LangMart: Qwen: Qwen3 235B A22B Thinking 2507
Model Overview
| Property | Value |
|---|---|
| Model ID | openrouter/qwen/qwen3-235b-a22b-thinking-2507 |
| Name | Qwen: Qwen3 235B A22B Thinking 2507 |
| Provider | qwen |
| Released | 2025-07-25 |
Description
Qwen3-235B-A22B-Thinking-2507 is a high-performance, open-weight Mixture-of-Experts (MoE) language model optimized for complex reasoning tasks. It activates 22B of its 235B parameters per forward pass and natively supports up to 262,144 tokens of context. This "thinking-only" variant enhances structured logical reasoning, mathematics, science, and long-form generation, showing strong benchmark performance across AIME, SuperGPQA, LiveCodeBench, and MMLU-Redux. It enforces a special reasoning mode () and is designed for high-token outputs (up to 81,920 tokens) in challenging domains.
The model is instruction-tuned and excels at step-by-step reasoning, tool use, agentic workflows, and multilingual tasks. This release represents the most capable open-source variant in the Qwen3-235B series, surpassing many closed models in structured reasoning use cases.
Description
LangMart: Qwen: Qwen3 235B A22B 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 | 262,144 tokens |
| Modalities | text->text |
| Input Modalities | text |
| Output Modalities | text |
Pricing
| Type | Price |
|---|---|
| Input | $0.11 per 1M tokens |
| Output | $0.60 per 1M tokens |
Capabilities
- Frequency penalty
- Include reasoning
- Logit bias
- Max tokens
- Min p
- Presence penalty
- Reasoning
- Repetition penalty
- Response format
- Seed
- Stop
- Structured outputs
- Temperature
- Tool choice
- Tools
- Top k
- Top p
Detailed Analysis
Qwen3-235B-A22B-Thinking-2507 is the July 2025 version-stable flagship MoE model with explicit reasoning, combining frontier capability, MoE efficiency, reasoning transparency, and version stability. Key characteristics: (1) Architecture: Full 235B total/22B activated MoE model frozen at July 2025 with toggleable reasoning via /think and /no_think tokens; demonstrates how frontier-scale expert activation produces complex reasoning; (2) Performance: Frontier-level reasoning at July 2025 baseline with version stability ensuring reproducible reasoning patterns; shows step-by-step thought process for complex problems at GPT-4/Claude 3 Opus level; (3) Use Cases: High-stakes explainable AI requiring maximum capability, regulated industries needing frontier reasoning with audit trails, research requiring reproducible frontier reasoning, debugging advanced AI systems, applications where both capability and reasoning transparency must be version-controlled; (4) Context Window: 131K tokens (reasoning consumes additional); (5) Pricing: Combines MoE efficiency with reasoning token costs; (6) Trade-offs: Frozen version means no improvements, but provides unique combination of frontier capability, reasoning transparency, MoE efficiency, and version control. Best for mission-critical explainable AI systems requiring maximum capability with fixed, auditable reasoning behavior - the pinnacle of reproducible explainable frontier AI.