LangMart: Qwen: Qwen3 235B A22B Instruct 2507

Model Overview

Property	Value
Model ID	`openrouter/qwen/qwen3-235b-a22b-2507`
Name	Qwen: Qwen3 235B A22B Instruct 2507
Provider	qwen
Released	2025-07-21

Description

Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. It is optimized for general-purpose text generation, including instruction following, logical reasoning, math, code, and tool usage. The model supports a native 262K context length and does not implement "thinking mode" ( blocks).

Compared to its base variant, this version delivers significant gains in knowledge coverage, long-context reasoning, coding benchmarks, and alignment with open-ended tasks. It is particularly strong on multilingual understanding, math reasoning (e.g., AIME, HMMT), and alignment evaluations like Arena-Hard and WritingBench.

Description

LangMart: Qwen: Qwen3 235B A22B 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	$0.07 per 1M tokens
Output	$0.46 per 1M tokens

Capabilities

Frequency penalty
Include reasoning
Logit bias
Logprobs
Max tokens
Min p
Presence penalty
Reasoning
Reasoning effort
Repetition penalty
Response format
Seed
Stop
Structured outputs
Temperature
Tool choice
Tools
Top k
Top logprobs
Top p

Detailed Analysis

Qwen3-235B-A22B-2507 is the July 2025 version-stable snapshot of the flagship 235B MoE model, providing frontier capabilities with reproducible behavior. Key characteristics: (1) Architecture: Full 235B total/22B activated MoE model frozen at July 2025 capabilities; maintains all frontier features at specific training checkpoint; (2) Performance: Consistent July 2025 frontier performance matching GPT-4/Claude 3 Opus levels with version stability ensuring reproducible results across time and deployments; (3) Use Cases: Enterprise production requiring maximum capability with version control, research requiring reproducible frontier results, compliance scenarios needing fixed high-capability model versions, long-term deployments of advanced AI systems, applications requiring audit trails at frontier capability level; (4) Context Window: 131K tokens; (5) Pricing: MoE efficiency (22B activated) providing frontier capability at optimized cost; (6) Trade-offs: Frozen at July 2025 means no future improvements, but provides critical version stability for enterprise frontier AI deployments. Best for production systems requiring both maximum language model capability AND reproducible version-controlled behavior - essential for regulated industries, scientific research, and enterprise applications where frontier AI must be auditable and consistent.