Gemini 3.5 Flash vs Qwen3.5 122B A10B
Compare Gemini 3.5 Flash and Qwen3.5 122B A10B side-by-side. See how these vision models stack up in Open Prompt, Image Captioning, and OCR.
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Gemini 3.5 Flash vs Qwen3.5 122B A10B Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | Gemini 3.5 Flash | Qwen3.5 122B A10B |
|---|---|---|
| Organization | Qwen | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | May 2026 | Feb 2026 |
| Context Window | 1.0M | 256K |
| Parameters | 122B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $1.50 | $0.260 |
| Output $/1M | $9.00 | $2.08 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 86.0% | Not evaluated |
| Avg cost / sample | $0.011 | – |
| Avg speed / sample | 14.77s | – |
| By task | ||
| Object Detection (low) | 70.6% ±2.0, Mean of 3 runs, range 68.7 to 72.6 | – |
| Object Detection (high) | 69.8% ±1.8, Mean of 3 runs, range 67.5 to 71.1 | – |
| Counting (low) | 80.6% ±0.7, Mean of 3 runs, range 79.7 to 81.1 | – |
| Counting (high) | 82.4% ±0.0, Mean of 3 runs, range 82.4 to 82.4 | – |
| Identification (low) | 99.0% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | – |
| Identification (high) | 97.9% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | – |
| OCR (low) | 89.3% ±1.6, Mean of 3 runs, range 88.0 to 91.1 | – |
| OCR (high) | 88.9% ±0.2, Mean of 3 runs, range 88.7 to 89.1 | – |
| Data Extraction (low) | 94.5% ±0.5, Mean of 3 runs, range 93.8 to 94.8 | – |
| Data Extraction (high) | 95.5% ±1.5, Mean of 3 runs, range 93.8 to 96.9 | – |
| Reasoning (low) | 82.1% ±2.0, Mean of 3 runs, range 80.1 to 84.1 | – |
| Reasoning (high) | 81.0% ±1.7, Mean of 3 runs, range 79.5 to 82.8 | – |
Gemini 3.5 Flash vs Qwen3.5 122B A10B: Overview
Gemini 3.5 Flash is a multimodal language model developed by Google DeepMind and released at Google I/O 2026. It is built on the Gemini 3 Flash reasoning foundation and introduces configurable thinking levels (minimal, low, medium, and high) that allow developers to tune the depth of internal reasoning before a response is generated. The model accepts text, image, video, audio, and PDF inputs and produces text output, with a 1 million token context window and up to 65,000 output tokens per request. It is natively multimodal, processing visual inputs alongside text to support tasks such as image captioning, classification, optical character recognition, object detection, and visual grounding, where the model references specific regions within an image or video frame.
Its vision capabilities extend to interpreting UI screenshots, diagrams, charts, and real-world scenes, as well as understanding video and live frame sequences for activity and scene recognition. The model supports combined tool use, including Google Search, URL context, code execution, and custom functions, within a single request, and it uses reasoning context from previous turns when thought signatures are present in the conversation history, enabling persistent multi-turn reasoning chains. Gemini 3.5 Flash carries a knowledge cutoff of January 2026 and is available via the Gemini API, Google AI Studio, Google Antigravity, and the Gemini Enterprise Agent Platform.
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.