Gemini 3 Pro vs Qwen3 VL 235B A22B Instruct
Compare Gemini 3 Pro and Qwen3 VL 235B A22B Instruct side-by-side. See how these vision models stack up in OCR, Image Captioning, and Open Prompt.
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Gemini 3 Pro is deprecated and can no longer be run. Details and evals are still available on its model page.
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Gemini 3 Pro vs Qwen3 VL 235B A22B Instruct Comparison Table
Evals updated August 14, 2026Pricing updated August 19, 2026
| Property | Gemini 3 Pro | Qwen3 VL 235B A22B Instruct |
|---|---|---|
| Organization | Qwen | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Nov 2025 | Sep 2025 |
| Context Window | 1.0M | 256K |
| Parameters | 235B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.210 | |
| Output $/1M | $1.90 | |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | ||
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | ||
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | Deprecated | 65.8% |
| Avg cost / sample | – | $0.0007 |
| Avg speed / sample | – | 9.17s |
| By task | ||
| Object Detection | – | 52.1% $0.0014 |
| Counting | – | 47.3% $0.0002 |
| Identification | – | 90.6% $0.0002 |
| OCR | – | 88.1% $0.0010 |
| Data Extraction | – | 86.6% $0.0002 |
| Reasoning (low) | – | 29.8% $0.0002 |
| Reasoning (high) | – | 33.8% $0.0002 |
Gemini 3 Pro vs Qwen3 VL 235B A22B Instruct: Overview
Gemini 3 Pro is Google DeepMind’s flagship multimodal frontier model, built for high-accuracy reasoning and large-scale context understanding across text, images, audio, video, code, and documents. It delivers major gains over Gemini 2.5 Pro, supported by a 1M-token window and strong performance on Google-reported benchmarks such as GPQA Diamond, MMMU-Pro, and Video-MMMU.
The model excels at structured outputs, tool use, and agentic coding, enabling complex multi-step workflows and analysis of entire books, codebases, or long videos in a single prompt. Positioned as Google’s top production model, it balances advanced reasoning with broad multimodal capabilities, making it well suited for research assistants, automation agents, coding systems, and enterprise-scale document and media analysis.
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.