Roboflow

Gemini 3.8 Flash vs Gemini 3.7 Flash vs Gemini 3.5 Flash

Gemini 3.5 Flash leads at 81.8% averaged across five Vision Evals tasks, ahead of Gemini 3.7 Flash (80.1%) and Gemini 3.8 Flash (79.8%).

MetricGemini 3.8 FlashGemini 3.7 FlashGemini 3.5 Flash
Average79.8% #880.1% #781.8% #5
Tasks won005
Cost / sample$0.0045$0.0033$0.012
Time / sample12.8s11.0s11.5s

Cost and time are overall averages per sample. Best in bold.

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Gemini 3.8 Flash vs Gemini 3.7 Flash vs Gemini 3.5 Flash on Vision Evals

Score by task
TaskGemini 3.8 FlashGemini 3.7 FlashGemini 3.5 Flash
Object Detection68.170.570.6
Counting78.878.480.6
Identification97.996.999.0
OCR72.873.976.7
Reasoning81.280.882.1

Best per task in bold. Roboflow Vision Evals.

Gemini 3.8 Flash vs Gemini 3.7 Flash Comparison Table + 1 other

Evals updated October 26, 2026Pricing updated October 29, 2026

PropertyGemini 3.8 FlashGemini 3.7 FlashGemini 3.5 Flash
OrganizationGoogleGoogleGoogle
Categoryclosedclosedclosed
Modalitymultimodalmultimodalmultimodal
Release DateSep 2026Aug 2026May 2026
Context Window1.0M1.0M1.0M
ParametersUnknownUndisclosedUnknown
LicenseProprietaryProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$0.825$0.750$1.50
Output $/1M$4.13$3.75$9.00
Vision Tasks
All support 10 more tasks
Model Features
All support 3 more features
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
79.8%
80.1%
81.8%
Avg cost / sample$0.0045$0.0033$0.012
Avg speed / sample12.76s11.04s11.52s
By task
Object Detection (low)
68.1%
±0.8, Mean of 3 runs, range 67.3 to 69.0
$0.0045
70.5%
±1.1, Mean of 3 runs, range 69.4 to 71.5
$0.0047
70.6%
±2.0, Mean of 3 runs, range 68.7 to 72.6
$0.016
Object Detection (high)
74.8%
±1.1, Mean of 3 runs, range 73.4 to 75.6
$0.023
74.3%
±0.8, Mean of 3 runs, range 73.3 to 75.0
$0.0089
69.8%
±1.8, Mean of 3 runs, range 67.5 to 71.1
$0.021
Counting (low)
78.8%
±2.7, Mean of 3 runs, range 75.7 to 81.1
$0.0040
78.4%
±1.4, Mean of 3 runs, range 77.0 to 79.7
$0.0025
80.6%
±0.7, Mean of 3 runs, range 79.7 to 81.1
$0.0075
Counting (high)
79.3%
±1.4, Mean of 3 runs, range 78.4 to 81.1
$0.027
79.3%
±2.0, Mean of 3 runs, range 77.0 to 81.1
$0.0056
82.4%
±0.0, Mean of 3 runs, range 82.4 to 82.4
$0.017
Identification (low)
97.9%
±1.6, Mean of 3 runs, range 96.9 to 100.0
$0.0016
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0013
99.0%
±1.6, Mean of 3 runs, range 96.9 to 100.0
$0.0040
Identification (high)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0037
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0021
97.9%
±1.6, Mean of 3 runs, range 96.9 to 100.0
$0.0068
OCR (low)
72.8%
$0.0049
73.9%
$0.0032
76.7%
$0.013
by category
Single value
73.5%
Transcription
91.3%
Structured JSON
86.1%
Text localization
26.4%
Single value
74.8%
Transcription
91.1%
Structured JSON
86.7%
Text localization
29.4%
Single value
71.7%
Transcription
90.0%
Structured JSON
86.8%
Text localization
56.2%
OCR (high)
78.7%
$0.039
78.8%
$0.010
75.5%
$0.031
by category
Single value
70.9%
Transcription
94.1%
Structured JSON
90.9%
Text localization
58.6%
Single value
71.7%
Transcription
91.9%
Structured JSON
90.6%
Text localization
59.2%
Single value
69.6%
Transcription
85.4%
Structured JSON
88.1%
Text localization
53.3%
Reasoning (low)
81.2%
±0.3, Mean of 3 runs, range 80.8 to 81.5
$0.0038
80.8%
±2.0, Mean of 3 runs, range 78.8 to 82.8
$0.0022
82.1%
±2.0, Mean of 3 runs, range 80.1 to 84.1
$0.0082
Reasoning (high)
84.5%
±1.0, Mean of 3 runs, range 83.4 to 85.4
$0.023
81.9%
±1.3, Mean of 3 runs, range 80.1 to 82.8
$0.0050
81.0%
±1.7, Mean of 3 runs, range 79.5 to 82.8
$0.018

Model Overviews

Gemini 3.8 Flash is a natively multimodal reasoning model in Google's Gemini 3 series, positioned as the speed and cost oriented Flash tier while targeting long-horizon software engineering, autonomous agents, and enterprise workflows. It accepts text, images, video, audio, and PDF documents in a single request and returns text, with an input limit of 1,048,576 tokens and an output limit of 65,536 tokens. Thinking is configurable at low, medium, and high levels, and the model supports function calling, code execution, structured outputs, context caching, search and Maps grounding, file search, and computer use in preview. Image generation, audio generation, and the Live API are not supported.

Following Gemini API conventions, it can localize objects by emitting bounding boxes as [ymin, xmin, ymax, xmax] integers normalized to a 0 to 1000 range, which supports prompt driven detection and grounding in addition to captioning, document parsing, and visual question answering. The knowledge cutoff is March 2026, though coverage in some domains reflects the January 2025 cutoff shared across the Gemini 3 family.

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.

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.