Qwen3.5-27B vs Qwen3.7 Plus
Compare Qwen3.5-27B and Qwen3.7 Plus side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, Object Detection, and OCR.
Compare Qwen3.5-27B vs Qwen3.7 Plus live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Detect and compare bounding boxes across models on the same image.
Upload an image
Drag and drop an image here, or click to browse
Models in this comparison
Qwen3.5-27B vs Qwen3.7 Plus on Vision Evals
Qwen3.5-27B scores higher on 2 of the 4 Vision Evals tasks.
The widest gap is Reasoning, where Qwen3.5-27B leads 58.1% to 39.7%.
Overall, Qwen3.5-27B averages 64.1% (#28 of 61) against 58.9% (#35 of 61) for Qwen3.7 Plus.
Qwen3.7 Plus is both cheaper ($0.0008 vs $0.0043 per sample) and faster (7.8s vs 80.4s per sample).
Qwen3.5-27B vs Qwen3.7 Plus Comparison Table
Evals updated October 8, 2026Pricing updated October 8, 2026
| Property | Qwen3.5-27B | Qwen3.7 Plus |
|---|---|---|
| Organization | Qwen | Qwen |
| Category | open | closed |
| Modality | multimodal | — |
| Release Date | Feb 2026 | Jun 2026 |
| Context Window | 262K | — |
| Parameters | 27B | Unknown |
| License | Apache 2.0 | Unknown |
| Pricing per 1M tokens | ||
| Input $/1M | $0.260 | $0.320 |
| Output $/1M | $2.60 | $1.28 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Visual Question Answering | Demo | Demo |
| Chart Question Answering | Supported | Not listed |
| Document Question Answering | Supported | Not listed |
| Image Tagging | Supported | Not listed |
| Multi-Label Classification | Supported | Not listed |
| Vision Language | Supported | Not listed |
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| LLMs with Vision Capabilities | Supported | Not listed |
| Multimodal Vision | Supported | Not listed |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 64.1% 4/5 tasks | 58.9% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0043 | $0.0008 |
| Avg speed / sample | 80.37s | 7.77s |
| By task | ||
| Object Detection | 50.5% ±3.5, Mean of 3 runs, range 46.1 to 53.0 | 60.1% |
| Counting | 67.6% ±1.4, Mean of 3 runs, range 66.2 to 68.9 | 50.0% |
| Identification | 80.2% ±4.7, Mean of 3 runs, range 75.0 to 84.4 | 84.4% |
| OCR (low) | – | 60.3% |
| by category |
| |
| OCR (high) | – | 65.5% |
| by category |
| |
| Reasoning (low) | 58.1% ±2.3, Mean of 3 runs, range 55.6 to 60.3 | 39.7% |
| Reasoning (high) | – | 68.2% |
Qwen3.5-27B vs Qwen3.7 Plus: Overview
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.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.5-27B performed slightly better overall. The two split the 4 vision tasks 2 to 2, but Qwen3.5-27B averages 64.1% (#28 of 61) against 58.9% (#35 of 61) for Qwen3.7 Plus. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Reasoning benchmark at low effort, Qwen3.5-27B leads with 58.1% against 39.7%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.7 Plus is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0008 per sample against $0.0043. Actual costs depend on your image sizes, prompts, and output length.
Qwen3.7 Plus is faster. Across Roboflow's Vision Evals it averaged 7.8s per inference against 80.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.