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GPT-5.2 vs Qwen3 VL 235B A22B Instruct

Compare GPT-5.2 and Qwen3 VL 235B A22B Instruct side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.

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GPT-5.2 vs Qwen3 VL 235B A22B Instruct Comparison Table

Evals updated July 10, 2026Pricing updated July 21, 2026

PropertyGPT-5.2Qwen3 VL 235B A22B Instruct
OrganizationOpenAIQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateDec 2025Sep 2025
Context Window400K256K
Parameters235B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$1.75$0.210
Output $/1M$14.00$1.90
Vision Tasks
CaptioningDemoDemo
Object DetectionDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
ClassificationDemo
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalsground-truth scores across 6 vision tasks
OverallNot evaluated
66.4%
Object Detection
42.3%
Counting
47.3%
Identification
90.6%
OCR
88.1%
Data Extraction
86.6%
Reasoning
43.5%
Avg cost / sample$0.0007
Avg speed / sample8.2s

GPT-5.2 vs Qwen3 VL 235B A22B Instruct: Overview

GPT-5.2

GPT-5.2 is OpenAI’s latest flagship large language model, released in December 2025. It is a proprietary, multimodal system supporting text and vision inputs, along with tool use, and features a 400,000-token context window designed for working with long documents, extended conversations, and complex workflows.

Relative to GPT-5.1, GPT-5.2 is positioned by OpenAI as offering improved long-context reasoning, more capable tool use, and stronger performance on professional tasks such as writing, coding, spreadsheet work, and image interpretation. The model is available in multiple variants (including Instant, Thinking, and Pro) that balance speed, cost, and depth of reasoning, making GPT-5.2 a general-purpose model aimed at reliability and workflow robustness rather than minimal latency or lowest cost.

Qwen3 VL 235B A22B Instruct

Qwen3 VL 235B A22B Instruct is a flagship multimodal vision-language model developed by Qwen (Alibaba Cloud), designed for instruction-following tasks that combine advanced text generation with visual understanding. It serves as a high-end open-weight model for developers and researchers building multimodal AI systems that require strong reasoning, perception, and long-context capabilities.

The model supports interleaved text and image inputs, very long context windows (up to roughly 256K tokens), and efficient inference through a mixture-of-experts architecture with about 22B active parameters out of 235B total. In today’s landscape, it competes with top-tier proprietary vision-language models while offering the advantages of open weights and flexible deployment. Typical applications include multimodal assistants, document and image analysis, visual reasoning, and large-context instruction-based workflows.