GPT-5.4 vs Qwen3.5 9b

Compare GPT-5.4 and Qwen3.5 9b side-by-side. See how these vision models stack up in OCR, Image Captioning, and Open Prompt.

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OpenAIGPT-5.4
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OpenAI

GPT-5.4 vs Qwen3.5 9b: Overview

GPT-5.4

GPT-5.4 is a proprietary multimodal large language model developed by OpenAI and released on March 5, 2026. It is designed for professional workloads such as advanced software development, research, and agentic automation. The model combines the general reasoning capabilities of the GPT-5 series with software engineering improvements derived from GPT-5.3-Codex. In the API and Codex environments it supports context windows of up to 1 million tokens, enabling long-context reasoning and large-scale code or document workflows.

Compared with GPT-5.2, GPT-5.4 reduces false individual claims by 33% and lowers overall response errors by 18%, improving factual reliability across complex tasks. It is also the first general-purpose OpenAI release with native computer-use capabilities, allowing agents to interact with desktops, browsers, and external applications to complete multi-step workflows. The model family includes three variants: GPT-5.4 (standard), GPT-5.4 Pro for higher-performance workloads, and GPT-5.4 Thinking, a reasoning-oriented version in ChatGPT that presents an upfront plan before generating its response. The API also introduces a Tool Search system that allows models to retrieve tool definitions dynamically, reducing token usage in tool-heavy integrations.

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-5.4 vs Qwen3.5 9b Comparison Table

PropertyGPT-5.4Qwen3.5 9b
OrganizationOpenAIQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateMar 2026Mar 2026
Context Window1.1M262K
Parameters9B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$2.50$0.100
Output $/1M$15.00$0.150
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
77.61%
71.64%
Avg Response Time7.16s8.99s
Median input tokensincl. image tokens1.4K
Median output tokens108
Est. cost / taskon this benchmark$0.0052
Defect Detection
86.7%(13/15)
86.7%(13/15)
Document Understanding
88.9%(8/9)
66.7%(6/9)
Object Counting
40%(4/10)
30%(3/10)
Object Understanding
85.7%(12/14)
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