Gemma 4 31B vs Qwen3.5-27B
Qwen3.5-27B and Gemma 4 31B score 64.1% vs 57.7% averaged across four Vision Evals tasks (#32 and #41 of 65 models), so Qwen3.5-27B leads overall.
- Qwen3.5-27B wins all 4 tasks, with its widest lead on counting (67.6% vs 51.4%).
- Gemma 4 31B is about a third of the cost: $0.0015 vs $0.0043 overall average cost per sample.
- Gemma 4 31B is faster: 34.4s vs 80.4s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemma 4 31B vs Qwen3.5-27B on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemma 4 31B vs Qwen3.5-27B Comparison Table
Evals updated October 26, 2026Pricing updated November 8, 2026
| Property | Gemma 4 31B | Qwen3.5-27B |
|---|---|---|
| Organization | Qwen | |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Feb 2026 |
| Context Window | 256K | 262K |
| Parameters | 31B | 27B |
| License | Apache 2.0 | Apache 2.0 |
| Commercial use | free for commercial use | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $0.200 | $0.260 |
| Output $/1M | $0.400 | $2.60 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks | ||
| Overall | 57.7%* | 64.1%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0015 | $0.0043 |
| Avg speed / sample | 34.36s | 80.37s |
| By task | ||
| Object Detection | 47.5% ±0.6, Mean of 3 runs, range 46.8 to 48.0 | 50.5% ±3.5, Mean of 3 runs, range 46.1 to 53.0 |
| Counting | 51.4% ±2.7, Mean of 3 runs, range 48.6 to 54.0 | 67.6% ±1.4, Mean of 3 runs, range 66.2 to 68.9 |
| Identification | 79.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | 80.2% ±4.7, Mean of 3 runs, range 75.0 to 84.4 |
| OCR | – | – |
| by category | ||
| Reasoning | 52.8% ±1.3, Mean of 3 runs, range 51.7 to 54.3 | 58.1% ±2.3, Mean of 3 runs, range 55.6 to 60.3 |
Gemma 4 31B vs Qwen3.5-27B: Overview
Gemma 4 31B is the largest dense model in Google's Gemma 4 family, built from the same research as Gemini 3 and released as open weights under the Apache 2.0 license. It supports a 256K token context window with text and image input, configurable thinking mode for step-by-step reasoning, and multilingual support across 140+ languages. The unquantized model fits on a single 80GB GPU.
For vision tasks, Gemma 4 31B supports image understanding with variable aspect ratios and resolutions, and can output structured bounding boxes for UI element detection, making it useful for document parsing and UI understanding. Compared to Gemma 3, it delivers stronger reasoning and multimodal performance. It is part of a four-size family alongside the 26B A4B MoE variant and two on-device models (E2B, E4B), with the 31B dense variant optimized for output quality and fine-tuning over inference speed.
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.