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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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GoogleGemini 3.5 Flash
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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 all five Vision Evals tasks.

The widest gap is OCR, where Gemini 3.5 Flash leads 76.7% to 73.9%.

Overall, Gemini 3.5 Flash averages 81.8% (#3 of 62) against 80.1% (#5 of 62) for Gemini 3.7 Flash.

Gemini 3.7 Flash is both cheaper ($0.0033 vs $0.012 per sample) and faster (11.0s vs 11.5s per sample).

Gemini 3.5 FlashGemini 3.7 Flash

Gemini 3.5 Flash vs Gemini 3.7 Flash Comparison Table

Evals updated October 13, 2026Pricing updated October 13, 2026

PropertyGemini 3.5 FlashGemini 3.7 Flash
OrganizationGoogleGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMay 2026Aug 2026
Context Window1.0M1.0M
ParametersUnknownUndisclosed
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$1.50$0.750
Output $/1M$9.00$3.75
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoDemo
OCRDemoDemo
Vision LanguageSupportedSupported
Visual Question AnsweringDemoDemo
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
81.8%
80.1%
Avg cost / sample$0.012$0.0033
Avg speed / sample11.52s11.04s
By task
Object Detection (low)
70.6%
±2.0, Mean of 3 runs, range 68.7 to 72.6
$0.016
70.5%
±1.1, Mean of 3 runs, range 69.4 to 71.5
$0.0047
Object Detection (high)
69.8%
±1.8, Mean of 3 runs, range 67.5 to 71.1
$0.021
74.3%
±0.8, Mean of 3 runs, range 73.3 to 75.0
$0.0089
Counting (low)
80.6%
±0.7, Mean of 3 runs, range 79.7 to 81.1
$0.0075
78.4%
±1.4, Mean of 3 runs, range 77.0 to 79.7
$0.0025
Counting (high)
82.4%
±0.0, Mean of 3 runs, range 82.4 to 82.4
$0.017
79.3%
±2.0, Mean of 3 runs, range 77.0 to 81.1
$0.0056
Identification (low)
99.0%
±1.6, Mean of 3 runs, range 96.9 to 100.0
$0.0040
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0013
Identification (high)
97.9%
±1.6, Mean of 3 runs, range 96.9 to 100.0
$0.0068
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0021
OCR (low)
76.7%
$0.013
73.9%
$0.0032
by category
Single value
71.7%
Transcription
90.0%
Structured JSON
86.8%
Text localization
56.2%
Single value
74.8%
Transcription
91.1%
Structured JSON
86.7%
Text localization
29.4%
OCR (high)
75.5%
$0.031
78.8%
$0.010
by category
Single value
69.6%
Transcription
85.4%
Structured JSON
88.1%
Text localization
53.3%
Single value
71.7%
Transcription
91.9%
Structured JSON
90.6%
Text localization
59.2%
Reasoning (low)
82.1%
±2.0, Mean of 3 runs, range 80.1 to 84.1
$0.0082
80.8%
±2.0, Mean of 3 runs, range 78.8 to 82.8
$0.0022
Reasoning (high)
81.0%
±1.7, Mean of 3 runs, range 79.5 to 82.8
$0.018
81.9%
±1.3, Mean of 3 runs, range 80.1 to 82.8
$0.0050

Gemini 3.5 Flash vs Gemini 3.7 Flash: Overview

Gemini 3.5 Flash

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

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.