step reasoning, advanced code generation and architectural planning, sophisticated research applications, long-context document analysis, production deployments prioritizing quality over cost; (4) Context Window: 131K tokens supporting extensive document processing and long-form generation; (5) Pricing: Premium tier reflecting large-scale model, but more cost-effective per capability unit than previous generation 72B models; (6) Trade-offs: Highest capability in Qwen 3 dense lineup before moving to MoE models; higher cost than smaller models but exceptional quality. Best for applications requiring proven dense architecture with flagship capabilities, where MoE complexity is undesired or maximum per-token computation is required.