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.
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Models in this comparison
Qwen3.5 27B vs Qwen3.7 Plus on Vision Evals
Qwen3.7 Plus scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where Qwen3.7 Plus leads 39.7% to 31.8%.
Overall, Qwen3.5 27B averages 64.3% (#26 of 31) against 67.4% (#18 of 31) for Qwen3.7 Plus.
Qwen3.5 27B is cheaper ($0.0007 vs $0.0008 per sample), while Qwen3.7 Plus is faster (7.0s vs 7.4s per sample).
Qwen3.5 27B vs Qwen3.7 Plus Comparison Table
Evals updated August 20, 2026Pricing updated August 24, 2026
| Property | Qwen3.5 27B | Qwen3.7 Plus |
|---|---|---|
| Organization | Qwen | Qwen |
| Category | open | closed |
| Modality | multimodal | — |
| Release Date | Feb 2026 | — |
| Context Window | 262K | — |
| Parameters | 27B | |
| License | Apache 2.0 | |
| Pricing per 1M tokens | ||
| Input $/1M | $0.195 | $0.320 |
| Output $/1M | $1.56 | $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 | ||
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Vision Language | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 64.3% | 67.4% |
| Avg cost / sample | $0.0007 | $0.0008 |
| Avg speed / sample | 7.38s | 7.01s |
| By task | ||
| Object Detection | 58.8% $0.0013 | 60.1% $0.0013 |
| Counting | 54.0% $0.0002 | 50.0% $0.0004 |
| Identification | 78.1% $0.0002 | 84.4% $0.0003 |
| OCR | 84.5% $0.0009 | 86.5% $0.0009 |
| Data Extraction | 78.3% $0.0002 | 83.5% $0.0004 |
| Reasoning (low) | 31.8% $0.0002 | 39.7% $0.0003 |
| Reasoning (high) | 61.6% $0.0065 | 68.2% $0.0043 |
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, Qwen3.7 Plus performed better. It scores higher on 5 of the six vision tasks and averages 67.4% (#18 of 31) against 64.3% (#26 of 31) for Qwen3.5 27B. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Reasoning benchmark at low effort, Qwen3.7 Plus leads with 39.7% against 31.8%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.5 27B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0007 per sample against $0.0008. Qwen3.5 27B is priced at $0.20 per 1M input tokens and $1.56 per 1M output; Qwen3.7 Plus is priced at $0.32 per 1M input tokens and $1.28 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Qwen3.7 Plus is faster. Across Roboflow's Vision Evals it averaged 7.0s per inference against 7.4s. 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.