Gemini 3.5 Flash vs Qwen3.7 Plus
Compare Gemini 3.5 Flash and Qwen3.7 Plus side-by-side. See how these vision models stack up in Open Prompt, Image Captioning, OCR, Classification, and Object Detection.
Compare Gemini 3.5 Flash vs Qwen3.7 Plus live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Detect and compare bounding boxes across models on the same image.
Upload an image
Drag and drop an image here, or click to browse
Models in this comparison
Gemini 3.5 Flash vs Qwen3.7 Plus on Vision Evals
Gemini 3.5 Flash scores higher on all six Vision Evals tasks.
The widest gap is Reasoning, where Gemini 3.5 Flash leads 82.1% to 39.7%.
Overall, Gemini 3.5 Flash averages 86.3% (#2 of 59) against 67.4% (#35 of 59) for Qwen3.7 Plus.
Qwen3.7 Plus is both cheaper ($0.0008 vs $0.011 per sample) and faster (7.0s vs 14.8s per sample).
Gemini 3.5 Flash vs Qwen3.7 Plus Comparison Table
Evals updated September 29, 2026Pricing updated October 6, 2026
| Property | Gemini 3.5 Flash | Qwen3.7 Plus |
|---|---|---|
| Organization | Qwen | |
| Category | closed | closed |
| Modality | multimodal | — |
| Release Date | May 2026 | Jun 2026 |
| Context Window | 1.0M | — |
| Parameters | Unknown | Unknown |
| License | Proprietary | Unknown |
| Pricing per 1M tokens | ||
| Input $/1M | $1.50 | $0.320 |
| Output $/1M | $9.00 | $1.28 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Visual Question Answering | Demo | Demo |
| Chart Question Answering | Supported | Not listed |
| Document Question Answering | Supported | Not listed |
| Image Tagging | Supported | Not listed |
| Multi-Label Classification | Supported | Not listed |
| Vision Language | Supported | Not listed |
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| LLMs with Vision Capabilities | Supported | Not listed |
| Multimodal Vision | Supported | Not listed |
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 86.3% | 67.4% |
| Avg cost / sample | $0.011 | $0.0008 |
| Avg speed / sample | 14.77s | 7.01s |
| By task | ||
| Object Detection (low) | 72.0% ±1.8, Mean of 3 runs, range 70.2 to 73.8 | 60.1% |
| Object Detection (high) | 70.5% ±1.3, Mean of 3 runs, range 68.8 to 71.5 | – |
| Counting (low) | 80.6% ±0.7, Mean of 3 runs, range 79.7 to 81.1 | 50.0% |
| 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 | 84.4% |
| 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 | 86.5% |
| 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 | 83.5% |
| 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 | 39.7% |
| Reasoning (high) | 81.0% ±1.7, Mean of 3 runs, range 79.5 to 82.8 | 68.2% |
Gemini 3.5 Flash vs Qwen3.7 Plus: 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.