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Gemini 3.5 Flash-Lite vs Gemini 3.7 Flash

Compare Gemini 3.5 Flash-Lite and Gemini 3.7 Flash side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, and OCR.

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GoogleGemini 3.5 Flash-Lite
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Models in this comparison

Gemini 3.5 Flash-Lite vs Gemini 3.7 Flash on Vision Evals

Gemini 3.7 Flash scores higher on 5 of the six Vision Evals tasks.

The widest gap is Reasoning, where Gemini 3.7 Flash leads 82.8% to 48.3%.

Overall, Gemini 3.5 Flash-Lite averages 69.6% (#16 of 30) against 84.6% (#2 of 30) for Gemini 3.7 Flash.

Gemini 3.5 Flash-Lite is both cheaper ($0.0014 vs $0.0016 per sample) and faster (2.7s vs 10.0s per sample).

Gemini 3.5 Flash-LiteGemini 3.7 Flash

Gemini 3.5 Flash-Lite vs Gemini 3.7 Flash Comparison Table

Evals updated August 14, 2026Pricing updated August 15, 2026

PropertyGemini 3.5 Flash-LiteGemini 3.7 Flash
OrganizationGoogleGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2026Aug 2026
Context Window1.0M1.0M
ParametersUndisclosed
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.300$0.375
Output $/1M$2.50$1.88
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemoDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Video Classification
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
69.6%
84.6%
Avg cost / sample$0.0014$0.0016
Avg speed / sample2.70s9.97s
By task
Object Detection
57.5%
$0.0023
69.4%
$0.0024
Counting
52.7%
$0.0007
77.0%
$0.0013
Identification
81.3%
$0.0004
96.9%
$0.0007
OCR
87.4%
$0.0011
86.9%
$0.0014
Data Extraction
90.7%
$0.0004
94.8%
$0.0007
Reasoning (low)
48.3%
$0.0012
82.8%
$0.0011
Reasoning (high)
68.9%
$0.0042
82.1%
$0.0026

Gemini 3.5 Flash-Lite vs Gemini 3.7 Flash: Overview

Gemini 3.5 Flash-Lite

Gemini 3.5 Flash-Lite is a natively multimodal reasoning model developed by Google DeepMind, released on July 21, 2026 as part of the Gemini 3.5 model family. It is the fastest model in the 3.5 series, designed for both low-latency tasks and high-throughput production workloads such as agentic search, document processing, receipt translation, and large-scale data extraction. The model accepts text, images, audio, and video as inputs, with a context window of up to 1 million tokens, and produces text output. It supports configurable thinking levels, allowing developers to tune the balance between response quality, cost, and latency depending on workload requirements.

On agentic and coding benchmarks, Gemini 3.5 Flash-Lite significantly outperforms its predecessor, Gemini 3.1 Flash-Lite, including on Terminal-Bench 2.1 (54% vs. 31%), GDM-MRCR v2 long-context (72.2% vs. 60.1%), and real-world task execution as measured by GDPval-AA v2 (1140 vs. 642). It also surpasses Gemini 3 Flash on SWE-Bench Pro (54.2% vs. 49.6%) and OSWorld-Verified (74.0% vs. 65.1%). According to the Artificial Analysis Index, the model generates output at approximately 350 tokens per second. It is built on the Gemini 3.5 Flash foundation and is evaluated across reasoning, coding, multimodal understanding, multilingual performance, and long-context tasks. The model is developed under Google's Frontier Safety Framework.

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.

Frequently Asked Questions

On Roboflow's Vision Evals, Gemini 3.7 Flash performed better. It scores higher on 5 of the six vision tasks and averages 84.6% (#2 of 30) against 69.6% (#16 of 30) for Gemini 3.5 Flash-Lite. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

No. On the Vision Evals Reasoning benchmark at low effort, Gemini 3.7 Flash leads with 82.8% against 48.3%. This is the widest gap between the two models across the benchmark's tasks.

Gemini 3.5 Flash-Lite is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0014 per sample against $0.0016. Gemini 3.5 Flash-Lite is priced at $0.30 per 1M input tokens and $2.50 per 1M output; Gemini 3.7 Flash is priced at $0.38 per 1M input tokens and $1.88 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Gemini 3.5 Flash-Lite is faster. Across Roboflow's Vision Evals it averaged 2.7s per inference against 10.0s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.

Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.