Gemini 3.1 Pro vs Qwen3.5 122B A10B
Compare Gemini 3.1 Pro and Qwen3.5 122B A10B side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, and OCR.
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Gemini 3.1 Pro vs Qwen3.5 122B A10B: 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.
Qwen3.5-122B-A10B is a high-capacity multimodal Mixture-of-Experts (MoE) model developed by Alibaba’s Qwen team as part of the Qwen3.5 model family. The architecture contains 122 billion total parameters while activating roughly 10 billion per token through sparse expert routing, allowing the model to balance large-scale reasoning ability with relatively efficient inference compared to dense models of similar size.
The model is designed to process both text and visual inputs within a unified multimodal framework, enabling tasks that require reasoning across images, documents, charts, and natural language. This makes it suitable for applications such as document understanding, diagram interpretation, and complex visual question answering.
Qwen3.5-122B-A10B supports a native context window of approximately 256,000 tokens, which can be extended further through techniques such as YaRN scaling to support very long-context workloads. Released under the Apache 2.0 license, it builds on earlier Qwen multimodal systems and provides developers with an open-weight model capable of handling demanding multimodal reasoning and analysis tasks.
Gemini 3.1 Pro vs Qwen3.5 122B A10B Comparison Table
| Property | Gemini 3.1 Pro | Qwen3.5 122B A10B |
|---|---|---|
| Organization | Qwen | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Feb 2026 |
| Context Window | 1.0M | 256K |
| Parameters | 122B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.260 |
| Output $/1M | $12.00 | $2.08 |
| 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 Evalspass/fail results · 66 prompts Score key:≥75%40–74%<40% | ||
| Overall Score | 75.76% | 76.12% |
| Avg Response Time | 6.13s | 1.77s |
| Median input tokensincl. image tokens | 1.1K | 1.2K |
| Median output tokens | 11 | 7 |
| Est. cost / taskon this benchmark | $0.0024 | $0.0003 |
| Defect Detection | 73.3%(11/15) | 86.7%(13/15) |
| Document Understanding | 88.9%(8/9) | 77.8%(7/9) |
| Object Counting | 44.4%(4/9) | 40%(4/10) |
| Object Understanding | 92.9%(13/14) | 92.9%(13/14) |
| Spatial Understanding | 73.7%(14/19) | 73.7%(14/19) |
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