GPT-5 Mini vs Qwen3.5-27B
Qwen3.5-27B: 64.1% average across four Vision Evals tasks (#35 of 68 models). We have not run GPT-5 Mini through Roboflow's Vision Evals, so there is no head-to-head score. Run both on your own image below.
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GPT-5 Mini vs Qwen3.5-27B Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 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 | Unknown | 27B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $0.250 | $0.260 |
| Output $/1M | $2.00 | $2.60 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks | ||
| Overall | Not evaluated | 64.1%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | – | $0.0043 |
| Avg speed / sample | – | 80.37s |
| By task | ||
| Object Detection | – | 50.5% ±3.5, Mean of 3 runs, range 46.1 to 53.0 |
| Counting | – | 67.6% ±1.4, Mean of 3 runs, range 66.2 to 68.9 |
| Identification | – | 80.2% ±4.7, Mean of 3 runs, range 75.0 to 84.4 |
| OCR | – | – |
| by category | ||
| Reasoning | – | 58.1% ±2.3, Mean of 3 runs, range 55.6 to 60.3 |
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. 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. 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.