Gemma 4 31B vs Qwen3.6 Plus
Compare Gemma 4 31B and Qwen3.6 Plus side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Gemma 4 31B vs Qwen3.6 Plus: 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.
Qwen3.6 Plus is a flagship model in Alibaba’s Qwen Plus series, designed for agentic workflows, coding, and multi-step reasoning. It supports a 1 million token context window and up to 65,536 output tokens, with built-in reasoning capabilities. The model is available as a hosted, proprietary API through Alibaba Cloud.
Compared to Qwen3.5, it improves reliability in multi-step execution and frontend code generation, with stronger performance on agentic coding tasks. It also supports document and image understanding, though its vision capabilities are more limited than dedicated Qwen-VL models. Qwen3.6 Plus is part of a broader Qwen ecosystem that includes both closed-source APIs and open-weight models.
Gemma 4 31B vs Qwen3.6 Plus Comparison Table
| Property | Gemma 4 31B | Qwen3.6 Plus |
|---|---|---|
| Organization | Qwen | |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Apr 2026 |
| Context Window | 256K | 1.0M |
| Parameters | 31B | |
| License | Apache 2.0 | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.120 | $0.325 |
| Output $/1M | $0.370 | $1.95 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | Demo | |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| classification | Demo | |
| Model Features | ||
| Multimodal Vision | ||
| LLMs with Vision Capabilities | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 67.16% | 68.66% |
| Avg Response Time | 34.59s | 34.17s |
| Median input tokensincl. image tokens | 294 | 1.2K |
| Median output tokens | 169 | 47 |
| Est. cost / taskon this benchmark | $0.0001 | $0.0005 |
| Defect Detection | 80%(12/15) | 86.7%(13/15) |
| Document Understanding | 88.9%(8/9) | 77.8%(7/9) |
| Object Counting | 10%(1/10) | 20%(2/10) |
| Object Understanding | 71.4%(10/14) | 78.6%(11/14) |
| Spatial Understanding | 73.7%(14/19) | 68.4%(13/19) |
| OCR | ||
| Overall Score | 84.72% | 58.52% |
| Avg Response Time | 11.82s | 5.49s |
| Median input tokensincl. image tokens | 290 | 124 |
| Median output tokens | 131 | 18 |
| Est. cost / taskon this benchmark | $0.0001 | $0.0001 |
| Focused Scene OCR | 86.9%(86/99) | 76.8%(76/99) |
| Handwritten Math | 50%(5/10) | 80%(8/10) |
| License Plate Recognition | 93.3%(28/30) | 13.3%(4/30) |
| Text Recognition | 80%(24/30) | 50%(15/30) |
| VQA & Extraction | 85%(51/60) | 51.7%(31/60) |
Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology