Gemini 3.1 Pro vs Qwen3 VL 235B A22B Instruct
Compare Gemini 3.1 Pro and Qwen3 VL 235B A22B Instruct side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, and OCR.
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Models in this comparison
Gemini 3.1 Pro vs Qwen3 VL 235B A22B Instruct on Vision Evals
Gemini 3.1 Pro scores higher on all six Vision Evals tasks.
The widest gap is Reasoning, where Gemini 3.1 Pro leads 91.3% to 43.5%.
Overall, Gemini 3.1 Pro averages 84.6% (#2 of 16) against 66.4% (#10 of 16) for Qwen3 VL 235B A22B Instruct.
Qwen3 VL 235B A22B Instruct is cheaper ($0.0007 vs $0.0068 per sample), while Gemini 3.1 Pro is faster (5.9s vs 8.2s per sample).
Gemini 3.1 Pro vs Qwen3 VL 235B A22B Instruct Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | Gemini 3.1 Pro | Qwen3 VL 235B A22B Instruct |
|---|---|---|
| Organization | Qwen | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Sep 2025 |
| Context Window | 1.0M | 256K |
| Parameters | 235B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.210 |
| Output $/1M | $12.00 | $1.90 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | Demo | |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Classification | Demo | |
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Foundation Vision | ||
Vision Evalsground-truth scores across 6 vision tasks | ||
| Overall | 84.6% | 66.4% |
| Object Detection | 56.9% | 42.3% |
| Counting | 71.6% | 47.3% |
| Identification | 100.0% | 90.6% |
| OCR | 92.6% | 88.1% |
| Data Extraction | 94.8% | 86.6% |
| Reasoning | 91.3% | 43.5% |
| Avg cost / sample | $0.0068 | $0.0007 |
| Avg speed / sample | 5.9s | 8.2s |
Gemini 3.1 Pro vs Qwen3 VL 235B A22B Instruct: Overview
Gemini 3.1 Pro is a proprietary multimodal model from Google’s Gemini 3 series, released in early 2026 and designed for advanced reasoning across large multimodal datasets. It accepts text, images, audio, video, and documents, supporting up to a 1-million-token input context with up to 64k output tokens. Compared with Gemini 3 Pro, it improves long-context synthesis and multi-step reasoning, enabling more reliable analysis of large documents, datasets, and software codebases.
The model also advances visual understanding and grounding, allowing it to interpret UI screenshots, diagrams, and real-world scenes while referencing specific regions within images or video. These capabilities make Gemini 3.1 Pro well suited for multimodal workflows involving document processing, interface analysis, robotics research, and complex visual reasoning.
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.