Gemini 3.5 Flash vs Gemini 3.7 Flash
Compare Gemini 3.5 Flash and Gemini 3.7 Flash side-by-side. See how these vision models stack up in Open Prompt, Image Captioning, OCR, Classification, and Object Detection.
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Models in this comparison
Gemini 3.5 Flash vs Gemini 3.7 Flash on Vision Evals
Gemini 3.5 Flash scores higher on 5 of the six Vision Evals tasks.
The widest gap is Counting, where Gemini 3.5 Flash leads 80.6% to 78.4%.
Overall, Gemini 3.5 Flash averages 86.0% (#2 of 53) against 85.2% (#3 of 53) for Gemini 3.7 Flash.
Gemini 3.7 Flash is cheaper ($0.0031 vs $0.011 per sample), while Gemini 3.5 Flash is faster (14.8s vs 16.5s per sample).
Gemini 3.5 Flash vs Gemini 3.7 Flash Comparison Table
Evals updated September 5, 2026Pricing updated September 13, 2026
| Property | Gemini 3.5 Flash | Gemini 3.7 Flash |
|---|---|---|
| Organization | ||
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | May 2026 | Aug 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | Undisclosed | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $1.50 | $0.750 |
| Output $/1M | $9.00 | $3.75 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | 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% | 85.2% |
| Avg cost / sample | $0.011 | $0.0031 |
| Avg speed / sample | 14.77s | 16.50s |
| By task | ||
| Object Detection (low) | 70.6% ±2.0, Mean of 3 runs, range 68.7 to 72.6 | 70.5% ±1.1, Mean of 3 runs, range 69.4 to 71.5 |
| Object Detection (high) | 69.8% ±1.8, Mean of 3 runs, range 67.5 to 71.1 | 74.3% ±0.8, Mean of 3 runs, range 73.3 to 75.0 |
| Counting (low) | 80.6% ±0.7, Mean of 3 runs, range 79.7 to 81.1 | 78.4% ±1.4, Mean of 3 runs, range 77.0 to 79.7 |
| Counting (high) | 82.4% ±0.0, Mean of 3 runs, range 82.4 to 82.4 | 79.3% ±2.0, Mean of 3 runs, range 77.0 to 81.1 |
| Identification (low) | 99.0% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| Identification (high) | 97.9% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| OCR (low) | 89.3% ±1.6, Mean of 3 runs, range 88.0 to 91.1 | 88.2% ±1.6, Mean of 3 runs, range 86.9 to 90.0 |
| OCR (high) | 88.9% ±0.2, Mean of 3 runs, range 88.7 to 89.1 | 89.0% ±0.8, Mean of 3 runs, range 88.3 to 89.9 |
| Data Extraction (low) | 94.5% ±0.5, Mean of 3 runs, range 93.8 to 94.8 | 96.2% ±0.5, Mean of 3 runs, range 95.9 to 96.9 |
| Data Extraction (high) | 95.5% ±1.5, Mean of 3 runs, range 93.8 to 96.9 | 95.9% ±0.0, Mean of 3 runs, range 95.9 to 95.9 |
| Reasoning (low) | 82.1% ±2.0, Mean of 3 runs, range 80.1 to 84.1 | 80.8% ±2.0, Mean of 3 runs, range 78.8 to 82.8 |
| Reasoning (high) | 81.0% ±1.7, Mean of 3 runs, range 79.5 to 82.8 | 81.9% ±1.3, Mean of 3 runs, range 80.1 to 82.8 |
Gemini 3.5 Flash vs Gemini 3.7 Flash: 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.
Gemini 3.7 Flash is a proprietary multimodal model from Google, positioned in the Flash branch of the Gemini 3 series that trades some of the capacity of the larger Pro models for lower latency and lower cost per token. It accepts interleaved text and image input alongside other modalities handled by the Gemini family and returns text, and it continues the series pattern of exposing a configurable thinking budget so that reasoning effort can be scaled up for harder problems or reduced for high throughput extraction, routing and classification work. The model is announced roughly three weeks after Gemini 3.6 Flash, part of an unusually fast iteration cadence within the Flash line.
Google reports gains concentrated in agentic coding and front end generation, citing a WebDev Arena Elo of 1588 for this release compared with 1538 for the preceding Flash model, and describes it as producing more functional layouts and more feature complete applications in fewer prompts. Weights are not published and the architecture, parameter count and training corpus are undisclosed, consistent with prior Gemini releases. Visual capability follows the Flash lineage, covering image and document understanding, chart and diagram interpretation, text recognition in images, and general visual question answering.