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GPT-5 Mini vs Qwen3.5 397B A17B

Compare GPT-5 Mini 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 Mini
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QwenQwen3.5 397B A17B
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GPT-5 Mini vs Qwen3.5 397B A17B Comparison Table

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

PropertyGPT-5 MiniQwen3.5 397B A17B
OrganizationOpenAIQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateAug 2025Feb 2026
Context Window400K262K
Parameters397B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$0.250$0.390
Output $/1M$2.00$2.34
Vision Tasks
CaptioningDemoDemo
Object DetectionDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
ClassificationDemo
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision

GPT-5 Mini vs Qwen3.5 397B A17B: Overview

GPT-5 Mini

GPT-5 Mini, released by OpenAI on August 7, 2025, is a mid-tier variant of the GPT-5 family that balances cost, speed, and capability. It is multimodal, supporting both text and image inputs, and offers a substantial input context window of ~400,000 tokens with output lengths up to ~128,000 tokens. While less powerful than the full GPT-5, it inherits its safety tuning, instruction-following improvements, and multimodal reasoning, making it a practical choice for developers who need large context handling without the expense of premium models.

GPT-5 Mini is optimized for affordability while retaining strong reasoning performance. Benchmarks show it outperforming earlier models such as GPT-4o on many multimodal and medical VQA tasks, though it lags behind GPT-5 on the most complex problems. Ideal use cases include prototyping, scalable content generation, document analysis, and mid-range reasoning tasks where efficiency and context capacity matter more than top-tier accuracy.

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.