Gemma 4 12B vs Qwen3.5 397B A17B
Compare Gemma 4 12B and Qwen3.5 397B A17B side-by-side.
Compare Gemma 4 12B vs Qwen3.5 397B A17B live
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These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Models in this comparison
Gemma 4 12B vs Qwen3.5 397B A17B: Overview
Gemma 4 12B is an open-weight multimodal model from Google in the Gemma 4 family. It is intended for text and image understanding tasks such as visual question answering, OCR, captioning, and document understanding, with a smaller parameter footprint than the larger Gemma 4 variants.
This entry is connected to Roboflow Playground vision evals for comparison. No runnable Playground workflow is configured yet, so the model page is used for discovery and benchmark context rather than direct hosted inference.
Qwen3.5-397B-A17B is a 397B-parameter (17B active) open-weight multimodal model developed by Alibaba’s Qwen team, released on 2026-02-16 under Apache-2.0. It supports text and image inputs with text outputs, combining a sparse Mixture-of-Experts architecture with Gated Delta Networks for efficient scaling. The model provides native vision-language reasoning and a large ~262K token context window, extendable to ~1M tokens.
As the first open-weight release in the Qwen3.5 family, it positions itself as a high-capacity, long-context alternative in the large vision-language space, balancing scale and efficiency via sparse activation. It is designed for advanced reasoning, coding, agent workflows, and multimodal understanding tasks.
Gemma 4 12B vs Qwen3.5 397B A17B Comparison Table
| Property | Gemma 4 12B | Qwen3.5 397B A17B |
|---|---|---|
| Organization | Qwen | |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Feb 2026 |
| Context Window | — | 262K |
| Parameters | 12B | 397B |
| License | Apache 2.0 | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.385 | |
| Output $/1M | $2.45 | |
| Vision Tasks | ||
| Captioning | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Object Detection | ||
| Model Features | ||
| Multimodal Vision | ||
| LLMs with Vision Capabilities | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Overall Score | 62.69% | 58.21% |
| Avg Response Time | 6.88s | 56.61s |
| Median input tokensincl. image tokens | 1.1K | |
| Median output tokens | 54 | |
| Est. cost / taskon this benchmark | $0.0006 | |
| Defect Detection | 73.3%(11/15) | 66.7%(10/15) |
| Document Understanding | 88.9%(8/9) | 77.8%(7/9) |
| Object Counting | 10%(1/10) | 20%(2/10) |
| Object Understanding | 78.6%(11/14) | 64.3%(9/14) |
| Spatial Understanding | 57.9%(11/19) | 57.9%(11/19) |
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