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.
Compare GPT-5 Mini vs Qwen3.5 397B A17B live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Extract and compare text from images across multiple models.
Upload an image
Drag and drop an image here, or click to browse
Models in this comparison
GPT-5 Mini vs Qwen3.5 397B A17B: Overview
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 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 Mini vs Qwen3.5 397B A17B Comparison Table
| Property | GPT-5 Mini | Qwen3.5 397B A17B |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Aug 2025 | Feb 2026 |
| Context Window | 400K | 262K |
| Parameters | 397B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.250 | $0.385 |
| Output $/1M | $2.00 | $2.45 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | Demo | |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Classification | Demo | |
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Foundation Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Overall Score | 73.13% | 58.21% |
| Avg Response Time | 11.72s | 56.61s |
| Median input tokensincl. image tokens | 1.4K | 1.1K |
| Median output tokens | 143 | 54 |
| Est. cost / taskon this benchmark | $0.0006 | $0.0006 |
| Defect Detection | 80%(12/15) | 66.7%(10/15) |
| Document Understanding | 77.8%(7/9) | 77.8%(7/9) |
| Object Counting | 10%(1/10) | 20%(2/10) |
| Object Understanding | 85.7%(12/14) | 64.3%(9/14) |
| Spatial Understanding | 89.5%(17/19) | 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