GPT-5.5 vs Qwen3.5 9b

Compare GPT-5.5 and Qwen3.5 9b side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, and OCR.

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OpenAIGPT-5.5
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QwenQwen3.5 9b
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Models in this comparison

OpenAI

GPT-5.5 vs Qwen3.5 9b: Overview

GPT-5.5

GPT-5.5 is a multimodal large language model released by OpenAI on April 23, 2026, engineered for autonomous, multi-step knowledge work and agentic workflows. It accepts text, images, and code as input, featuring enhanced spatial reasoning and visual grounding to support its computer use capabilities for operating software and navigating UI elements. Built to execute complex workflows end-to-end, the model interprets loosely defined tasks, selects appropriate tools, and performs self-verification with minimal user intervention. It is available in a standard version, a Thinking mode for extended reasoning budgets, and a Pro variant that uses parallel test-time compute for maximum precision on complex tasks.

Co-optimized with NVIDIA for GB200 NVL72 infrastructure, GPT-5.5 delivers per-token latency comparable to its predecessor GPT-5.4 while maintaining a 1-million-token context window. Despite increased capability, the model achieves greater token efficiency in coding and data analysis workflows, often completing tasks with fewer total tokens than previous versions. OpenAI reports a 60% reduction in hallucination rate compared to GPT-5.4, improving reliability for accuracy-sensitive applications. API access is available via the Responses and Chat Completions endpoints at $5 per million input tokens and $30 per million output tokens, double the unit price of GPT-5.4.

Qwen3.5 9b

Qwen3.5-9B is a 9-billion-parameter multimodal foundation model developed by Alibaba Cloud's Qwen team, released on March 2, 2026 as part of the Qwen3.5 model family. Designed for efficient multimodal reasoning and long-context language tasks, it notably outperforms the older Qwen3-30B, a model more than three times its size, on key benchmarks including GPQA Diamond, IFEval, and LongBench.

The model supports vision-language inputs through an early-fusion multimodal architecture built on a dense hybrid foundation of Gated Delta Networks and Gated Attention. It can also operate in a text-only mode by skipping the vision encoder during inference. It provides a 262,144-token context window (extensible to ~1M tokens via YaRN) and is released under the Apache License 2.0. Within the current AI landscape, Qwen3.5-9B offers a strong balance of capability and efficiency, making it well-suited for multimodal assistants, document analysis, long-context reasoning, and developer-deployed agentic systems.

GPT-5.5 vs Qwen3.5 9b Comparison Table

PropertyGPT-5.5Qwen3.5 9b
OrganizationOpenAIQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateApr 2026Mar 2026
Context Window1.0M262K
Parameters9B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$5.00$0.100
Output $/1M$30.00$0.150
Vision Tasks
CaptioningDemoDemo
Object DetectionDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
ClassificationDemo
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
77.61%
71.64%
Avg Response Time30.12s8.99s
Median input tokensincl. image tokens1.4K
Median output tokens138
Est. cost / taskon this benchmark$0.011
Defect Detection
86.7%(13/15)
86.7%(13/15)
Document Understanding
88.9%(8/9)
66.7%(6/9)
Object Counting
30%(3/10)
30%(3/10)
Object Understanding
92.9%(13/14)
71.4%(10/14)
Spatial Understanding
78.9%(15/19)
84.2%(16/19)

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