Gemini 3 Flash vs GPT-5.5

Compare Gemini 3 Flash and GPT-5.5 side-by-side. See how these vision models stack up in Object Detection, Classification, Open Prompt, OCR, and Image Captioning.

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GoogleGemini 3 Flash
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OpenAIGPT-5.5
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Models in this comparison

OpenAI

Gemini 3 Flash vs GPT-5.5: Overview

Gemini 3 Flash

Gemini 3 Flash is a proprietary multimodal large language model developed by Google through Google DeepMind, designed to deliver fast, cost-efficient reasoning across real-time products and developer workflows. Released in December 2025, it is the Flash-tier variant of the Gemini 3 family, balancing low latency with reasoning quality approaching Pro models.

The model supports text, images, audio, and video, with an exceptionally large context window of roughly one million input tokens and outputs up to ~65k tokens. It emphasizes rapid responses for coding, summarization, analysis, and agentic tasks, and exposes configurable “thinking levels” via API to trade speed for deeper reasoning. Today, Gemini 3 Flash positions itself as a high-throughput, production-ready model, serving as the default in the Gemini app and Google Search’s AI Mode, optimized for scalable, interactive AI applications.

GPT-5.5

GPT-5.5 is a multimodal large language model released by OpenAI on April 23, 2026, engineered for autonomous, multi-step knowledge work and agentic workflows. It accepts text, images, and code as input, featuring enhanced spatial reasoning and visual grounding to support its computer use capabilities for operating software and navigating UI elements. Built to execute complex workflows end-to-end, the model interprets loosely defined tasks, selects appropriate tools, and performs self-verification with minimal user intervention. It is available in a standard version, a Thinking mode for extended reasoning budgets, and a Pro variant that uses parallel test-time compute for maximum precision on complex tasks.

Co-optimized with NVIDIA for GB200 NVL72 infrastructure, GPT-5.5 delivers per-token latency comparable to its predecessor GPT-5.4 while maintaining a 1-million-token context window. Despite increased capability, the model achieves greater token efficiency in coding and data analysis workflows, often completing tasks with fewer total tokens than previous versions. OpenAI reports a 60% reduction in hallucination rate compared to GPT-5.4, improving reliability for accuracy-sensitive applications. API access is available via the Responses and Chat Completions endpoints at $5 per million input tokens and $30 per million output tokens, double the unit price of GPT-5.4.

Gemini 3 Flash vs GPT-5.5 Comparison Table

PropertyGemini 3 FlashGPT-5.5
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateDec 2025Apr 2026
Context Window1.0M1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.500$5.00
Output $/1M$3.00$30.00
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
74.63%
77.61%
Avg Response Time9.85s30.12s
Median input tokensincl. image tokens1.1K1.4K
Median output tokens290138
Est. cost / taskon this benchmark$0.0014$0.011
Defect Detection
73.3%(11/15)
86.7%(13/15)
Document Understanding
88.9%(8/9)
88.9%(8/9)
Object Counting
30%(3/10)
30%(3/10)
Object Understanding
85.7%(12/14)
92.9%(13/14)
Spatial Understanding
84.2%(16/19)
78.9%(15/19)
OCR
Overall Score
93.01%
81.22%
Avg Response Time12.40s5.16s
Median input tokensincl. image tokens1.1K105
Median output tokens16083
Est. cost / taskon this benchmark$0.0010$0.0030
Focused Scene OCR
94.9%(94/99)
77.8%(77/99)
Handwritten Math
100%(10/10)
40%(4/10)
License Plate Recognition
100%(30/30)
93.3%(28/30)
Text Recognition
86.7%(26/30)
83.3%(25/30)
VQA & Extraction
88.3%(53/60)
86.7%(52/60)

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