GPT-5.2 vs Qwen3.5 397B A17B

Compare GPT-5.2 and Qwen3.5 397B A17B side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.

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OpenAIGPT-5.2
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QwenQwen3.5 397B A17B
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OpenAI

GPT-5.2 vs Qwen3.5 397B A17B: 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.

Qwen3.5 397B A17B

Qwen3.5-397B-A17B is a 397B-parameter (17B active) open-weight multimodal model developed by Alibaba’s Qwen team, released on 2026-02-16 under Apache-2.0. It supports text and image inputs with text outputs, combining a sparse Mixture-of-Experts architecture with Gated Delta Networks for efficient scaling. The model provides native vision-language reasoning and a large ~262K token context window, extendable to ~1M tokens.

As the first open-weight release in the Qwen3.5 family, it positions itself as a high-capacity, long-context alternative in the large vision-language space, balancing scale and efficiency via sparse activation. It is designed for advanced reasoning, coding, agent workflows, and multimodal understanding tasks.

GPT-5.2 vs Qwen3.5 397B A17B Comparison Table

PropertyGPT-5.2Qwen3.5 397B A17B
OrganizationOpenAIQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateDec 2025Feb 2026
Context Window400K262K
Parameters397B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$1.75$0.385
Output $/1M$14.00$2.45
Vision Tasks
CaptioningDemoDemo
Object DetectionDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
ClassificationDemo
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
58.21%
Avg Response Time56.61s
Median input tokensincl. image tokens1.1K
Median output tokens54
Est. cost / taskon this benchmark$0.0006
Defect Detection
66.7%(10/15)
Document Understanding
77.8%(7/9)
Object Counting
20%(2/10)
Object Understanding
64.3%(9/14)
Spatial Understanding
57.9%(11/19)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology