Gemini 3.5 Flash vs GPT-5.1

Compare Gemini 3.5 Flash and GPT-5.1 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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OpenAIGPT-5.1
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OpenAI

Gemini 3.5 Flash vs GPT-5.1: 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.

GPT-5.1

GPT-5.1 is an OpenAI frontier-grade model in the GPT-5 series, offering stronger general-purpose reasoning, clearer long-form responses, and improved instruction following. It introduces two variants—Instant and Thinking—that dynamically adjust computational depth. Instant focuses on fast, conversational replies, while Thinking provides deeper, more thorough reasoning for complex tasks. In ChatGPT, GPT-5.1 also powers an Auto mode that switches between these variants automatically based on task difficulty.

The model supports significantly expanded context windows: up to 16K/32K/128K tokens for Instant (depending on tier) and up to 196K tokens for Thinking on paid tiers. GPT-5.1 is also compatible with ChatGPT tools such as web search, file and image analysis, and multi-step workflows.

GPT-5.1 includes enhanced tone and style controls, allowing responses to be tailored using presets like Friendly, Professional, or Efficient, along with fine-grained adjustments for warmth, brevity, and emoji usage. Designed for broad applications in research assistance, coding, analysis, and conversational agents, GPT-5.1 serves as OpenAI’s primary full-capability successor to GPT-5 across ChatGPT and API integrations.

Gemini 3.5 Flash vs GPT-5.1 Comparison Table

PropertyGemini 3.5 FlashGPT-5.1
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMay 2026Nov 2025
Context Window1.0M196K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$1.50$1.25
Output $/1M$9.00$10.00
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Visual Question AnsweringDemoDemo
Chart Question Answering
Document Question Answering
Multi-Label Classification
Vision Language
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
79.1%
Avg Response Time6.71s
Median input tokensincl. image tokens1.1K
Median output tokens294
Est. cost / taskon this benchmark$0.0043
Defect Detection
80%(12/15)
Document Understanding
77.8%(7/9)
Object Counting
60%(6/10)
Object Understanding
92.9%(13/14)
Spatial Understanding
78.9%(15/19)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology