Gemma 4 26B A4B vs Qwen3.6 Plus
Compare Gemma 4 26B A4B 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 26B A4B vs Qwen3.6 Plus: Overview
Gemma 4 26B A4B is the Mixture-of-Experts variant in Google's Gemma 4 family, with 25.2B total parameters but only 3.8B active per token. Built from the same Gemini 3 research as the 31B dense sibling and released as open weights under the Apache 2.0 license, it supports a 256K token context window with text and image input and configurable thinking mode. The "A4B" in the name refers to its approximately 4B active parameters. The MoE design makes it significantly faster at inference than the dense 31B, running nearly as fast as a 4B-parameter model while delivering roughly 97% of the dense model's quality.
For vision tasks, the 26B A4B shares the same multimodal capabilities as the 31B image understanding with variable aspect ratios and resolutions, and structured bounding box output for UI element detection. The tradeoff versus the 31B dense model is a small quality reduction in exchange for much faster inference and lower hardware requirements, fitting in 18GB of VRAM at 4-bit quantization. It ranked #6 among open models on the Arena AI text leaderboard at launch.
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 26B A4B vs Qwen3.6 Plus Comparison Table
| Property | Gemma 4 26B A4B | Qwen3.6 Plus |
|---|---|---|
| Organization | Qwen | |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Apr 2026 |
| Context Window | 256K | 1.0M |
| Parameters | 25.2B | |
| License | Apache 2.0 | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | $0.325 |
| Output $/1M | $0.300 | $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 | 68.66% | 68.66% |
| Avg Response Time | 30.23s | 34.17s |
| Median input tokensincl. image tokens | 294 | 1.2K |
| Median output tokens | 214 | 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 | 85.7%(12/14) | 78.6%(11/14) |
| Spatial Understanding | 68.4%(13/19) | 68.4%(13/19) |
| OCR | ||
| Overall Score | 83.84% | 58.52% |
| Avg Response Time | 12.05s | 5.49s |
| Median input tokensincl. image tokens | 290 | 124 |
| Median output tokens | 42 | 18 |
| Est. cost / taskon this benchmark | <$0.0001 | $0.0001 |
| Focused Scene OCR | 85.9%(85/99) | 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 | 83.3%(50/60) | 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