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GPT-5.4 Nano vs Qwen3.6 Plus

Compare GPT-5.4 Nano and Qwen3.6 Plus side-by-side. See how these vision models stack up in OCR, Image Captioning, and Open Prompt.

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OpenAIGPT-5.4 Nano
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QwenQwen3.6 Plus
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GPT-5.4 Nano vs Qwen3.6 Plus: Overview

GPT-5.4 Nano

GPT-5.4 nano is a high-throughput model developed by OpenAI and released on March 17, 2026, as the efficiency-optimized entry in the GPT-5.4 family. Engineered for cost-sensitive production environments and latency-critical workloads, it features an expanded 400,000-token context window that enables the processing of large document batches or extensive logs in a single pass. The model is primarily optimized for text-heavy operations, serving as a premier engine for high-volume classification, data extraction, ranking, and the orchestration of lightweight sub-agents where speed and low per-token costs are the primary requirements.

While it supports text and image inputs, GPT-5.4 nano is designed as a text-first worker rather than a specialized visual reasoning tool. In multi-model architectures, it is best utilized for structured text tasks and simple coding sub-tasks, leaving intensive vision reasoning and UI navigation to its sibling, GPT-5.4 mini. Compared to the previous GPT-5 nano, this version provides a significant leap in reliability for structured outputs and tool calling, making it a dependable and economical choice for developers building scalable, automated pipelines that require rapid execution at the edge of the GPT-5.4 ecosystem.

Qwen3.6 Plus

Qwen3.6 Plus is a flagship model in Alibaba’s Qwen Plus series, designed for agentic workflows, coding, and multi-step reasoning. It supports a 1 million token context window and up to 65,536 output tokens, with built-in reasoning capabilities. The model is available as a hosted, proprietary API through Alibaba Cloud.

Compared to Qwen3.5, it improves reliability in multi-step execution and frontend code generation, with stronger performance on agentic coding tasks. It also supports document and image understanding, though its vision capabilities are more limited than dedicated Qwen-VL models. Qwen3.6 Plus is part of a broader Qwen ecosystem that includes both closed-source APIs and open-weight models.

GPT-5.4 Nano vs Qwen3.6 Plus Comparison Table

PropertyGPT-5.4 NanoQwen3.6 Plus
OrganizationOpenAIQwen
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMar 2026Apr 2026
Context Window400K1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.200$0.325
Output $/1M$1.25$1.95
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%
Visual Understanding
Overall Score
62.69%
68.66%
Avg Response Time3.72s34.17s
Median input tokensincl. image tokens1.4K1.2K
Median output tokens10547
Est. cost / taskon this benchmark$0.0004$0.0005
Defect Detection
80%(12/15)
86.7%(13/15)
Document Understanding
77.8%(7/9)
77.8%(7/9)
Object Counting
30%(3/10)
20%(2/10)
Object Understanding
64.3%(9/14)
78.6%(11/14)
Spatial Understanding
57.9%(11/19)
68.4%(13/19)
OCR
Overall Score
62.45%
58.52%
Avg Response Time2.59s5.49s
Median input tokensincl. image tokens105124
Median output tokens8718
Est. cost / taskon this benchmark$0.0001$0.0001
Focused Scene OCR
55.6%(55/99)
76.8%(76/99)
Handwritten Math
20%(2/10)
80%(8/10)
License Plate Recognition
83.3%(25/30)
13.3%(4/30)
Text Recognition
70%(21/30)
50%(15/30)
VQA & Extraction
66.7%(40/60)
51.7%(31/60)

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