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Gemini 3.5 Flash vs Gemini 3.8 Flash

Compare Gemini 3.5 Flash and Gemini 3.8 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 3.8 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 72.8%.

Overall, Gemini 3.5 Flash averages 81.8% (#4 of 63) against 79.8% (#7 of 63) for Gemini 3.8 Flash.

Gemini 3.8 Flash is cheaper ($0.0045 vs $0.012 per sample), while Gemini 3.5 Flash is faster (11.5s vs 12.8s per sample).

Gemini 3.5 FlashGemini 3.8 Flash

Gemini 3.5 Flash vs Gemini 3.8 Flash Comparison Table

Evals updated October 13, 2026Pricing updated October 23, 2026

PropertyGemini 3.5 FlashGemini 3.8 Flash
OrganizationGoogleGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMay 2026Sep 2026
Context Window1.0M1.0M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$1.50$0.825
Output $/1M$9.00$4.13
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%
79.8%
Avg cost / sample$0.012$0.0045
Avg speed / sample11.52s12.76s
By task
Object Detection (low)
70.6%
±2.0, Mean of 3 runs, range 68.7 to 72.6
$0.016
68.1%
±0.8, Mean of 3 runs, range 67.3 to 69.0
$0.0045
Object Detection (high)
69.8%
±1.8, Mean of 3 runs, range 67.5 to 71.1
$0.021
74.8%
±1.1, Mean of 3 runs, range 73.4 to 75.6
$0.023
Counting (low)
80.6%
±0.7, Mean of 3 runs, range 79.7 to 81.1
$0.0075
78.8%
±2.7, Mean of 3 runs, range 75.7 to 81.1
$0.0040
Counting (high)
82.4%
±0.0, Mean of 3 runs, range 82.4 to 82.4
$0.017
79.3%
±1.4, Mean of 3 runs, range 78.4 to 81.1
$0.027
Identification (low)
99.0%
±1.6, Mean of 3 runs, range 96.9 to 100.0
$0.0040
97.9%
±1.6, Mean of 3 runs, range 96.9 to 100.0
$0.0016
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
$0.0037
OCR (low)
76.7%
$0.013
72.8%
$0.0049
by category
Single value
71.7%
Transcription
90.0%
Structured JSON
86.8%
Text localization
56.2%
Single value
73.5%
Transcription
91.3%
Structured JSON
86.1%
Text localization
26.4%
OCR (high)
75.5%
$0.031
78.7%
$0.039
by category
Single value
69.6%
Transcription
85.4%
Structured JSON
88.1%
Text localization
53.3%
Single value
70.9%
Transcription
94.1%
Structured JSON
90.9%
Text localization
58.6%
Reasoning (low)
82.1%
±2.0, Mean of 3 runs, range 80.1 to 84.1
$0.0082
81.2%
±0.3, Mean of 3 runs, range 80.8 to 81.5
$0.0038
Reasoning (high)
81.0%
±1.7, Mean of 3 runs, range 79.5 to 82.8
$0.018
84.5%
±1.0, Mean of 3 runs, range 83.4 to 85.4
$0.023

Gemini 3.5 Flash vs Gemini 3.8 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 3.8 Flash

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.

On vision oriented evaluations the model reports 86.2% on CharXiv Reasoning for chart and figure synthesis and 87.8% on LVBench for long video understanding in agentic mode, alongside 90.8% on Terminal-Bench 2.1 and 61.6% on SWE-Bench Pro for coding. 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.

Frequently Asked Questions

On Roboflow's Vision Evals, Gemini 3.5 Flash performed better. It scores higher on all five vision tasks and averages 81.8% (#4 of 63) against 79.8% (#7 of 63) for Gemini 3.8 Flash. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

Yes. On the Vision Evals OCR benchmark at low effort, Gemini 3.5 Flash leads with 76.7% against 72.8%. This is the widest gap between the two models across the benchmark's tasks.

Gemini 3.8 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0045 per sample against $0.012. Gemini 3.5 Flash is priced at $1.50 per 1M input tokens and $9.00 per 1M output; Gemini 3.8 Flash is priced at $0.83 per 1M input tokens and $4.13 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Gemini 3.5 Flash is faster. Across Roboflow's Vision Evals it averaged 11.5s per inference against 12.8s. 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 open prompts and image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.