Qwen3-VL-235B-A22B-Thinking
About Qwen3-VL-235B-A22B-Thinking
Qwen3-VL-235B-A22B-Thinking is one of the Qwen3-VL series models, a reasoning-enhanced Thinking edition that achieves state-of-the-art (SOTA) results across many multimodal reasoning benchmarks, excelling in STEM, math, causal analysis, and logical, evidence-based answers. It features a Mixture-of-Experts (MoE) architecture with 235B total parameters and 22B active parameters.
Explore how Qwen3-VL-235B-A22B-Thinking's advanced multimodal reasoning solves complex, real-world problems by integrating visual and textual data.
Advanced Scientific Discovery
Accelerate research by analyzing complex visual and textual data, generating proofs, and drafting papers with robust, step-by-step reasoning.
Use Case Example:
"Assisted a materials scientist by analyzing microscopy videos and experimental data to identify a novel crystal growth mechanism, formulating a predictive model in Python."
Visual Code Generation & Debug
Generate code from UI designs or debug complex systems by analyzing execution flows and visual outputs, identifying subtle errors and suggesting optimizations.
Use Case Example:
"Generated a functional React component from a Figma design screenshot, then debugged a performance bottleneck in a Go microservice by analyzing its distributed tracing logs and visual dashboards."
Multimodal Financial Insights
Perform deep quantitative and qualitative analysis on diverse financial documents, market charts, and news feeds to infer causal links and provide strategic recommendations.
Use Case Example:
"Analyzed a company's annual report (PDF), stock price charts, and recent news articles to predict market sentiment and recommend a portfolio adjustment, detailing the reasoning."
Intelligent System Auditing
Audit complex systems like engineering schematics, legal documents, or UI flows by reasoning through logical dependencies, identifying inconsistencies, and flagging potential issues.
Use Case Example:
"Audited a complex industrial control system's schematics (images) and operational logs (text) to identify a potential safety vulnerability, then simulated a mitigation strategy using its visual interface."
Metadata
Specification
State
Deprecated
Architecture
Mixture of Experts
Calibrated
No
Mixture of Experts
Yes
Total Parameters
235B
Activated Parameters
22B
Reasoning
No
Precision
FP8
Context length
262K
Max Tokens
262K
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