Qwen3.5 27B vs Qwen3 VL 235B A22B Instruct
Compare Qwen3.5 27B and Qwen3 VL 235B A22B Instruct side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, and OCR.
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Models in this comparison
Qwen3.5 27B vs Qwen3 VL 235B A22B Instruct on Vision Evals
Qwen3.5 27B scores higher on 3 of the six Vision Evals tasks.
The widest gap is Identification, where Qwen3 VL 235B A22B Instruct leads 90.6% to 78.1%.
Overall, Qwen3.5 27B averages 64.3% (#22 of 25) against 65.8% (#20 of 25) for Qwen3 VL 235B A22B Instruct.
Qwen3 VL 235B A22B Instruct is cheaper ($0.0006 vs $0.0007 per sample), while Qwen3.5 27B is faster (7.4s vs 9.2s per sample).
Qwen3.5 27B vs Qwen3 VL 235B A22B Instruct Comparison Table
Evals updated August 6, 2026Pricing updated August 12, 2026
| Property | Qwen3.5 27B | Qwen3 VL 235B A22B Instruct |
|---|---|---|
| Organization | Qwen | Qwen |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Sep 2025 |
| Context Window | 262K | 256K |
| Parameters | 27B | 235B |
| License | Apache 2.0 | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.195 | $0.260 |
| Output $/1M | $1.56 | $1.04 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | 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 | 64.3% | 65.8% |
| Avg cost / sample | $0.0007 | $0.0006 |
| Avg speed / sample | 7.38s | 9.17s |
| By task | ||
| Object Detection | 58.8% $0.0013 | 52.2% $0.0011 |
| Counting | 54.0% $0.0002 | 47.3% $0.0003 |
| Identification | 78.1% $0.0002 | 90.6% $0.0003 |
| OCR | 84.5% $0.0009 | 88.1% $0.0007 |
| Data Extraction | 78.3% $0.0002 | 86.6% $0.0003 |
| Reasoning (low) | 31.8% $0.0002 | 29.8% $0.0003 |
| Reasoning (high) | 61.6% $0.0065 | 33.8% $0.0003 |
Qwen3.5 27B vs Qwen3 VL 235B A22B Instruct: 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.
Qwen3 VL 235B A22B Instruct is a flagship multimodal vision-language model developed by Qwen (Alibaba Cloud), designed for instruction-following tasks that combine advanced text generation with visual understanding. It serves as a high-end open-weight model for developers and researchers building multimodal AI systems that require strong reasoning, perception, and long-context capabilities.
The model supports interleaved text and image inputs, very long context windows (up to roughly 256K tokens), and efficient inference through a mixture-of-experts architecture with about 22B active parameters out of 235B total. In today’s landscape, it competes with top-tier proprietary vision-language models while offering the advantages of open weights and flexible deployment. Typical applications include multimodal assistants, document and image analysis, visual reasoning, and large-context instruction-based workflows.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3 VL 235B A22B Instruct performed slightly better overall. The two split the six vision tasks 3 to 3, but Qwen3 VL 235B A22B Instruct averages 65.8% (#20 of 25) against 64.3% (#22 of 25) 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 Identification benchmark, Qwen3 VL 235B A22B Instruct leads with 90.6% against 78.1%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3 VL 235B A22B Instruct is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0006 per sample against $0.0007. Qwen3.5 27B is priced at $0.20 per 1M input tokens and $1.56 per 1M output; Qwen3 VL 235B A22B Instruct is priced at $0.26 per 1M input tokens and $1.04 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Qwen3.5 27B is faster. Across Roboflow's Vision Evals it averaged 7.4s per inference against 9.2s. 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.