Gemma 4 12B vs Qwen3 VL 235B A22B Instruct
Compare Gemma 4 12B and Qwen3 VL 235B A22B Instruct side-by-side.
Compare Gemma 4 12B vs Qwen3 VL 235B A22B Instruct live
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Gemma 4 12B vs Qwen3 VL 235B A22B Instruct Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | Gemma 4 12B | Qwen3 VL 235B A22B Instruct |
|---|---|---|
| Organization | Qwen | |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Sep 2025 |
| Context Window | — | 256K |
| Parameters | 12B | 235B |
| License | Apache 2.0 | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.210 | |
| Output $/1M | $1.90 | |
| Vision Tasks | ||
| Captioning | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Object Detection | ||
| Model Features | ||
| Multimodal Vision | ||
| LLMs with Vision Capabilities | ||
Vision Evalsground-truth scores across 6 vision tasks | ||
| Overall | Not evaluated | 66.4% |
| Object Detection | – | 42.3% |
| Counting | – | 47.3% |
| Identification | – | 90.6% |
| OCR | – | 88.1% |
| Data Extraction | – | 86.6% |
| Reasoning | – | 43.5% |
| Avg cost / sample | – | $0.0007 |
| Avg speed / sample | – | 8.2s |
Gemma 4 12B vs Qwen3 VL 235B A22B Instruct: 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 VL 235B A22B Instruct is a flagship multimodal vision-language model developed by Qwen (Alibaba Cloud), designed for instruction-following tasks that combine advanced text generation with visual understanding. It serves as a high-end open-weight model for developers and researchers building multimodal AI systems that require strong reasoning, perception, and long-context capabilities.
The model supports interleaved text and image inputs, very long context windows (up to roughly 256K tokens), and efficient inference through a mixture-of-experts architecture with about 22B active parameters out of 235B total. In today’s landscape, it competes with top-tier proprietary vision-language models while offering the advantages of open weights and flexible deployment. Typical applications include multimodal assistants, document and image analysis, visual reasoning, and large-context instruction-based workflows.