Gemma 3 27B vs Qwen3.5-27B
Qwen3.5-27B: 64.1% average across four Vision Evals tasks (#35 of 68 models). We have not run Gemma 3 27B through Roboflow's Vision Evals, so there is no head-to-head score. Run both on your own image below.
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Gemma 3 27B vs Qwen3.5-27B Comparison Table
Evals updated November 10, 2026Pricing updated November 12, 2026
| Property | Gemma 3 27B | Qwen3.5-27B |
|---|---|---|
| Organization | Qwen | |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Mar 2025 | Feb 2026 |
| Context Window | 128K | 262K |
| Parameters | Unknown | 27B |
| License | Custom | Apache 2.0 |
| Commercial use | See license terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $0.080 | $0.260 |
| Output $/1M | $0.350 | $2.60 |
| Vision Tasks | ||
| Classification | Supported | Demo |
| Object Detection | Not listed | Demo |
| 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 |
Gemma 3 27B vs Qwen3.5-27B: Overview
Gemma 3 27B, announced on March 12, 2025, is the largest open-weight model in Google DeepMind’s Gemma 3 family. With around 27 billion parameters, it is multimodal—accepting both text and images as input and producing text outputs. It supports a 128,000-token context window and typically generates up to ~8,192 tokens, enabling it to process multi-page documents, extended conversations, or large batches of images in a single prompt.
The model is instruction-tuned in its “-it” variants for chat, reasoning, and summarization use cases, and it supports structured outputs and function calling. It is multilingual, covering over 140 languages. Deployment is flexible: the full BF16 model requires ~46 GB of VRAM, but quantization-aware training (QAT) versions in 8-bit or 4-bit reduce the footprint significantly, allowing more accessible use outside large-scale clusters. While it delivers stronger reasoning and multimodal performance than smaller Gemma models, it remains lighter and more open than proprietary systems, making it well-suited for research, development, and fine-tuned applications.
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.