Qwen3-235B-A22B-Thinking-2507
About Qwen3-235B-A22B-Thinking-2507
Qwen3-235B-A22B-Thinking-2507 is a member of the Qwen3 large language model series developed by Alibaba's Qwen team, specializing in highly complex reasoning tasks. The model is built on a Mixture-of-Experts (MoE) architecture, with 235 billion total parameters and approximately 22 billion activated parameters per token, which enhances computational efficiency while maintaining powerful performance. As a dedicated 'thinking' model, it demonstrates significantly improved performance on tasks requiring human expertise, such as logical reasoning, mathematics, science, coding, and academic benchmarks, achieving state-of-the-art results among open-source thinking models. Furthermore, the model features enhanced general capabilities like instruction following, tool usage, and text generation, and it natively supports a 256K long-context understanding capability, making it ideal for scenarios that require deep reasoning and processing of long documents
Explore how Qwen3-235B-A22B-Thinking-2507's unparalleled reasoning and long-context capabilities can solve highly complex, real-world problems.
Advanced Scientific Discovery & Proof Verification
Accelerate research by analyzing vast datasets, generating and verifying complex mathematical proofs, and synthesizing interdisciplinary findings with deep, step-by-step reasoning.
Use Case Example:
"Assisted a quantum physics team by deriving and validating a novel theoretical framework for particle interactions, significantly reducing the manual effort in proof construction and peer review."
Large-Scale Codebase Refactoring & Security Audits
Analyze entire enterprise codebases to identify architectural flaws, subtle logical vulnerabilities, and suggest advanced refactoring strategies for improved maintainability and security.
Use Case Example:
"Pinpointed a critical race condition in a Go microservices application by tracing complex asynchronous execution paths across multiple services, providing a precise, optimized fix."
Multi-Jurisdictional Regulatory Compliance
Automate the review of extensive legal and regulatory documents (up to 1M tokens), identifying cross-document inconsistencies, compliance gaps, and potential liabilities across multiple jurisdictions.
Use Case Example:
"Reviewed thousands of financial contracts and policy documents against new GDPR and CCPA regulations, flagging specific clauses requiring amendment and generating a detailed compliance report."
Complex Engineering System Optimization
Optimize intricate engineering designs, from hardware schematics to complex software architectures, by reasoning through logical dependencies, simulating performance, and proposing efficiency enhancements.
Use Case Example:
"Optimized the data flow and resource allocation for a large-scale IoT sensor network, identifying bottlenecks and suggesting a more robust, energy-efficient communication protocol for millions of devices."
Metadata
Specification
State
Deprecated
Architecture
Mixture of Experts
Calibrated
Yes
Mixture of Experts
Yes
Total Parameters
235B
Activated Parameters
22B
Reasoning
No
Precision
FP8
Context length
262K
Max Tokens
262K
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