GPT-5 Mini vs Qwen3.5 27B
Compare GPT-5 Mini and Qwen3.5 27B side-by-side. See how these vision models stack up in Image Captioning, OCR, Open Prompt, Object Detection, and Classification.
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GPT-5 Mini vs Qwen3.5 27B Comparison Table
Evals updated August 6, 2026Pricing updated August 12, 2026
| Property | GPT-5 Mini | Qwen3.5 27B |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Aug 2025 | Feb 2026 |
| Context Window | 400K | 262K |
| Parameters | 27B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.250 | $0.195 |
| Output $/1M | $2.00 | $1.56 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 64.3% |
| Avg cost / sample | – | $0.0007 |
| Avg speed / sample | – | 7.38s |
| By task | ||
| Object Detection | – | 58.8% $0.0013 |
| Counting | – | 54.0% $0.0002 |
| Identification | – | 78.1% $0.0002 |
| OCR | – | 84.5% $0.0009 |
| Data Extraction | – | 78.3% $0.0002 |
| Reasoning (low) | – | 31.8% $0.0002 |
| Reasoning (high) | – | 61.6% $0.0065 |
GPT-5 Mini vs Qwen3.5 27B: 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.
Qwen3.5-27B is a multimodal dense hybrid model developed by Alibaba Cloud’s Qwen team and released in February 2026 as a high-precision entry in the Qwen3.5 "Medium" series. Unlike its Mixture-of-Experts (MoE) siblings, the 27B model utilizes a dense architecture combining Gated Delta Networks with a feed-forward structure, activating its full parameter suite for every inference to maximize reliability. This design provides the highest instruction-following and coding accuracy in its class, with a notable IFEval score of 95.0. The model features a native 262K-token context window, extensible to 1M tokens via YaRN (RoPE scaling), and is released under the Apache-2.0 license.
Optimized for agentic workflows, Qwen3.5-27B employs an early-fusion architecture that treats visual and textual data as a unified stream for deep cross-modal reasoning. This unified approach allows the model to excel in technical analysis and software engineering, matching GPT-5-mini with a 72.4% score on SWE-bench Verified. While the larger MoE variants in the family lead in raw knowledge benchmarks, the 27B model offers a stable and high-density alternative for structured data extraction and spatial perception, contributing to the Qwen3.5 family’s generational leap in OCR accuracy over the previous Qwen3-VL series.